quasi experimental research data analysis

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• Quasi-Experimental Design | Definition, Types & Examples

Quasi-Experimental Design | Definition, Types & Examples

Published on July 31, 2020 by Lauren Thomas . Revised on January 22, 2024.

Like a true experiment , a quasi-experimental design aims to establish a cause-and-effect relationship between an independent and dependent variable .

However, unlike a true experiment, a quasi-experiment does not rely on random assignment . Instead, subjects are assigned to groups based on non-random criteria.

Quasi-experimental design is a useful tool in situations where true experiments cannot be used for ethical or practical reasons.

Table of contents

Differences between quasi-experiments and true experiments, types of quasi-experimental designs, when to use quasi-experimental design, advantages and disadvantages, other interesting articles, frequently asked questions about quasi-experimental designs.

There are several common differences between true and quasi-experimental designs.

Example of a true experiment vs a quasi-experiment

However, for ethical reasons, the directors of the mental health clinic may not give you permission to randomly assign their patients to treatments. In this case, you cannot run a true experiment.

Instead, you can use a quasi-experimental design.

You can use these pre-existing groups to study the symptom progression of the patients treated with the new therapy versus those receiving the standard course of treatment.

Prevent plagiarism. Run a free check.

Many types of quasi-experimental designs exist. Here we explain three of the most common types: nonequivalent groups design, regression discontinuity, and natural experiments.

Nonequivalent groups design

In nonequivalent group design, the researcher chooses existing groups that appear similar, but where only one of the groups experiences the treatment.

In a true experiment with random assignment , the control and treatment groups are considered equivalent in every way other than the treatment. But in a quasi-experiment where the groups are not random, they may differ in other ways—they are nonequivalent groups .

When using this kind of design, researchers try to account for any confounding variables by controlling for them in their analysis or by choosing groups that are as similar as possible.

This is the most common type of quasi-experimental design.

Regression discontinuity

Many potential treatments that researchers wish to study are designed around an essentially arbitrary cutoff, where those above the threshold receive the treatment and those below it do not.

Near this threshold, the differences between the two groups are often so minimal as to be nearly nonexistent. Therefore, researchers can use individuals just below the threshold as a control group and those just above as a treatment group.

However, since the exact cutoff score is arbitrary, the students near the threshold—those who just barely pass the exam and those who fail by a very small margin—tend to be very similar, with the small differences in their scores mostly due to random chance. You can therefore conclude that any outcome differences must come from the school they attended.

Natural experiments

In both laboratory and field experiments, researchers normally control which group the subjects are assigned to. In a natural experiment, an external event or situation (“nature”) results in the random or random-like assignment of subjects to the treatment group.

Even though some use random assignments, natural experiments are not considered to be true experiments because they are observational in nature.

Although the researchers have no control over the independent variable , they can exploit this event after the fact to study the effect of the treatment.

However, as they could not afford to cover everyone who they deemed eligible for the program, they instead allocated spots in the program based on a random lottery.

Although true experiments have higher internal validity , you might choose to use a quasi-experimental design for ethical or practical reasons.

Sometimes it would be unethical to provide or withhold a treatment on a random basis, so a true experiment is not feasible. In this case, a quasi-experiment can allow you to study the same causal relationship without the ethical issues.

The Oregon Health Study is a good example. It would be unethical to randomly provide some people with health insurance but purposely prevent others from receiving it solely for the purposes of research.

However, since the Oregon government faced financial constraints and decided to provide health insurance via lottery, studying this event after the fact is a much more ethical approach to studying the same problem.

True experimental design may be infeasible to implement or simply too expensive, particularly for researchers without access to large funding streams.

At other times, too much work is involved in recruiting and properly designing an experimental intervention for an adequate number of subjects to justify a true experiment.

In either case, quasi-experimental designs allow you to study the question by taking advantage of data that has previously been paid for or collected by others (often the government).

Quasi-experimental designs have various pros and cons compared to other types of studies.

• Higher external validity than most true experiments, because they often involve real-world interventions instead of artificial laboratory settings.
• Higher internal validity than other non-experimental types of research, because they allow you to better control for confounding variables than other types of studies do.
• Lower internal validity than true experiments—without randomization, it can be difficult to verify that all confounding variables have been accounted for.
• The use of retrospective data that has already been collected for other purposes can be inaccurate, incomplete or difficult to access.

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Thomas, L. (2024, January 22). Quasi-Experimental Design | Definition, Types & Examples. Scribbr. Retrieved August 12, 2024, from https://www.scribbr.com/methodology/quasi-experimental-design/

Is this article helpful?

Lauren Thomas

Other students also liked, guide to experimental design | overview, steps, & examples, random assignment in experiments | introduction & examples, control variables | what are they & why do they matter, get unlimited documents corrected.

✔ Free APA citation check included ✔ Unlimited document corrections ✔ Specialized in correcting academic texts

• Search Menu
• Sign in through your institution
• Advance articles
• Editor's Choice
• Supplement Archive
• Cover Archive
• IDSA Guidelines
• IDSA Journals
• The Journal of Infectious Diseases
• Open Forum Infectious Diseases
• Photo Quizzes
• State-of-the-Art Reviews
• Voices of ID
• Author Guidelines
• Open Access
• Why Publish
• IDSA Journals Calls for Papers
• Advertising and Corporate Services
• Advertising
• Journals Career Network
• Reprints and ePrints
• Sponsored Supplements
• Branded Books
• About Clinical Infectious Diseases
• About the Infectious Diseases Society of America
• About the HIV Medicine Association
• IDSA COI Policy
• Editorial Board
• Self-Archiving Policy
• For Reviewers
• For Press Offices
• Journals on Oxford Academic
• Books on Oxford Academic

Article Contents

Two-group tests, regression analysis, time-series analysis, adding a control group, acknowledgments.

• < Previous

Statistical Analysis and Application of Quasi Experiments to Antimicrobial Resistance Intervention Studies

• Article contents
• Figures & tables
• Supplementary Data

George M. Eliopoulos, Michelle Shardell, Anthony D. Harris, Samer S. El-Kamary, Jon P. Furuno, Ram R. Miller, Eli N. Perencevich, Statistical Analysis and Application of Quasi Experiments to Antimicrobial Resistance Intervention Studies, Clinical Infectious Diseases , Volume 45, Issue 7, 1 October 2007, Pages 901–907, https://doi.org/10.1086/521255

• Permissions Icon Permissions

Quasi-experimental study designs are frequently used to assess interventions that aim to limit the emergence of antimicrobial-resistant pathogens. However, previous studies using these designs have often used suboptimal statistical methods, which may result in researchers making spurious conclusions. Methods used to analyze quasi-experimental data include 2-group tests, regression analysis, and time-series analysis, and they all have specific assumptions, data requirements, strengths, and limitations. An example of a hospital-based intervention to reduce methicillin-resistant Staphylococcus aureus infection rates and reduce overall length of stay is used to explore these methods.

Choosing the appropriate study design is critical when performing antimicrobial resistance intervention studies. When randomized studies in single hospitals or multihospital cluster-randomized trials are infeasible, investigators often choose before-and-after quasi-experimental designs [ 1 , 2 ]. Quasi-experimental studies can assess interventions applied at the hospital or unit level (e.g., hygiene education program in the medical intensive care unit [MICU] [ 3 ]) or individual level (e.g., methicillin-resistant Staphylococcus aureus [MRSA] decolonization programs [ 4 ]), in which data are collected at equally spaced time intervals (e.g., monthly) before and after the intervention.

Nonrandomization and the resulting data structure of quasi experiments impart several methodological challenges for analysis. First, common statistical methods, including 2-group Student's t tests and linear regression, were developed to analyze independent, individual-level observations, whereas quasi-experimental data are typically correlated unit-level observations; for example, MRSA counts (defined as the number of MRSA infections at multiple time intervals) collected 1 month apart are likely more similar than MRSA counts collected 2 months apart. Second, nonrandom assignment of the intervention often necessitates analytical control for potential confounders.

Unfortunately, application of statistical techniques to quasi experiments is rarely described in introductory biostatistics texts and courses. We aim to provide a resource for bridging the gap between clinician researchers and biostatisticians by introducing clinicians to statistical analysis of quasi experiments while guiding biostatisticians regarding design-related challenges of intervention studies for controlling antimicrobial resistance, thereby improving conduct and reporting of these studies, as recently outlined [ 5 , 6 ]. Strength of evidence from quasi-experimental data depends on the study design [ 1 , 2 , 7 ]. Studies with a concurrent nonequivalent control group provide stronger evidence about effectiveness of an intervention than do studies without a control group. Also, studies with several preintervention observations provide stronger evidence than do studies with few or no preintervention observations. As discussed below, the internal validity of quasi experiments is partially related to study design elements that affect researchers' ability to control for correlation, confounding, and time trends. Thus, before a study is initiated, hypotheses should be clearly stated, and design and analysis plans should be carefully developed.

We discuss several statistical techniques using the following example (motivated by a study by Pittet et al. [ 3 ]). After several months of abnormally high MRSA infection rates in the MICU, a hospital epidemiologist launches an education-based intervention to increase compliance with hand-disinfection procedures. The epidemiologist aims to compare rates of positivity for MRSA in clinical cultures before and after implementing the intervention. A secondary aim is to assess whether the intervention decreases overall length of stay (LOS) in the MICU. For both aims, data from 36 months before the intervention (2003–2005) are compared with data from 12 months after the intervention (2006). For ease of explanation, we first describe statistical methods for this example without a control group. We then discuss adaptations of methods for studies with a nonequivalent control group.

We discuss 2-group tests (e.g., Student's t test and χ 2 test), regression analysis (including segmented models), and time-series analysis in application to quasi-experimental studies of interventions to control antibiotic-resistant bacterial pathogens. We use simulated data for illustration and review data requirements, software, strengths, and limitations for each statistical method (tables 1 and 2 ). Persons seeking additional resources on statistics or quasi experiments are urged to consult a statistics primer [ 8 ] and literature regarding quasi-experimental studies, respectively [ 1 , 2 , 7 ].

Statistical method and software commands by outcome type.

Characteristics of each statistical method.

Two-group (i.e., bivariate) tests make crude comparisons (i.e., unadjusted for confounders) of MRSA infection rates and mean LOS in pre- and postintervention periods. We specifically discuss Student's t tests for continuous outcomes (e.g., LOS) and 2-rate χ 2 tests for count outcomes (e.g., number of MRSA infections).

Continuous outcomes. For continuous outcomes, 2 mean values are compared using Student's t test. In our example, we test the equality of the mean LOS before and after the education-based hand disinfection intervention. When data from several preintervention and postintervention periods are collected, as in interrupted time-series study designs [ 1 , 2 , 7 ], data from multiple periods before and after implementation of the intervention are pooled to produce 2 grand mean values. For example, 2300 patients per year (6900 total) with a mean LOS of 3.0 days during 2003–2005 (preintervention period) and 2800 patients with a mean LOS of 2.5 days in 2006 (postintervention period) can be compared. However, Student's t tests are sensitive to outlying values. If some patients have atypically long LOS, the median value is the preferred measurement of central tendency. Transformation (e.g., natural logarithm) of individual patients' LOS or a nonparametric test to compare median values (e.g., Wilcoxon rank-sum test) can be used.

Count outcomes. Crude comparisons can be made for count outcomes (e.g., number of MRSA infections) by performing a 2-rate χ 2 test. In our example, because the number of hospital admissions varies over time, comparing numbers of pre- and postintervention MRSA infections may produce invalid results. Summarizing data as a proportion, with the number of MRSA infections divided by the number of hospital admissions (e.g., 150 infections/6900 hospital admissions [2.2%], compared with 40 infections/2800 hospital admissions [1.4%]; P = .009), is appropriate if all patients are observed for the same duration of follow-up, when the proportion is interpreted as risk of infection for that particular follow-up period (e.g., 3-day risk of MRSA infection). However, observation of patients in infection-control studies is typically limited to hospital stays that vary in duration. The 2-rate χ 2 test accommodates this difference by comparing rates (number of infections per unit of person-time) between pre- and postintervention periods [ 6 ]. Given 150 and 40 infections before and after the intervention, respectively, if 6700 preintervention person-days per year (20,100 total) and 6600 postintervention person-days are observed, then the rates are 7.5 and 6.1 infections per 1000 person-days before and after the intervention, respectively ( P = .21). Thus, correcting for person-time using rates may produce conclusions different from those using proportions.

The 2-rate χ 2 test assumes that infection counts follow a Poisson distribution [ 9 , 10–11 ]. The Poisson assumption implies that the mean infection count per person-time equals the variance in the infection count for that person-time. If this assumption is violated, then incorrect SE estimates are calculated, resulting in incorrect confidence intervals and P values.

In interrupted time-series study designs, rates are collected at several periods, allowing the variance of infection counts per person per unit of time to be empirically estimated and compared with the mean value. If the “mean equals variance” assumption is not valid, a test using “robust” SEs on the basis of empirically estimated variances is recommended [ 12 , 13 ]. Consider 12 months of data on MRSA infection rates with a mean rate of 2.8 cases per 1000 person-days and a variance of 2.2. Thus, the Poisson assumption appears valid. In contrast, consider MRSA infection rates with a mean rate of 4.4 cases per 1000 person-days and a variance of 6.6. This latter example is typical such that satisfying the Poisson assumption is rare in practical applications. Therefore, researchers should perform both 2-rate χ 2 tests (with and without robust SEs) to evaluate whether confidence intervals and P values vary across assumptions. If conclusions differ between the 2 methods, test results using the more conservative robust SEs should be reported.

Strengths and limitations. Strengths of 2-group tests include simplicity, interpretability of results, and minimal data requirements (2 observation periods) ( table 2 ). These tests can accommodate >2 groups (e.g., before intervention, after intervention, and after intervention plus change in antimicrobial prescribing), using analysis of variance for continuous outcomes and χ 2 tests for count outcomes.

Two-group tests are limited by several assumptions. One assumption, independence between patients admitted to the hospital in the same period, is implausible because infectious organisms are transmissible. Independence of observations between periods is also implausible, because patients admitted to the hospital in different months may be exposed to constant antibiotic prescribing patterns. Also, without multiple levels of stratification, the ability to adjust for potential confounders (e.g., differences in severity of illness) is limited. Last, 2-group tests can detect changes in outcome levels but not changes in trends (e.g., monthly increases or decreases in the MRSA infection rate). If we use the 2-rate χ 2 test with data in figure 1 , the MRSA infection rates for 36 months before and 12 months after an intervention are 6.8 and 6.6 cases per 1000 person-days, respectively ( P = .87). However, figure 1 shows rates increasing by 0.25 cases per 1000 person-days per month until implementation of the intervention, then decreasing by 0.75 cases per 1000 person-days per month. By pooling counts into single pre- and postintervention rates, the 2-rate χ 2 test cannot detect this change in slope or trend, incorrectly finding no evidence of effectiveness of the intervention. To detect changes in slopes, a different statistical method, such as segmented regression, is needed.

Changes in rate of infection with methicillin-resistant Staphylococcus aureus (MRSA) over time before and after an intervention implemented at month 36, showing a change in slope that would not be detected by 2-group tests. Preintervention and postintervention rates are 6.8 and 6.6 infections per 1000 person-days, respectively ( P = .87, by 2-rate χ 2 test). Preintervention and postintervention slopes are 0.25 and -0.75 infections per 1000 person-days per month, respectively.

Regression analysis quantifies the relationship between an outcome (e.g., LOS or MRSA infection) and an intervention, allowing for statistical control of known confounders. Linear regression is used for continuous normally distributed outcomes (e.g., average monthly LOS or log-transformed individual LOS). Other outcome types, including MRSA counts, require analysis using generalized linear models [ 14 ]. In our example, MRSA infections are considered as MRSA counts per time period with an assumed Poisson distribution; thus, the appropriate method is Poisson regression.

Unlike in statistical literature, in clinical literature, “segmented regression” means regression analysis in which changes in mean outcome levels and trends before and after an intervention are estimated [ 15 ]. If changes in slopes are not estimated (e.g., nonsegmented regression model is fit), then estimates of the slopes may be biased, and changes in time trends attributable to the intervention would be undetected. Segmented regression models can be fit to estimate changes in levels and trends. In our example below, we estimate pre- and postintervention changes in LOS and MRSA levels and trends.

Continuous outcomes. Although individual LOS is usually skewed, mean monthly LOS is approximately normally distributed for large sample sizes (i.e., >30 patients per month). If LOS increases over time secondary to a steady increase in MRSA infection rates, regression analysis can model this pattern and estimate the effect of an intervention controlling for potential confounders (e.g., age and reasons for hospitalization). Given intervention status and potential confounders, the outcome variable (in this case, LOS) must satisfy the assumption of having constant variance.

Using the same data, we estimate changes in mean LOS, controlling for trends, using 2 different models ( figure 2 ). Figure 2A shows the results of nonsegmented linear regression, which cannot assess a change in time trend (i.e., slope). Figure 2B shows the results of segmented linear regression, which allows the slopes to differ before and after the intervention. Compared with the model in figure 2 A , the estimated time trend using segmented linear regression in figure 2 B is flatter after the intervention. Forcing equal slopes before and after the intervention when they are unequal can lead to spurious conclusions about an intervention's effectiveness.

Interrupted time-series data regarding length of hospital stay (LOS) simulated from a segmented linear regression model with a change in slope (before vs. after the intervention), fit with a nonsegmented linear regression model that cannot estimate a change in slope (A) and a segmented linear regression model that can estimate a change in slope (B). The intervention was implemented at month 36.

Count outcomes. Poisson regression is preferred over linear regression for estimating the association between the intervention and monthly MRSA infection rates, controlling for time trend, because counts are not normally distributed ( figure 3 ). Differences estimated from this model are summarized as incident rate ratios of MRSA infections.

Figure 3. Interrupted time-series methicillin-resistant Staphylococcus aureus (MRSA) infection data simulated from a segmented Poisson regression model with a change in slope (before vs. after the intervention), fit with a nonsegmented Poisson regression model that cannot estimate a change in slope (A) and a segmented Poisson regression model that can estimate a change in slope (B). The intervention was implemented at month 36.

Using the same data, we estimate changes in MRSA infection rates, controlling for trends, using 2 models ( figure 3 ). Figure 3A shows the results of nonsegmented Poisson regression, which precludes estimation of changes in time trend (i.e., slope), whereas figure 3 B shows the results of segmented Poisson regression, which allows different slopes before and after the intervention.

SE estimates of Poisson regression models are constrained by the “mean equals variance” assumption. This assumption is relaxed by fitting an overdispersed Poisson regression model [ 14 , 16 ]. Allowing overdispersion can affect SE estimates if the Poisson assumption is false without changing estimated regression parameters, producing more valid inferences. Poisson regression and overdispersed Poisson regression result in equal incident rate ratio estimates but different confidence intervals.

Strengths and limitations. Regression allows estimation of associations between the intervention and outcome while controlling for potential confounders, which is particularly important in nonrandomized quasi-experimental studies ( table 2 ). Segmented regression models estimate changes in mean outcome levels (i.e., intercepts) and trends (i.e., slopes), unlike standard regression models. However, some limitations previously discussed with 2-group tests remain. Specifically, independence between individuals and time periods is assumed. Additionally, regression analysis, in contrast to 2-group tests, requires data from multiple pre- and postintervention time intervals to estimate the slope. General guidelines suggest the use of at least 10 observations per model parameter to avoid overfitting [ 17 ]. The models in figures 2B and 3B contained 5 parameters; thus, they should be used only for studies with at least 50 total observations (in our example, months). For intervention studies, data from at least 10 observations before and after the intervention should be used. However, using at least 24 observations (in our example, 12 months before and after the intervention) would capture potential seasonal changes. Data from shorter intervals can be used (e.g., biweekly); however, choice of time interval is a compromise between maximizing the number of observations and maintaining sufficient data within each interval to provide interpretable summary measures [ 15 , 18 ]. In SAS, the command PROC GENMOD can estimate Poisson and linear regression models ( table 1 ) [ 19 ].

Time-series analysis consists of advanced statistical techniques that require understanding of regression and correlation. Whereas “interrupted time-series design” refers to studies consisting of equally spaced pre- and postintervention observations, “time-series analysis” refers to statistical methods for analyzing time-series design data. Two-group tests and regression analysis assume that monthly LOS and MRSA infection rates are independent over time. In contrast, time-series analysis estimates regression models while relaxing the independence assumption by estimating the autocorrelation between observations collected at different times (e.g., MRSA infection counts among MICU patients across different periods). To estimate autocorrelation, a correlation model is specified along with the regression model, resulting in more accurate SE estimates and improved statistical inference.

Continuous outcomes. Time-series analysis accommodates the previously discussed regression models; however, the challenge is how to correctly model correlation. In linear regression, monthly LOS measurements are assumed to be independent. However, autocorrelation may take one of several forms. For example, if correlation between 2 observations gradually decreases as time between them increases (e.g., correlation between months 1 and 2 is 0.5, correlation between months 1 and 3 is 0.25, and correlation between months 1 and 4 is 0.12), autocorrelation is likely autoregressive. However, if autocorrelation between 2 observations is initially strong but abruptly decreases to ∼0 (e.g., correlation between months 1 and 2 is 0.5 and correlation between months 1 and 3 is 0.05), a moving-average model is more appropriate. Occasionally, autocorrelation is strong for observations close in time and then sharply decreases to a nonzero level after some time threshold. In this case, autoregressive or moving-average models would be inadequate, and autoregressive moving-average (ARMA) models should be used. When correlation between observations does not decrease with duration of time, autoregressive, integrated, moving-average (ARIMA) models may be appropriate. In SAS, PROC AUTOREG estimates autoregressive models, and PROC ARIMA estimates autoregressive, moving-average, ARMA, and ARIMA models.

Count outcomes. Although most time-series software assume that outcomes are normally distributed, methods for Poisson counts are available [ 20 , 21 , 22–23 ]. One approach is to transform counts into monthly rates and use time-series methods for normal data (rates are approximately normally distributed if they are based on large numbers). In addition, Autoregressive [ 22 , 23 ], moving-average [ 21 ], and ARMA [ 20 ] models have been extended for generalized linear models (including Poisson models), called generalized ARMA models. The “garma” command in the R software library VGAM estimates generalized ARMA models [ 24 ].

Strengths and limitations. Time-series methods estimate dependence (i.e., correlation) between observations over time, lessening a common threat to valid inferences. They also accommodate segmented models. Thus, time-series methods generalize regression by relaxing the assumption of independent observations. However, the large data requirements often preclude its use. A general guideline is having ∼50 time points (e.g., 3 years of monthly preintervention data and 1 year of monthly postintervention data) to estimate complex correlation structures [ 25 ]. If fewer observations are available, only simple correlation structures can be reliably estimated [ 15 ].

Another limitation of time-series analysis is difficulty in building and interpreting correlation models. Several technical resources are available to guide analysts [ 26 , 27–28 ]. Review articles [ 25 , 29 , 30 ] and biomedical examples are also available [ 18 , 31 , 32 ]. Bootstrapping circumvents the problem of specifying and estimating an autocorrelation model. Bootstrap SEs can be calculated by estimating regression parameters assuming independence (i.e., linear or Poisson regression). Resulting SEs account for autocorrelation by sampling the data multiple (e.g., 1000 times) with replacement and estimating the parameters with each sample [ 33 ]. Thus, the bootstrap with regression is an alternative to time-series analysis when too few time intervals are observed.

Each method can easily accommodate comparison with a nonequivalent control group, a preferred epidemiological quasi-experimental design, because regression to the mean and maturation effects are common threats in these studies [ 1 , 7 ]. In our example, the intervention could be implemented in the MICU, and the nonequivalent control group could be the surgical intensive care unit. A 2-group t test would then compare changes in the mean LOS in the MICU and surgical intensive care unit (mean LOS after the intervention minus mean LOS before the intervention). Regression analysis (e.g., linear and Poisson) controlling for confounding variables can be performed by fitting separate trends for the MICU and surgical intensive care unit and comparing differences in changes in levels (i.e., intercepts) and trends (i.e., slopes) between the 2 units ( figure 4 ). In our example, the MRSA infection rate in the MICU decreases by 0.8 cases per 1000 person-days immediately on implementation of the intervention, suggesting a large impact of the intervention. However, the MRSA infection rate in the surgical intensive care unit decreases by 0.6 cases per 1000 person-days, suggesting that the decrease in the MRSA infection rate is partially attributable to nonintervention factors, which could not have been identified without a control group. Hence, including a control group is recommended to identify the true impact of an intervention.

Segmented Poisson regression analysis of interrupted time-series methicillin-resistant Staphylococcus aureus (MRSA) infection data, comparing infection rates in the medical intensive care unit (MICU; intervention group) and surgical intensive care unit (SICU; control group) before and after the intervention (implemented at month 36). The reduction of 0.6 infections per 1000 person-days in the SICU suggests that the reduction of 0.8 infections per 1000 person-days in the MICU was not solely due to the intervention.

In summary, 2-group tests, regression analysis, and time-series analysis can accommodate interrupted time-series quasi-experimental data. However, statistical validity depends on using appropriate methods for the study question, meeting data requirements, and verifying modeling assumptions. This last step requires premodeling exploratory data analysis and postmodeling diagnostics not addressed here [ 14 , 17 , 26 , 27 ].

Obtaining high-quality results depends on performing a well-designed study, because statistics cannot correct for a poor initial design [ 1 , 7 , 34 ], nor can they compensate for poor reporting of methods [ 5 , 6 ]. Results from analyses can only provide valid inference on the level of intervention. We provide guidelines of minimal data requirements for using each statistical method ( table 2 ). However, larger sample sizes may be needed to obtain a desired precision for estimating measures of association (e.g., mean difference or rate ratio) or power for statistical tests. A simulation study can determine required sample size using model-generated data analyzed with an appropriate method [ 35 ]. Investigators are encouraged to report sample size calculations in addition to statistical analysis methods [ 5 , 6 ]. Analyzing quasi-experimental data is challenging; therefore, we recommend collaboration between investigators, epidemiologists, and statisticians.

Financial support. National Institute of Health (grants R37 AG09901, 1 R01 AI6085901A1, and P30 AG028747-01 to M.S.; P60 AG12583 to R.R.M.; and institutional grant 1K12RR023250-01 to J.P.F.), Centers for Disease Control and Prevention (grant 1 R01 CI000369-01 to A.D.H. and E.N.P.), and Department of Veterans Affairs Health Services Research and Development Service (grants IIR 04-123-2 and Level 2 Advanced Career Development Award to E.N.P.).

Potential conflicts of interest. All authors: no conflicts.

Google Scholar

Google Preview

• drug resistance, microbial
• length of stay
• pathogenic organism
• antimicrobials
• methicillin-resistant staphylococcus aureus infections

Email alerts

More on this topic, related articles in pubmed, citing articles via, looking for your next opportunity.

• Recommend to your Library

Affiliations

• Online ISSN 1537-6591
• Print ISSN 1058-4838
• Copyright © 2024 Infectious Diseases Society of America
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

• Skip to secondary menu
• Skip to main content
• Skip to primary sidebar

Statistics By Jim

Making statistics intuitive

Quasi Experimental Design Overview & Examples

By Jim Frost Leave a Comment

What is a Quasi Experimental Design?

A quasi experimental design is a method for identifying causal relationships that does not randomly assign participants to the experimental groups. Instead, researchers use a non-random process. For example, they might use an eligibility cutoff score or preexisting groups to determine who receives the treatment.

Quasi-experimental research is a design that closely resembles experimental research but is different. The term “quasi” means “resembling,” so you can think of it as a cousin to actual experiments. In these studies, researchers can manipulate an independent variable — that is, they change one factor to see what effect it has. However, unlike true experimental research, participants are not randomly assigned to different groups.

Learn more about Experimental Designs: Definition & Types .

When to Use Quasi-Experimental Design

Researchers typically use a quasi-experimental design because they can’t randomize due to practical or ethical concerns. For example:

• Practical Constraints : A school interested in testing a new teaching method can only implement it in preexisting classes and cannot randomly assign students.
• Ethical Concerns : A medical study might not be able to randomly assign participants to a treatment group for an experimental medication when they are already taking a proven drug.

Quasi-experimental designs also come in handy when researchers want to study the effects of naturally occurring events, like policy changes or environmental shifts, where they can’t control who is exposed to the treatment.

Quasi-experimental designs occupy a unique position in the spectrum of research methodologies, sitting between observational studies and true experiments. This middle ground offers a blend of both worlds, addressing some limitations of purely observational studies while navigating the constraints often accompanying true experiments.

A significant advantage of quasi-experimental research over purely observational studies and correlational research is that it addresses the issue of directionality, determining which variable is the cause and which is the effect. In quasi-experiments, an intervention typically occurs during the investigation, and the researchers record outcomes before and after it, increasing the confidence that it causes the observed changes.

However, it’s crucial to recognize its limitations as well. Controlling confounding variables is a larger concern for a quasi-experimental design than a true experiment because it lacks random assignment.

In sum, quasi-experimental designs offer a valuable research approach when random assignment is not feasible, providing a more structured and controlled framework than observational studies while acknowledging and attempting to address potential confounders.

Types of Quasi-Experimental Designs and Examples

Quasi-experimental studies use various methods, depending on the scenario.

Natural Experiments

This design uses naturally occurring events or changes to create the treatment and control groups. Researchers compare outcomes between those whom the event affected and those it did not affect. Analysts use statistical controls to account for confounders that the researchers must also measure.

Natural experiments are related to observational studies, but they allow for a clearer causality inference because the external event or policy change provides both a form of quasi-random group assignment and a definite start date for the intervention.

For example, in a natural experiment utilizing a quasi-experimental design, researchers study the impact of a significant economic policy change on small business growth. The policy is implemented in one state but not in neighboring states. This scenario creates an unplanned experimental setup, where the state with the new policy serves as the treatment group, and the neighboring states act as the control group.

Researchers are primarily interested in small business growth rates but need to record various confounders that can impact growth rates. Hence, they record state economic indicators, investment levels, and employment figures. By recording these metrics across the states, they can include them in the model as covariates and control them statistically. This method allows researchers to estimate differences in small business growth due to the policy itself, separate from the various confounders.

Nonequivalent Groups Design

This method involves matching existing groups that are similar but not identical. Researchers attempt to find groups that are as equivalent as possible, particularly for factors likely to affect the outcome.

For instance, researchers use a nonequivalent groups quasi-experimental design to evaluate the effectiveness of a new teaching method in improving students’ mathematics performance. A school district considering the teaching method is planning the study. Students are already divided into schools, preventing random assignment.

The researchers matched two schools with similar demographics, baseline academic performance, and resources. The school using the traditional methodology is the control, while the other uses the new approach. Researchers are evaluating differences in educational outcomes between the two methods.

They perform a pretest to identify differences between the schools that might affect the outcome and include them as covariates to control for confounding. They also record outcomes before and after the intervention to have a larger context for the changes they observe.

Regression Discontinuity

This process assigns subjects to a treatment or control group based on a predetermined cutoff point (e.g., a test score). The analysis primarily focuses on participants near the cutoff point, as they are likely similar except for the treatment received. By comparing participants just above and below the cutoff, the design controls for confounders that vary smoothly around the cutoff.

For example, in a regression discontinuity quasi-experimental design focusing on a new medical treatment for depression, researchers use depression scores as the cutoff point. Individuals with depression scores just above a certain threshold are assigned to receive the latest treatment, while those just below the threshold do not receive it. This method creates two closely matched groups: one that barely qualifies for treatment and one that barely misses out.

By comparing the mental health outcomes of these two groups over time, researchers can assess the effectiveness of the new treatment. The assumption is that the only significant difference between the groups is whether they received the treatment, thereby isolating its impact on depression outcomes.

Controlling Confounders in a Quasi-Experimental Design

Accounting for confounding variables is a challenging but essential task for a quasi-experimental design.

In a true experiment, the random assignment process equalizes confounders across the groups to nullify their overall effect. It’s the gold standard because it works on all confounders, known and unknown.

Unfortunately, the lack of random assignment can allow differences between the groups to exist before the intervention. These confounding factors might ultimately explain the results rather than the intervention.

Consequently, researchers must use other methods to equalize the groups roughly using matching and cutoff values or statistically adjust for preexisting differences they measure to reduce the impact of confounders.

A key strength of quasi-experiments is their frequent use of “pre-post testing.” This approach involves conducting initial tests before collecting data to check for preexisting differences between groups that could impact the study’s outcome. By identifying these variables early on and including them as covariates, researchers can more effectively control potential confounders in their statistical analysis.

Additionally, researchers frequently track outcomes before and after the intervention to better understand the context for changes they observe.

Statisticians consider these methods to be less effective than randomization. Hence, quasi-experiments fall somewhere in the middle when it comes to internal validity , or how well the study can identify causal relationships versus mere correlation . They’re more conclusive than correlational studies but not as solid as true experiments.

In conclusion, quasi-experimental designs offer researchers a versatile and practical approach when random assignment is not feasible. This methodology bridges the gap between controlled experiments and observational studies, providing a valuable tool for investigating cause-and-effect relationships in real-world settings. Researchers can address ethical and logistical constraints by understanding and leveraging the different types of quasi-experimental designs while still obtaining insightful and meaningful results.

Cook, T. D., & Campbell, D. T. (1979).  Quasi-experimentation: Design & analysis issues in field settings . Boston, MA: Houghton Mifflin

Share this:

Reader Interactions

Comments and questions cancel reply.

• Privacy Policy

Home » Quasi-Experimental Research Design – Types, Methods

Quasi-Experimental Research Design – Types, Methods

Table of Contents

Quasi-Experimental Design

Quasi-experimental design is a research method that seeks to evaluate the causal relationships between variables, but without the full control over the independent variable(s) that is available in a true experimental design.

In a quasi-experimental design, the researcher uses an existing group of participants that is not randomly assigned to the experimental and control groups. Instead, the groups are selected based on pre-existing characteristics or conditions, such as age, gender, or the presence of a certain medical condition.

Types of Quasi-Experimental Design

There are several types of quasi-experimental designs that researchers use to study causal relationships between variables. Here are some of the most common types:

Non-Equivalent Control Group Design

This design involves selecting two groups of participants that are similar in every way except for the independent variable(s) that the researcher is testing. One group receives the treatment or intervention being studied, while the other group does not. The two groups are then compared to see if there are any significant differences in the outcomes.

Interrupted Time-Series Design

This design involves collecting data on the dependent variable(s) over a period of time, both before and after an intervention or event. The researcher can then determine whether there was a significant change in the dependent variable(s) following the intervention or event.

Pretest-Posttest Design

This design involves measuring the dependent variable(s) before and after an intervention or event, but without a control group. This design can be useful for determining whether the intervention or event had an effect, but it does not allow for control over other factors that may have influenced the outcomes.

Regression Discontinuity Design

This design involves selecting participants based on a specific cutoff point on a continuous variable, such as a test score. Participants on either side of the cutoff point are then compared to determine whether the intervention or event had an effect.

Natural Experiments

This design involves studying the effects of an intervention or event that occurs naturally, without the researcher’s intervention. For example, a researcher might study the effects of a new law or policy that affects certain groups of people. This design is useful when true experiments are not feasible or ethical.

Data Analysis Methods

Here are some data analysis methods that are commonly used in quasi-experimental designs:

Descriptive Statistics

This method involves summarizing the data collected during a study using measures such as mean, median, mode, range, and standard deviation. Descriptive statistics can help researchers identify trends or patterns in the data, and can also be useful for identifying outliers or anomalies.

Inferential Statistics

This method involves using statistical tests to determine whether the results of a study are statistically significant. Inferential statistics can help researchers make generalizations about a population based on the sample data collected during the study. Common statistical tests used in quasi-experimental designs include t-tests, ANOVA, and regression analysis.

Propensity Score Matching

This method is used to reduce bias in quasi-experimental designs by matching participants in the intervention group with participants in the control group who have similar characteristics. This can help to reduce the impact of confounding variables that may affect the study’s results.

Difference-in-differences Analysis

This method is used to compare the difference in outcomes between two groups over time. Researchers can use this method to determine whether a particular intervention has had an impact on the target population over time.

Interrupted Time Series Analysis

This method is used to examine the impact of an intervention or treatment over time by comparing data collected before and after the intervention or treatment. This method can help researchers determine whether an intervention had a significant impact on the target population.

Regression Discontinuity Analysis

This method is used to compare the outcomes of participants who fall on either side of a predetermined cutoff point. This method can help researchers determine whether an intervention had a significant impact on the target population.

Steps in Quasi-Experimental Design

Here are the general steps involved in conducting a quasi-experimental design:

• Identify the research question: Determine the research question and the variables that will be investigated.
• Choose the design: Choose the appropriate quasi-experimental design to address the research question. Examples include the pretest-posttest design, non-equivalent control group design, regression discontinuity design, and interrupted time series design.
• Select the participants: Select the participants who will be included in the study. Participants should be selected based on specific criteria relevant to the research question.
• Measure the variables: Measure the variables that are relevant to the research question. This may involve using surveys, questionnaires, tests, or other measures.
• Implement the intervention or treatment: Implement the intervention or treatment to the participants in the intervention group. This may involve training, education, counseling, or other interventions.
• Collect data: Collect data on the dependent variable(s) before and after the intervention. Data collection may also include collecting data on other variables that may impact the dependent variable(s).
• Analyze the data: Analyze the data collected to determine whether the intervention had a significant impact on the dependent variable(s).
• Draw conclusions: Draw conclusions about the relationship between the independent and dependent variables. If the results suggest a causal relationship, then appropriate recommendations may be made based on the findings.

Quasi-Experimental Design Examples

Here are some examples of real-time quasi-experimental designs:

• Evaluating the impact of a new teaching method: In this study, a group of students are taught using a new teaching method, while another group is taught using the traditional method. The test scores of both groups are compared before and after the intervention to determine whether the new teaching method had a significant impact on student performance.
• Assessing the effectiveness of a public health campaign: In this study, a public health campaign is launched to promote healthy eating habits among a targeted population. The behavior of the population is compared before and after the campaign to determine whether the intervention had a significant impact on the target behavior.
• Examining the impact of a new medication: In this study, a group of patients is given a new medication, while another group is given a placebo. The outcomes of both groups are compared to determine whether the new medication had a significant impact on the targeted health condition.
• Evaluating the effectiveness of a job training program : In this study, a group of unemployed individuals is enrolled in a job training program, while another group is not enrolled in any program. The employment rates of both groups are compared before and after the intervention to determine whether the training program had a significant impact on the employment rates of the participants.
• Assessing the impact of a new policy : In this study, a new policy is implemented in a particular area, while another area does not have the new policy. The outcomes of both areas are compared before and after the intervention to determine whether the new policy had a significant impact on the targeted behavior or outcome.

Applications of Quasi-Experimental Design

Here are some applications of quasi-experimental design:

• Educational research: Quasi-experimental designs are used to evaluate the effectiveness of educational interventions, such as new teaching methods, technology-based learning, or educational policies.
• Health research: Quasi-experimental designs are used to evaluate the effectiveness of health interventions, such as new medications, public health campaigns, or health policies.
• Social science research: Quasi-experimental designs are used to investigate the impact of social interventions, such as job training programs, welfare policies, or criminal justice programs.
• Business research: Quasi-experimental designs are used to evaluate the impact of business interventions, such as marketing campaigns, new products, or pricing strategies.
• Environmental research: Quasi-experimental designs are used to evaluate the impact of environmental interventions, such as conservation programs, pollution control policies, or renewable energy initiatives.

When to use Quasi-Experimental Design

Here are some situations where quasi-experimental designs may be appropriate:

• When the research question involves investigating the effectiveness of an intervention, policy, or program : In situations where it is not feasible or ethical to randomly assign participants to intervention and control groups, quasi-experimental designs can be used to evaluate the impact of the intervention on the targeted outcome.
• When the sample size is small: In situations where the sample size is small, it may be difficult to randomly assign participants to intervention and control groups. Quasi-experimental designs can be used to investigate the impact of an intervention without requiring a large sample size.
• When the research question involves investigating a naturally occurring event : In some situations, researchers may be interested in investigating the impact of a naturally occurring event, such as a natural disaster or a major policy change. Quasi-experimental designs can be used to evaluate the impact of the event on the targeted outcome.
• When the research question involves investigating a long-term intervention: In situations where the intervention or program is long-term, it may be difficult to randomly assign participants to intervention and control groups for the entire duration of the intervention. Quasi-experimental designs can be used to evaluate the impact of the intervention over time.
• When the research question involves investigating the impact of a variable that cannot be manipulated : In some situations, it may not be possible or ethical to manipulate a variable of interest. Quasi-experimental designs can be used to investigate the relationship between the variable and the targeted outcome.

Purpose of Quasi-Experimental Design

The purpose of quasi-experimental design is to investigate the causal relationship between two or more variables when it is not feasible or ethical to conduct a randomized controlled trial (RCT). Quasi-experimental designs attempt to emulate the randomized control trial by mimicking the control group and the intervention group as much as possible.

The key purpose of quasi-experimental design is to evaluate the impact of an intervention, policy, or program on a targeted outcome while controlling for potential confounding factors that may affect the outcome. Quasi-experimental designs aim to answer questions such as: Did the intervention cause the change in the outcome? Would the outcome have changed without the intervention? And was the intervention effective in achieving its intended goals?

Quasi-experimental designs are useful in situations where randomized controlled trials are not feasible or ethical. They provide researchers with an alternative method to evaluate the effectiveness of interventions, policies, and programs in real-life settings. Quasi-experimental designs can also help inform policy and practice by providing valuable insights into the causal relationships between variables.

Overall, the purpose of quasi-experimental design is to provide a rigorous method for evaluating the impact of interventions, policies, and programs while controlling for potential confounding factors that may affect the outcome.

Advantages of Quasi-Experimental Design

Quasi-experimental designs have several advantages over other research designs, such as:

• Greater external validity : Quasi-experimental designs are more likely to have greater external validity than laboratory experiments because they are conducted in naturalistic settings. This means that the results are more likely to generalize to real-world situations.
• Ethical considerations: Quasi-experimental designs often involve naturally occurring events, such as natural disasters or policy changes. This means that researchers do not need to manipulate variables, which can raise ethical concerns.
• More practical: Quasi-experimental designs are often more practical than experimental designs because they are less expensive and easier to conduct. They can also be used to evaluate programs or policies that have already been implemented, which can save time and resources.
• No random assignment: Quasi-experimental designs do not require random assignment, which can be difficult or impossible in some cases, such as when studying the effects of a natural disaster. This means that researchers can still make causal inferences, although they must use statistical techniques to control for potential confounding variables.
• Greater generalizability : Quasi-experimental designs are often more generalizable than experimental designs because they include a wider range of participants and conditions. This can make the results more applicable to different populations and settings.

Limitations of Quasi-Experimental Design

There are several limitations associated with quasi-experimental designs, which include:

• Lack of Randomization: Quasi-experimental designs do not involve randomization of participants into groups, which means that the groups being studied may differ in important ways that could affect the outcome of the study. This can lead to problems with internal validity and limit the ability to make causal inferences.
• Selection Bias: Quasi-experimental designs may suffer from selection bias because participants are not randomly assigned to groups. Participants may self-select into groups or be assigned based on pre-existing characteristics, which may introduce bias into the study.
• History and Maturation: Quasi-experimental designs are susceptible to history and maturation effects, where the passage of time or other events may influence the outcome of the study.
• Lack of Control: Quasi-experimental designs may lack control over extraneous variables that could influence the outcome of the study. This can limit the ability to draw causal inferences from the study.
• Limited Generalizability: Quasi-experimental designs may have limited generalizability because the results may only apply to the specific population and context being studied.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Qualitative Research – Methods, Analysis Types...

Survey Research – Types, Methods, Examples

Focus Groups – Steps, Examples and Guide

Ethnographic Research -Types, Methods and Guide

Questionnaire – Definition, Types, and Examples

Textual Analysis – Types, Examples and Guide

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case AskWhy Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research Research Tools and Apps

Quasi-experimental Research: What It Is, Types & Examples

Much like an actual experiment, quasi-experimental research tries to demonstrate a cause-and-effect link between a dependent and an independent variable. A quasi-experiment, on the other hand, does not depend on random assignment, unlike an actual experiment. The subjects are sorted into groups based on non-random variables.

What is Quasi-Experimental Research?

“Resemblance” is the definition of “quasi.” Individuals are not randomly allocated to conditions or orders of conditions, even though the regression analysis is changed. As a result, quasi-experimental research is research that appears to be experimental but is not.

The directionality problem is avoided in quasi-experimental research since the regression analysis is altered before the multiple regression is assessed. However, because individuals are not randomized at random, there are likely to be additional disparities across conditions in quasi-experimental research.

As a result, in terms of internal consistency, quasi-experiments fall somewhere between correlational research and actual experiments.

The key component of a true experiment is randomly allocated groups. This means that each person has an equivalent chance of being assigned to the experimental group or the control group, depending on whether they are manipulated or not.

Simply put, a quasi-experiment is not a real experiment. A quasi-experiment does not feature randomly allocated groups since the main component of a real experiment is randomly assigned groups. Why is it so crucial to have randomly allocated groups, given that they constitute the only distinction between quasi-experimental and actual  experimental research ?

Let’s use an example to illustrate our point. Let’s assume we want to discover how new psychological therapy affects depressed patients. In a genuine trial, you’d split half of the psych ward into treatment groups, With half getting the new psychotherapy therapy and the other half receiving standard  depression treatment .

And the physicians compare the outcomes of this treatment to the results of standard treatments to see if this treatment is more effective. Doctors, on the other hand, are unlikely to agree with this genuine experiment since they believe it is unethical to treat one group while leaving another untreated.

A quasi-experimental study will be useful in this case. Instead of allocating these patients at random, you uncover pre-existing psychotherapist groups in the hospitals. Clearly, there’ll be counselors who are eager to undertake these trials as well as others who prefer to stick to the old ways.

These pre-existing groups can be used to compare the symptom development of individuals who received the novel therapy with those who received the normal course of treatment, even though the groups weren’t chosen at random.

If any substantial variations between them can be well explained, you may be very assured that any differences are attributable to the treatment but not to other extraneous variables.

As we mentioned before, quasi-experimental research entails manipulating an independent variable by randomly assigning people to conditions or sequences of conditions. Non-equivalent group designs, pretest-posttest designs, and regression discontinuity designs are only a few of the essential types.

What are quasi-experimental research designs?

Quasi-experimental research designs are a type of research design that is similar to experimental designs but doesn’t give full control over the independent variable(s) like true experimental designs do.

In a quasi-experimental design, the researcher changes or watches an independent variable, but the participants are not put into groups at random. Instead, people are put into groups based on things they already have in common, like their age, gender, or how many times they have seen a certain stimulus.

Because the assignments are not random, it is harder to draw conclusions about cause and effect than in a real experiment. However, quasi-experimental designs are still useful when randomization is not possible or ethical.

The true experimental design may be impossible to accomplish or just too expensive, especially for researchers with few resources. Quasi-experimental designs enable you to investigate an issue by utilizing data that has already been paid for or gathered by others (often the government). 

Because they allow better control for confounding variables than other forms of studies, they have higher external validity than most genuine experiments and higher  internal validity  (less than true experiments) than other non-experimental research.

Is quasi-experimental research quantitative or qualitative?

Quasi-experimental research is a quantitative research method. It involves numerical data collection and statistical analysis. Quasi-experimental research compares groups with different circumstances or treatments to find cause-and-effect links. 

It draws statistical conclusions from quantitative data. Qualitative data can enhance quasi-experimental research by revealing participants’ experiences and opinions, but quantitative data is the method’s foundation.

Quasi-experimental research types

There are many different sorts of quasi-experimental designs. Three of the most popular varieties are described below: Design of non-equivalent groups, Discontinuity in regression, and Natural experiments.

Design of Non-equivalent Groups

Example: design of non-equivalent groups, discontinuity in regression, example: discontinuity in regression, natural experiments, example: natural experiments.

However, because they couldn’t afford to pay everyone who qualified for the program, they had to use a random lottery to distribute slots.

Experts were able to investigate the program’s impact by utilizing enrolled people as a treatment group and those who were qualified but did not play the jackpot as an experimental group.

How QuestionPro helps in quasi-experimental research?

QuestionPro can be a useful tool in quasi-experimental research because it includes features that can assist you in designing and analyzing your research study. Here are some ways in which QuestionPro can help in quasi-experimental research:

Design surveys

Randomize participants, collect data over time, analyze data, collaborate with your team.

With QuestionPro, you have access to the most mature market research platform and tool that helps you collect and analyze the insights that matter the most. By leveraging InsightsHub, the unified hub for data management, you can ​​leverage the consolidated platform to organize, explore, search, and discover your  research data  in one organized data repository . 

Optimize Your quasi-experimental research with QuestionPro. Get started now!

LEARN MORE         FREE TRIAL

MORE LIKE THIS

Jotform vs Wufoo: Comparison of Features and Prices

Aug 13, 2024

Product or Service: Which is More Important? — Tuesday CX Thoughts

Life@QuestionPro: Thomas Maiwald-Immer’s Experience

Aug 9, 2024

Top 13 Reporting Tools to Transform Your Data Insights & More

Aug 8, 2024

Other categories

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Tuesday CX Thoughts (TCXT)
• Uncategorized
• What’s Coming Up
• Workforce Intelligence

The use and interpretation of quasi-experimental design

Last updated

6 February 2023

Reviewed by

Miroslav Damyanov

Short on time? Get an AI generated summary of this article instead

• What is a quasi-experimental design?

Commonly used in medical informatics (a field that uses digital information to ensure better patient care), researchers generally use this design to evaluate the effectiveness of a treatment – perhaps a type of antibiotic or psychotherapy, or an educational or policy intervention.

Even though quasi-experimental design has been used for some time, relatively little is known about it. Read on to learn the ins and outs of this research design.

Make research less tedious

Dovetail streamlines research to help you uncover and share actionable insights

• When to use a quasi-experimental design

A quasi-experimental design is used when it's not logistically feasible or ethical to conduct randomized, controlled trials. As its name suggests, a quasi-experimental design is almost a true experiment. However, researchers don't randomly select elements or participants in this type of research.

Researchers prefer to apply quasi-experimental design when there are ethical or practical concerns. Let's look at these two reasons more closely.

Ethical reasons

In some situations, the use of randomly assigned elements can be unethical. For instance, providing public healthcare to one group and withholding it to another in research is unethical. A quasi-experimental design would examine the relationship between these two groups to avoid physical danger.

Practical reasons

Randomized controlled trials may not be the best approach in research. For instance, it's impractical to trawl through large sample sizes of participants without using a particular attribute to guide your data collection .

Recruiting participants and properly designing a data-collection attribute to make the research a true experiment requires a lot of time and effort, and can be expensive if you don’t have a large funding stream.

A quasi-experimental design allows researchers to take advantage of previously collected data and use it in their study.

• Examples of quasi-experimental designs

Quasi-experimental research design is common in medical research, but any researcher can use it for research that raises practical and ethical concerns. Here are a few examples of quasi-experimental designs used by different researchers:

Example 1: Determining the effectiveness of math apps in supplementing math classes

A school wanted to supplement its math classes with a math app. To select the best app, the school decided to conduct demo tests on two apps before selecting the one they will purchase.

Scope of the research

Since every grade had two math teachers, each teacher used one of the two apps for three months. They then gave the students the same math exams and compared the results to determine which app was most effective.

Reasons why this is a quasi-experimental study

This simple study is a quasi-experiment since the school didn't randomly assign its students to the applications. They used a pre-existing class structure to conduct the study since it was impractical to randomly assign the students to each app.

Example 2: Determining the effectiveness of teaching modern leadership techniques in start-up businesses

A hypothetical quasi-experimental study was conducted in an economically developing country in a mid-sized city.

Five start-ups in the textile industry and five in the tech industry participated in the study. The leaders attended a six-week workshop on leadership style, team management, and employee motivation.

After a year, the researchers assessed the performance of each start-up company to determine growth. The results indicated that the tech start-ups were further along in their growth than the textile companies.

The basis of quasi-experimental research is a non-randomized subject-selection process. This study didn't use specific aspects to determine which start-up companies should participate. Therefore, the results may seem straightforward, but several aspects may determine the growth of a specific company, apart from the variables used by the researchers.

Example 3: A study to determine the effects of policy reforms and of luring foreign investment on small businesses in two mid-size cities

In a study to determine the economic impact of government reforms in an economically developing country, the government decided to test whether creating reforms directed at small businesses or luring foreign investments would spur the most economic development.

The government selected two cities with similar population demographics and sizes. In one of the cities, they implemented specific policies that would directly impact small businesses, and in the other, they implemented policies to attract foreign investment.

After five years, they collected end-of-year economic growth data from both cities. They looked at elements like local GDP growth, unemployment rates, and housing sales.

The study used a non-randomized selection process to determine which city would participate in the research. Researchers left out certain variables that would play a crucial role in determining the growth of each city. They used pre-existing groups of people based on research conducted in each city, rather than random groups.

• Advantages of a quasi-experimental design

Some advantages of quasi-experimental designs are:

Researchers can manipulate variables to help them meet their study objectives.

It offers high external validity, making it suitable for real-world applications, specifically in social science experiments.

Integrating this methodology into other research designs is easier, especially in true experimental research. This cuts down on the time needed to determine your outcomes.

• Disadvantages of a quasi-experimental design

Despite the pros that come with a quasi-experimental design, there are several disadvantages associated with it, including the following:

It has a lower internal validity since researchers do not have full control over the comparison and intervention groups or between time periods because of differences in characteristics in people, places, or time involved. It may be challenging to determine whether all variables have been used or whether those used in the research impacted the results.

There is the risk of inaccurate data since the research design borrows information from other studies.

There is the possibility of bias since researchers select baseline elements and eligibility.

• What are the different quasi-experimental study designs?

There are three distinct types of quasi-experimental designs:

Nonequivalent

Regression discontinuity, natural experiment.

This is a hybrid of experimental and quasi-experimental methods and is used to leverage the best qualities of the two. Like the true experiment design, nonequivalent group design uses pre-existing groups believed to be comparable. However, it doesn't use randomization, the lack of which is a crucial element for quasi-experimental design.

Researchers usually ensure that no confounding variables impact them throughout the grouping process. This makes the groupings more comparable.

Example of a nonequivalent group design

A small study was conducted to determine whether after-school programs result in better grades. Researchers randomly selected two groups of students: one to implement the new program, the other not to. They then compared the results of the two groups.

This type of quasi-experimental research design calculates the impact of a specific treatment or intervention. It uses a criterion known as "cutoff" that assigns treatment according to eligibility.

Researchers often assign participants above the cutoff to the treatment group. This puts a negligible distinction between the two groups (treatment group and control group).

Example of regression discontinuity

Students must achieve a minimum score to be enrolled in specific US high schools. Since the cutoff score used to determine eligibility for enrollment is arbitrary, researchers can assume that the disparity between students who only just fail to achieve the cutoff point and those who barely pass is a small margin and is due to the difference in the schools that these students attend.

Researchers can then examine the long-term effects of these two groups of kids to determine the effect of attending certain schools. This information can be applied to increase the chances of students being enrolled in these high schools.

This research design is common in laboratory and field experiments where researchers control target subjects by assigning them to different groups. Researchers randomly assign subjects to a treatment group using nature or an external event or situation.

However, even with random assignment, this research design cannot be called a true experiment since nature aspects are observational. Researchers can also exploit these aspects despite having no control over the independent variables.

Example of the natural experiment approach

An example of a natural experiment is the 2008 Oregon Health Study.

Oregon intended to allow more low-income people to participate in Medicaid.

Since they couldn't afford to cover every person who qualified for the program, the state used a random lottery to allocate program slots.

Researchers assessed the program's effectiveness by assigning the selected subjects to a randomly assigned treatment group, while those that didn't win the lottery were considered the control group.

• Differences between quasi-experiments and true experiments

There are several differences between a quasi-experiment and a true experiment:

Participants in true experiments are randomly assigned to the treatment or control group, while participants in a quasi-experiment are not assigned randomly.

In a quasi-experimental design, the control and treatment groups differ in unknown or unknowable ways, apart from the experimental treatments that are carried out. Therefore, the researcher should try as much as possible to control these differences.

Quasi-experimental designs have several "competing hypotheses," which compete with experimental manipulation to explain the observed results.

Quasi-experiments tend to have lower internal validity (the degree of confidence in the research outcomes) than true experiments, but they may offer higher external validity (whether findings can be extended to other contexts) as they involve real-world interventions instead of controlled interventions in artificial laboratory settings.

Despite the distinct difference between true and quasi-experimental research designs, these two research methodologies share the following aspects:

Both study methods subject participants to some form of treatment or conditions.

Researchers have the freedom to measure some of the outcomes of interest.

Researchers can test whether the differences in the outcomes are associated with the treatment.

• An example comparing a true experiment and quasi-experiment

Imagine you wanted to study the effects of junk food on obese people. Here's how you would do this as a true experiment and a quasi-experiment:

How to carry out a true experiment

In a true experiment, some participants would eat junk foods, while the rest would be in the control group, adhering to a regular diet. At the end of the study, you would record the health and discomfort of each group.

This kind of experiment would raise ethical concerns since the participants assigned to the treatment group are required to eat junk food against their will throughout the experiment. This calls for a quasi-experimental design.

How to carry out a quasi-experiment

In quasi-experimental research, you would start by finding out which participants want to try junk food and which prefer to stick to a regular diet. This allows you to assign these two groups based on subject choice.

In this case, you didn't assign participants to a particular group, so you can confidently use the results from the study.

When is a quasi-experimental design used?

Quasi-experimental designs are used when researchers don’t want to use randomization when evaluating their intervention.

What are the characteristics of quasi-experimental designs?

Some of the characteristics of a quasi-experimental design are:

Researchers don't randomly assign participants into groups, but study their existing characteristics and assign them accordingly.

Researchers study the participants in pre- and post-testing to determine the progress of the groups.

Quasi-experimental design is ethical since it doesn’t involve offering or withholding treatment at random.

Quasi-experimental design encompasses a broad range of non-randomized intervention studies. This design is employed when it is not ethical or logistically feasible to conduct randomized controlled trials. Researchers typically employ it when evaluating policy or educational interventions, or in medical or therapy scenarios.

How do you analyze data in a quasi-experimental design?

You can use two-group tests, time-series analysis, and regression analysis to analyze data in a quasi-experiment design. Each option has specific assumptions, strengths, limitations, and data requirements.

Should you be using a customer insights hub?

Do you want to discover previous research faster?

Do you share your research findings with others?

Do you analyze research data?

Start for free today, add your research, and get to key insights faster

Editor’s picks

Last updated: 18 April 2023

Last updated: 27 February 2023

Last updated: 6 February 2023

Last updated: 5 February 2023

Last updated: 16 April 2023

Last updated: 9 March 2023

Last updated: 30 April 2024

Last updated: 12 December 2023

Last updated: 11 March 2024

Last updated: 4 July 2024

Last updated: 6 March 2024

Last updated: 5 March 2024

Last updated: 13 May 2024

Latest articles

Related topics, .css-je19u9{-webkit-align-items:flex-end;-webkit-box-align:flex-end;-ms-flex-align:flex-end;align-items:flex-end;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:row;-ms-flex-direction:row;flex-direction:row;-webkit-box-flex-wrap:wrap;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap;-webkit-box-pack:center;-ms-flex-pack:center;-webkit-justify-content:center;justify-content:center;row-gap:0;text-align:center;max-width:671px;}@media (max-width: 1079px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}}@media (max-width: 799px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}} decide what to .css-1kiodld{max-height:56px;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}@media (max-width: 1079px){.css-1kiodld{display:none;}} build next, decide what to build next, log in or sign up.

Get started for free

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

7.3 Quasi-Experimental Research

Learning objectives.

• Explain what quasi-experimental research is and distinguish it clearly from both experimental and correlational research.
• Describe three different types of quasi-experimental research designs (nonequivalent groups, pretest-posttest, and interrupted time series) and identify examples of each one.

The prefix quasi means “resembling.” Thus quasi-experimental research is research that resembles experimental research but is not true experimental research. Although the independent variable is manipulated, participants are not randomly assigned to conditions or orders of conditions (Cook & Campbell, 1979). Because the independent variable is manipulated before the dependent variable is measured, quasi-experimental research eliminates the directionality problem. But because participants are not randomly assigned—making it likely that there are other differences between conditions—quasi-experimental research does not eliminate the problem of confounding variables. In terms of internal validity, therefore, quasi-experiments are generally somewhere between correlational studies and true experiments.

Quasi-experiments are most likely to be conducted in field settings in which random assignment is difficult or impossible. They are often conducted to evaluate the effectiveness of a treatment—perhaps a type of psychotherapy or an educational intervention. There are many different kinds of quasi-experiments, but we will discuss just a few of the most common ones here.

Nonequivalent Groups Design

Recall that when participants in a between-subjects experiment are randomly assigned to conditions, the resulting groups are likely to be quite similar. In fact, researchers consider them to be equivalent. When participants are not randomly assigned to conditions, however, the resulting groups are likely to be dissimilar in some ways. For this reason, researchers consider them to be nonequivalent. A nonequivalent groups design , then, is a between-subjects design in which participants have not been randomly assigned to conditions.

Imagine, for example, a researcher who wants to evaluate a new method of teaching fractions to third graders. One way would be to conduct a study with a treatment group consisting of one class of third-grade students and a control group consisting of another class of third-grade students. This would be a nonequivalent groups design because the students are not randomly assigned to classes by the researcher, which means there could be important differences between them. For example, the parents of higher achieving or more motivated students might have been more likely to request that their children be assigned to Ms. Williams’s class. Or the principal might have assigned the “troublemakers” to Mr. Jones’s class because he is a stronger disciplinarian. Of course, the teachers’ styles, and even the classroom environments, might be very different and might cause different levels of achievement or motivation among the students. If at the end of the study there was a difference in the two classes’ knowledge of fractions, it might have been caused by the difference between the teaching methods—but it might have been caused by any of these confounding variables.

Of course, researchers using a nonequivalent groups design can take steps to ensure that their groups are as similar as possible. In the present example, the researcher could try to select two classes at the same school, where the students in the two classes have similar scores on a standardized math test and the teachers are the same sex, are close in age, and have similar teaching styles. Taking such steps would increase the internal validity of the study because it would eliminate some of the most important confounding variables. But without true random assignment of the students to conditions, there remains the possibility of other important confounding variables that the researcher was not able to control.

Pretest-Posttest Design

In a pretest-posttest design , the dependent variable is measured once before the treatment is implemented and once after it is implemented. Imagine, for example, a researcher who is interested in the effectiveness of an antidrug education program on elementary school students’ attitudes toward illegal drugs. The researcher could measure the attitudes of students at a particular elementary school during one week, implement the antidrug program during the next week, and finally, measure their attitudes again the following week. The pretest-posttest design is much like a within-subjects experiment in which each participant is tested first under the control condition and then under the treatment condition. It is unlike a within-subjects experiment, however, in that the order of conditions is not counterbalanced because it typically is not possible for a participant to be tested in the treatment condition first and then in an “untreated” control condition.

If the average posttest score is better than the average pretest score, then it makes sense to conclude that the treatment might be responsible for the improvement. Unfortunately, one often cannot conclude this with a high degree of certainty because there may be other explanations for why the posttest scores are better. One category of alternative explanations goes under the name of history . Other things might have happened between the pretest and the posttest. Perhaps an antidrug program aired on television and many of the students watched it, or perhaps a celebrity died of a drug overdose and many of the students heard about it. Another category of alternative explanations goes under the name of maturation . Participants might have changed between the pretest and the posttest in ways that they were going to anyway because they are growing and learning. If it were a yearlong program, participants might become less impulsive or better reasoners and this might be responsible for the change.

Another alternative explanation for a change in the dependent variable in a pretest-posttest design is regression to the mean . This refers to the statistical fact that an individual who scores extremely on a variable on one occasion will tend to score less extremely on the next occasion. For example, a bowler with a long-term average of 150 who suddenly bowls a 220 will almost certainly score lower in the next game. Her score will “regress” toward her mean score of 150. Regression to the mean can be a problem when participants are selected for further study because of their extreme scores. Imagine, for example, that only students who scored especially low on a test of fractions are given a special training program and then retested. Regression to the mean all but guarantees that their scores will be higher even if the training program has no effect. A closely related concept—and an extremely important one in psychological research—is spontaneous remission . This is the tendency for many medical and psychological problems to improve over time without any form of treatment. The common cold is a good example. If one were to measure symptom severity in 100 common cold sufferers today, give them a bowl of chicken soup every day, and then measure their symptom severity again in a week, they would probably be much improved. This does not mean that the chicken soup was responsible for the improvement, however, because they would have been much improved without any treatment at all. The same is true of many psychological problems. A group of severely depressed people today is likely to be less depressed on average in 6 months. In reviewing the results of several studies of treatments for depression, researchers Michael Posternak and Ivan Miller found that participants in waitlist control conditions improved an average of 10 to 15% before they received any treatment at all (Posternak & Miller, 2001). Thus one must generally be very cautious about inferring causality from pretest-posttest designs.

Does Psychotherapy Work?

Early studies on the effectiveness of psychotherapy tended to use pretest-posttest designs. In a classic 1952 article, researcher Hans Eysenck summarized the results of 24 such studies showing that about two thirds of patients improved between the pretest and the posttest (Eysenck, 1952). But Eysenck also compared these results with archival data from state hospital and insurance company records showing that similar patients recovered at about the same rate without receiving psychotherapy. This suggested to Eysenck that the improvement that patients showed in the pretest-posttest studies might be no more than spontaneous remission. Note that Eysenck did not conclude that psychotherapy was ineffective. He merely concluded that there was no evidence that it was, and he wrote of “the necessity of properly planned and executed experimental studies into this important field” (p. 323). You can read the entire article here:

http://psychclassics.yorku.ca/Eysenck/psychotherapy.htm

Fortunately, many other researchers took up Eysenck’s challenge, and by 1980 hundreds of experiments had been conducted in which participants were randomly assigned to treatment and control conditions, and the results were summarized in a classic book by Mary Lee Smith, Gene Glass, and Thomas Miller (Smith, Glass, & Miller, 1980). They found that overall psychotherapy was quite effective, with about 80% of treatment participants improving more than the average control participant. Subsequent research has focused more on the conditions under which different types of psychotherapy are more or less effective.

In a classic 1952 article, researcher Hans Eysenck pointed out the shortcomings of the simple pretest-posttest design for evaluating the effectiveness of psychotherapy.

Wikimedia Commons – CC BY-SA 3.0.

Interrupted Time Series Design

A variant of the pretest-posttest design is the interrupted time-series design . A time series is a set of measurements taken at intervals over a period of time. For example, a manufacturing company might measure its workers’ productivity each week for a year. In an interrupted time series-design, a time series like this is “interrupted” by a treatment. In one classic example, the treatment was the reduction of the work shifts in a factory from 10 hours to 8 hours (Cook & Campbell, 1979). Because productivity increased rather quickly after the shortening of the work shifts, and because it remained elevated for many months afterward, the researcher concluded that the shortening of the shifts caused the increase in productivity. Notice that the interrupted time-series design is like a pretest-posttest design in that it includes measurements of the dependent variable both before and after the treatment. It is unlike the pretest-posttest design, however, in that it includes multiple pretest and posttest measurements.

Figure 7.5 “A Hypothetical Interrupted Time-Series Design” shows data from a hypothetical interrupted time-series study. The dependent variable is the number of student absences per week in a research methods course. The treatment is that the instructor begins publicly taking attendance each day so that students know that the instructor is aware of who is present and who is absent. The top panel of Figure 7.5 “A Hypothetical Interrupted Time-Series Design” shows how the data might look if this treatment worked. There is a consistently high number of absences before the treatment, and there is an immediate and sustained drop in absences after the treatment. The bottom panel of Figure 7.5 “A Hypothetical Interrupted Time-Series Design” shows how the data might look if this treatment did not work. On average, the number of absences after the treatment is about the same as the number before. This figure also illustrates an advantage of the interrupted time-series design over a simpler pretest-posttest design. If there had been only one measurement of absences before the treatment at Week 7 and one afterward at Week 8, then it would have looked as though the treatment were responsible for the reduction. The multiple measurements both before and after the treatment suggest that the reduction between Weeks 7 and 8 is nothing more than normal week-to-week variation.

Figure 7.5 A Hypothetical Interrupted Time-Series Design

The top panel shows data that suggest that the treatment caused a reduction in absences. The bottom panel shows data that suggest that it did not.

Combination Designs

A type of quasi-experimental design that is generally better than either the nonequivalent groups design or the pretest-posttest design is one that combines elements of both. There is a treatment group that is given a pretest, receives a treatment, and then is given a posttest. But at the same time there is a control group that is given a pretest, does not receive the treatment, and then is given a posttest. The question, then, is not simply whether participants who receive the treatment improve but whether they improve more than participants who do not receive the treatment.

Imagine, for example, that students in one school are given a pretest on their attitudes toward drugs, then are exposed to an antidrug program, and finally are given a posttest. Students in a similar school are given the pretest, not exposed to an antidrug program, and finally are given a posttest. Again, if students in the treatment condition become more negative toward drugs, this could be an effect of the treatment, but it could also be a matter of history or maturation. If it really is an effect of the treatment, then students in the treatment condition should become more negative than students in the control condition. But if it is a matter of history (e.g., news of a celebrity drug overdose) or maturation (e.g., improved reasoning), then students in the two conditions would be likely to show similar amounts of change. This type of design does not completely eliminate the possibility of confounding variables, however. Something could occur at one of the schools but not the other (e.g., a student drug overdose), so students at the first school would be affected by it while students at the other school would not.

Finally, if participants in this kind of design are randomly assigned to conditions, it becomes a true experiment rather than a quasi experiment. In fact, it is the kind of experiment that Eysenck called for—and that has now been conducted many times—to demonstrate the effectiveness of psychotherapy.

Key Takeaways

• Quasi-experimental research involves the manipulation of an independent variable without the random assignment of participants to conditions or orders of conditions. Among the important types are nonequivalent groups designs, pretest-posttest, and interrupted time-series designs.
• Quasi-experimental research eliminates the directionality problem because it involves the manipulation of the independent variable. It does not eliminate the problem of confounding variables, however, because it does not involve random assignment to conditions. For these reasons, quasi-experimental research is generally higher in internal validity than correlational studies but lower than true experiments.
• Practice: Imagine that two college professors decide to test the effect of giving daily quizzes on student performance in a statistics course. They decide that Professor A will give quizzes but Professor B will not. They will then compare the performance of students in their two sections on a common final exam. List five other variables that might differ between the two sections that could affect the results.

Discussion: Imagine that a group of obese children is recruited for a study in which their weight is measured, then they participate for 3 months in a program that encourages them to be more active, and finally their weight is measured again. Explain how each of the following might affect the results:

• regression to the mean
• spontaneous remission

Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design & analysis issues in field settings . Boston, MA: Houghton Mifflin.

Eysenck, H. J. (1952). The effects of psychotherapy: An evaluation. Journal of Consulting Psychology, 16 , 319–324.

Posternak, M. A., & Miller, I. (2001). Untreated short-term course of major depression: A meta-analysis of studies using outcomes from studies using wait-list control groups. Journal of Affective Disorders, 66 , 139–146.

Smith, M. L., Glass, G. V., & Miller, T. I. (1980). The benefits of psychotherapy . Baltimore, MD: Johns Hopkins University Press.

Research Methods in Psychology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

A Modern Guide to Understanding and Conducting Research in Psychology

Chapter 7 quasi-experimental research, learning objectives.

• Explain what quasi-experimental research is and distinguish it clearly from both experimental and correlational research.
• Describe three different types of quasi-experimental research designs (nonequivalent groups, pretest-posttest, and interrupted time series) and identify examples of each one.

The prefix quasi means “resembling.” Thus quasi-experimental research is research that resembles experimental research but is not true experimental research. Although the independent variable is manipulated, participants are not randomly assigned to conditions or orders of conditions ( Cook et al., 1979 ) . Because the independent variable is manipulated before the dependent variable is measured, quasi-experimental research eliminates the directionality problem. But because participants are not randomly assigned—making it likely that there are other differences between conditions—quasi-experimental research does not eliminate the problem of confounding variables. In terms of internal validity, therefore, quasi-experiments are generally somewhere between correlational studies and true experiments.

Quasi-experiments are most likely to be conducted in field settings in which random assignment is difficult or impossible. They are often conducted to evaluate the effectiveness of a treatment—perhaps a type of psychotherapy or an educational intervention. There are many different kinds of quasi-experiments, but we will discuss just a few of the most common ones here, focusing first on nonequivalent groups, pretest-posttest, interrupted time series, and combination designs before turning to single subject designs (including reversal and multiple-baseline designs).

7.1 Nonequivalent Groups Design

Recall that when participants in a between-subjects experiment are randomly assigned to conditions, the resulting groups are likely to be quite similar. In fact, researchers consider them to be equivalent. When participants are not randomly assigned to conditions, however, the resulting groups are likely to be dissimilar in some ways. For this reason, researchers consider them to be nonequivalent. A nonequivalent groups design , then, is a between-subjects design in which participants have not been randomly assigned to conditions.

Imagine, for example, a researcher who wants to evaluate a new method of teaching fractions to third graders. One way would be to conduct a study with a treatment group consisting of one class of third-grade students and a control group consisting of another class of third-grade students. This would be a nonequivalent groups design because the students are not randomly assigned to classes by the researcher, which means there could be important differences between them. For example, the parents of higher achieving or more motivated students might have been more likely to request that their children be assigned to Ms. Williams’s class. Or the principal might have assigned the “troublemakers” to Mr. Jones’s class because he is a stronger disciplinarian. Of course, the teachers’ styles, and even the classroom environments, might be very different and might cause different levels of achievement or motivation among the students. If at the end of the study there was a difference in the two classes’ knowledge of fractions, it might have been caused by the difference between the teaching methods—but it might have been caused by any of these confounding variables.

Of course, researchers using a nonequivalent groups design can take steps to ensure that their groups are as similar as possible. In the present example, the researcher could try to select two classes at the same school, where the students in the two classes have similar scores on a standardized math test and the teachers are the same sex, are close in age, and have similar teaching styles. Taking such steps would increase the internal validity of the study because it would eliminate some of the most important confounding variables. But without true random assignment of the students to conditions, there remains the possibility of other important confounding variables that the researcher was not able to control.

7.2 Pretest-Posttest Design

In a pretest-posttest design , the dependent variable is measured once before the treatment is implemented and once after it is implemented. Imagine, for example, a researcher who is interested in the effectiveness of an STEM education program on elementary school students’ attitudes toward science, technology, engineering and math. The researcher could measure the attitudes of students at a particular elementary school during one week, implement the STEM program during the next week, and finally, measure their attitudes again the following week. The pretest-posttest design is much like a within-subjects experiment in which each participant is tested first under the control condition and then under the treatment condition. It is unlike a within-subjects experiment, however, in that the order of conditions is not counterbalanced because it typically is not possible for a participant to be tested in the treatment condition first and then in an “untreated” control condition.

If the average posttest score is better than the average pretest score, then it makes sense to conclude that the treatment might be responsible for the improvement. Unfortunately, one often cannot conclude this with a high degree of certainty because there may be other explanations for why the posttest scores are better. One category of alternative explanations goes under the name of history . Other things might have happened between the pretest and the posttest. Perhaps an science program aired on television and many of the students watched it, or perhaps a major scientific discover occured and many of the students heard about it. Another category of alternative explanations goes under the name of maturation . Participants might have changed between the pretest and the posttest in ways that they were going to anyway because they are growing and learning. If it were a yearlong program, participants might become more exposed to STEM subjects in class or better reasoners and this might be responsible for the change.

Another alternative explanation for a change in the dependent variable in a pretest-posttest design is regression to the mean . This refers to the statistical fact that an individual who scores extremely on a variable on one occasion will tend to score less extremely on the next occasion. For example, a bowler with a long-term average of 150 who suddenly bowls a 220 will almost certainly score lower in the next game. Her score will “regress” toward her mean score of 150. Regression to the mean can be a problem when participants are selected for further study because of their extreme scores. Imagine, for example, that only students who scored especially low on a test of fractions are given a special training program and then retested. Regression to the mean all but guarantees that their scores will be higher even if the training program has no effect. A closely related concept—and an extremely important one in psychological research—is spontaneous remission . This is the tendency for many medical and psychological problems to improve over time without any form of treatment. The common cold is a good example. If one were to measure symptom severity in 100 common cold sufferers today, give them a bowl of chicken soup every day, and then measure their symptom severity again in a week, they would probably be much improved. This does not mean that the chicken soup was responsible for the improvement, however, because they would have been much improved without any treatment at all. The same is true of many psychological problems. A group of severely depressed people today is likely to be less depressed on average in 6 months. In reviewing the results of several studies of treatments for depression, researchers Michael Posternak and Ivan Miller found that participants in waitlist control conditions improved an average of 10 to 15% before they received any treatment at all ( Posternak & Miller, 2001 ) . Thus one must generally be very cautious about inferring causality from pretest-posttest designs.

Finally, it is possible that the act of taking a pretest can sensitize participants to the measurement process or heighten their awareness of the variable under investigation. This heightened sensitivity, called a testing effect , can subsequently lead to changes in their posttest responses, even in the absence of any external intervention effect.

7.3 Interrupted Time Series Design

A variant of the pretest-posttest design is the interrupted time-series design . A time series is a set of measurements taken at intervals over a period of time. For example, a manufacturing company might measure its workers’ productivity each week for a year. In an interrupted time series-design, a time series like this is “interrupted” by a treatment. In a recent COVID-19 study, the intervention involved the implementation of state-issued mask mandates and restrictions on on-premises restaurant dining. The researchers examined the impact of these measures on COVID-19 cases and deaths ( Guy Jr et al., 2021 ) . Since there was a rapid reduction in daily case and death growth rates following the implementation of mask mandates, and this effect persisted for an extended period, the researchers concluded that the implementation of mask mandates was the cause of the decrease in COVID-19 transmission. This study employed an interrupted time series design, similar to a pretest-posttest design, as it involved measuring the outcomes before and after the intervention. However, unlike the pretest-posttest design, it incorporated multiple measurements before and after the intervention, providing a more comprehensive analysis of the policy impacts.

Figure 7.1 shows data from a hypothetical interrupted time-series study. The dependent variable is the number of student absences per week in a research methods course. The treatment is that the instructor begins publicly taking attendance each day so that students know that the instructor is aware of who is present and who is absent. The top panel of Figure 7.1 shows how the data might look if this treatment worked. There is a consistently high number of absences before the treatment, and there is an immediate and sustained drop in absences after the treatment. The bottom panel of Figure 7.1 shows how the data might look if this treatment did not work. On average, the number of absences after the treatment is about the same as the number before. This figure also illustrates an advantage of the interrupted time-series design over a simpler pretest-posttest design. If there had been only one measurement of absences before the treatment at Week 7 and one afterward at Week 8, then it would have looked as though the treatment were responsible for the reduction. The multiple measurements both before and after the treatment suggest that the reduction between Weeks 7 and 8 is nothing more than normal week-to-week variation.

Figure 7.1: Hypothetical interrupted time-series design. The top panel shows data that suggest that the treatment caused a reduction in absences. The bottom panel shows data that suggest that it did not.

7.4 Combination Designs

A type of quasi-experimental design that is generally better than either the nonequivalent groups design or the pretest-posttest design is one that combines elements of both. There is a treatment group that is given a pretest, receives a treatment, and then is given a posttest. But at the same time there is a control group that is given a pretest, does not receive the treatment, and then is given a posttest. The question, then, is not simply whether participants who receive the treatment improve but whether they improve more than participants who do not receive the treatment.

Imagine, for example, that students in one school are given a pretest on their current level of engagement in pro-environmental behaviors (i.e., recycling, eating less red meat, abstaining for single-use plastics, etc.), then are exposed to an pro-environmental program in which they learn about the effects of human caused climate change on the planet, and finally are given a posttest. Students in a similar school are given the pretest, not exposed to an pro-environmental program, and finally are given a posttest. Again, if students in the treatment condition become more involved in pro-environmental behaviors, this could be an effect of the treatment, but it could also be a matter of history or maturation. If it really is an effect of the treatment, then students in the treatment condition should become engage in more pro-environmental behaviors than students in the control condition. But if it is a matter of history (e.g., news of a forest fire or drought) or maturation (e.g., improved reasoning or sense of responsibility), then students in the two conditions would be likely to show similar amounts of change. This type of design does not completely eliminate the possibility of confounding variables, however. Something could occur at one of the schools but not the other (e.g., a local heat wave with record high temperatures), so students at the first school would be affected by it while students at the other school would not.

Finally, if participants in this kind of design are randomly assigned to conditions, it becomes a true experiment rather than a quasi experiment. In fact, this kind of design has now been conducted many times—to demonstrate the effectiveness of psychotherapy.

KEY TAKEAWAYS

• Quasi-experimental research involves the manipulation of an independent variable without the random assignment of participants to conditions or orders of conditions. Among the important types are nonequivalent groups designs, pretest-posttest, and interrupted time-series designs.
• Quasi-experimental research eliminates the directionality problem because it involves the manipulation of the independent variable. It does not eliminate the problem of confounding variables, however, because it does not involve random assignment to conditions. For these reasons, quasi-experimental research is generally higher in internal validity than correlational studies but lower than true experiments.
• Practice: Imagine that two college professors decide to test the effect of giving daily quizzes on student performance in a statistics course. They decide that Professor A will give quizzes but Professor B will not. They will then compare the performance of students in their two sections on a common final exam. List five other variables that might differ between the two sections that could affect the results.

regression to the mean

Spontaneous remission, 7.5 single-subject research.

• Explain what single-subject research is, including how it differs from other types of psychological research and who uses single-subject research and why.
• Design simple single-subject studies using reversal and multiple-baseline designs.
• Explain how single-subject research designs address the issue of internal validity.
• Interpret the results of simple single-subject studies based on the visual inspection of graphed data.
• Explain some of the points of disagreement between advocates of single-subject research and advocates of group research.

Researcher Vance Hall and his colleagues were faced with the challenge of increasing the extent to which six disruptive elementary school students stayed focused on their schoolwork ( Hall et al., 1968 ) . For each of several days, the researchers carefully recorded whether or not each student was doing schoolwork every 10 seconds during a 30-minute period. Once they had established this baseline, they introduced a treatment. The treatment was that when the student was doing schoolwork, the teacher gave him or her positive attention in the form of a comment like “good work” or a pat on the shoulder. The result was that all of the students dramatically increased their time spent on schoolwork and decreased their disruptive behavior during this treatment phase. For example, a student named Robbie originally spent 25% of his time on schoolwork and the other 75% “snapping rubber bands, playing with toys from his pocket, and talking and laughing with peers” (p. 3). During the treatment phase, however, he spent 71% of his time on schoolwork and only 29% on other activities. Finally, when the researchers had the teacher stop giving positive attention, the students all decreased their studying and increased their disruptive behavior. This was consistent with the claim that it was, in fact, the positive attention that was responsible for the increase in studying. This was one of the first studies to show that attending to positive behavior—and ignoring negative behavior—could be a quick and effective way to deal with problem behavior in an applied setting.

Figure 7.2: Single-subject research has shown that positive attention from a teacher for studying can increase studying and decrease disruptive behavior. Photo by Jerry Wang on Unsplash.

Most of this book is about what can be called group research, which typically involves studying a large number of participants and combining their data to draw general conclusions about human behavior. The study by Hall and his colleagues, in contrast, is an example of single-subject research, which typically involves studying a small number of participants and focusing closely on each individual. In this section, we consider this alternative approach. We begin with an overview of single-subject research, including some assumptions on which it is based, who conducts it, and why they do. We then look at some basic single-subject research designs and how the data from those designs are analyzed. Finally, we consider some of the strengths and weaknesses of single-subject research as compared with group research and see how these two approaches can complement each other.

Overview of Single-Subject Research

What is single-subject research.

Single-subject research is a type of quantitative, quasi-experimental research that involves studying in detail the behavior of each of a small number of participants. Note that the term single-subject does not mean that only one participant is studied; it is more typical for there to be somewhere between two and 10 participants. (This is why single-subject research designs are sometimes called small-n designs, where n is the statistical symbol for the sample size.) Single-subject research can be contrasted with group research , which typically involves studying large numbers of participants and examining their behavior primarily in terms of group means, standard deviations, and so on. The majority of this book is devoted to understanding group research, which is the most common approach in psychology. But single-subject research is an important alternative, and it is the primary approach in some areas of psychology.

Before continuing, it is important to distinguish single-subject research from two other approaches, both of which involve studying in detail a small number of participants. One is qualitative research, which focuses on understanding people’s subjective experience by collecting relatively unstructured data (e.g., detailed interviews) and analyzing those data using narrative rather than quantitative techniques (see. Single-subject research, in contrast, focuses on understanding objective behavior through experimental manipulation and control, collecting highly structured data, and analyzing those data quantitatively.

It is also important to distinguish single-subject research from case studies. A case study is a detailed description of an individual, which can include both qualitative and quantitative analyses. (Case studies that include only qualitative analyses can be considered a type of qualitative research.) The history of psychology is filled with influential cases studies, such as Sigmund Freud’s description of “Anna O.” (see box “The Case of ‘Anna O.’”) and John Watson and Rosalie Rayner’s description of Little Albert ( Watson & Rayner, 1920 ) who learned to fear a white rat—along with other furry objects—when the researchers made a loud noise while he was playing with the rat. Case studies can be useful for suggesting new research questions and for illustrating general principles. They can also help researchers understand rare phenomena, such as the effects of damage to a specific part of the human brain. As a general rule, however, case studies cannot substitute for carefully designed group or single-subject research studies. One reason is that case studies usually do not allow researchers to determine whether specific events are causally related, or even related at all. For example, if a patient is described in a case study as having been sexually abused as a child and then as having developed an eating disorder as a teenager, there is no way to determine whether these two events had anything to do with each other. A second reason is that an individual case can always be unusual in some way and therefore be unrepresentative of people more generally. Thus case studies have serious problems with both internal and external validity.

The Case of “Anna O.”

Sigmund Freud used the case of a young woman he called “Anna O.” to illustrate many principles of his theory of psychoanalysis ( Freud, 1957 ) . (Her real name was Bertha Pappenheim, and she was an early feminist who went on to make important contributions to the field of social work.) Anna had come to Freud’s colleague Josef Breuer around 1880 with a variety of odd physical and psychological symptoms. One of them was that for several weeks she was unable to drink any fluids. According to Freud,

She would take up the glass of water that she longed for, but as soon as it touched her lips she would push it away like someone suffering from hydrophobia.…She lived only on fruit, such as melons, etc., so as to lessen her tormenting thirst (p. 9).

But according to Freud, a breakthrough came one day while Anna was under hypnosis.

[S]he grumbled about her English “lady-companion,” whom she did not care for, and went on to describe, with every sign of disgust, how she had once gone into this lady’s room and how her little dog—horrid creature!—had drunk out of a glass there. The patient had said nothing, as she had wanted to be polite. After giving further energetic expression to the anger she had held back, she asked for something to drink, drank a large quantity of water without any difficulty, and awoke from her hypnosis with the glass at her lips; and thereupon the disturbance vanished, never to return.

Freud’s interpretation was that Anna had repressed the memory of this incident along with the emotion that it triggered and that this was what had caused her inability to drink. Furthermore, her recollection of the incident, along with her expression of the emotion she had repressed, caused the symptom to go away.

As an illustration of Freud’s theory, the case study of Anna O. is quite effective. As evidence for the theory, however, it is essentially worthless. The description provides no way of knowing whether Anna had really repressed the memory of the dog drinking from the glass, whether this repression had caused her inability to drink, or whether recalling this “trauma” relieved the symptom. It is also unclear from this case study how typical or atypical Anna’s experience was.

Figure 7.3: “Anna O.” was the subject of a famous case study used by Freud to illustrate the principles of psychoanalysis. Source: Wikimedia Commons

Assumptions of Single-Subject Research

Again, single-subject research involves studying a small number of participants and focusing intensively on the behavior of each one. But why take this approach instead of the group approach? There are two important assumptions underlying single-subject research, and it will help to consider them now.

First and foremost is the assumption that it is important to focus intensively on the behavior of individual participants. One reason for this is that group research can hide individual differences and generate results that do not represent the behavior of any individual. For example, a treatment that has a positive effect for half the people exposed to it but a negative effect for the other half would, on average, appear to have no effect at all. Single-subject research, however, would likely reveal these individual differences. A second reason to focus intensively on individuals is that sometimes it is the behavior of a particular individual that is primarily of interest. A school psychologist, for example, might be interested in changing the behavior of a particular disruptive student. Although previous published research (both single-subject and group research) is likely to provide some guidance on how to do this, conducting a study on this student would be more direct and probably more effective.

Another assumption of single-subject research is that it is important to study strong and consistent effects that have biological or social importance. Applied researchers, in particular, are interested in treatments that have substantial effects on important behaviors and that can be implemented reliably in the real-world contexts in which they occur. This is sometimes referred to as social validity ( Wolf, 1978 ) . The study by Hall and his colleagues, for example, had good social validity because it showed strong and consistent effects of positive teacher attention on a behavior that is of obvious importance to teachers, parents, and students. Furthermore, the teachers found the treatment easy to implement, even in their often chaotic elementary school classrooms.

Who Uses Single-Subject Research?

Single-subject research has been around as long as the field of psychology itself. In the late 1800s, one of psychology’s founders, Wilhelm Wundt, studied sensation and consciousness by focusing intensively on each of a small number of research participants. Herman Ebbinghaus’s research on memory and Ivan Pavlov’s research on classical conditioning are other early examples, both of which are still described in almost every introductory psychology textbook.

In the middle of the 20th century, B. F. Skinner clarified many of the assumptions underlying single-subject research and refined many of its techniques ( Skinner, 1938 ) . He and other researchers then used it to describe how rewards, punishments, and other external factors affect behavior over time. This work was carried out primarily using nonhuman subjects—mostly rats and pigeons. This approach, which Skinner called the experimental analysis of behavior —remains an important subfield of psychology and continues to rely almost exclusively on single-subject research. For examples of this work, look at any issue of the Journal of the Experimental Analysis of Behavior . By the 1960s, many researchers were interested in using this approach to conduct applied research primarily with humans—a subfield now called applied behavior analysis ( Baer et al., 1968 ) . Applied behavior analysis plays a significant role in contemporary research on developmental disabilities, education, organizational behavior, and health, among many other areas. Examples of this work (including the study by Hall and his colleagues) can be found in the Journal of Applied Behavior Analysis . The single-subject approach can also be used by clinicians who take any theoretical perspective—behavioral, cognitive, psychodynamic, or humanistic—to study processes of therapeutic change with individual clients and to document their clients’ improvement ( Kazdin, 2019 ) .

Single-Subject Research Designs

General features of single-subject designs.

Before looking at any specific single-subject research designs, it will be helpful to consider some features that are common to most of them. Many of these features are illustrated in Figure 7.4 , which shows the results of a generic single-subject study. First, the dependent variable (represented on the y-axis of the graph) is measured repeatedly over time (represented by the x-axis) at regular intervals. Second, the study is divided into distinct phases, and the participant is tested under one condition per phase. The conditions are often designated by capital letters: A, B, C, and so on. Thus Figure 7.4 represents a design in which the participant was tested first in one condition (A), then tested in another condition (B), and finally retested in the original condition (A). (This is called a reversal design and will be discussed in more detail shortly.)

Figure 7.4: Results of a generic single-subject study illustrating several principles of single-subject research.

Another important aspect of single-subject research is that the change from one condition to the next does not usually occur after a fixed amount of time or number of observations. Instead, it depends on the participant’s behavior. Specifically, the researcher waits until the participant’s behavior in one condition becomes fairly consistent from observation to observation before changing conditions. This is sometimes referred to as the steady state strategy ( Sidman, 1960 ) . The idea is that when the dependent variable has reached a steady state, then any change across conditions will be relatively easy to detect. Recall that we encountered this same principle when discussing experimental research more generally. The effect of an independent variable is easier to detect when the “noise” in the data is minimized.

Reversal Designs

The most basic single-subject research design is the reversal design , also called the ABA design . During the first phase, A, a baseline is established for the dependent variable. This is the level of responding before any treatment is introduced, and therefore the baseline phase is a kind of control condition. When steady state responding is reached, phase B begins as the researcher introduces the treatment. Again, the researcher waits until that dependent variable reaches a steady state so that it is clear whether and how much it has changed. Finally, the researcher removes the treatment and again waits until the dependent variable reaches a steady state. This basic reversal design can also be extended with the reintroduction of the treatment (ABAB), another return to baseline (ABABA), and so on. The study by Hall and his colleagues was an ABAB reversal design (Figure 7.5 ).

Figure 7.5: An approximation of the results for Hall and colleagues’ participant Robbie in their ABAB reversal design. The percentage of time he spent studying (the dependent variable) was low during the first baseline phase, increased during the first treatment phase until it leveled off, decreased during the second baseline phase, and again increased during the second treatment phase.

Why is the reversal—the removal of the treatment—considered to be necessary in this type of design? If the dependent variable changes after the treatment is introduced, it is not always clear that the treatment was responsible for the change. It is possible that something else changed at around the same time and that this extraneous variable is responsible for the change in the dependent variable. But if the dependent variable changes with the introduction of the treatment and then changes back with the removal of the treatment, it is much clearer that the treatment (and removal of the treatment) is the cause. In other words, the reversal greatly increases the internal validity of the study.

Multiple-Baseline Designs

There are two potential problems with the reversal design—both of which have to do with the removal of the treatment. One is that if a treatment is working, it may be unethical to remove it. For example, if a treatment seemed to reduce the incidence of self-injury in a developmentally disabled child, it would be unethical to remove that treatment just to show that the incidence of self-injury increases. The second problem is that the dependent variable may not return to baseline when the treatment is removed. For example, when positive attention for studying is removed, a student might continue to study at an increased rate. This could mean that the positive attention had a lasting effect on the student’s studying, which of course would be good, but it could also mean that the positive attention was not really the cause of the increased studying in the first place.

One solution to these problems is to use a multiple-baseline design , which is represented in Figure 7.6 . In one version of the design, a baseline is established for each of several participants, and the treatment is then introduced for each one. In essence, each participant is tested in an AB design. The key to this design is that the treatment is introduced at a different time for each participant. The idea is that if the dependent variable changes when the treatment is introduced for one participant, it might be a coincidence. But if the dependent variable changes when the treatment is introduced for multiple participants—especially when the treatment is introduced at different times for the different participants—then it is less likely to be a coincidence.

Figure 7.6: Results of a generic multiple-baseline study. The multiple baselines can be for different participants, dependent variables, or settings. The treatment is introduced at a different time on each baseline.

As an example, consider a study by Scott Ross and Robert Horner ( Ross et al., 2009 ) . They were interested in how a school-wide bullying prevention program affected the bullying behavior of particular problem students. At each of three different schools, the researchers studied two students who had regularly engaged in bullying. During the baseline phase, they observed the students for 10-minute periods each day during lunch recess and counted the number of aggressive behaviors they exhibited toward their peers. (The researchers used handheld computers to help record the data.) After 2 weeks, they implemented the program at one school. After 2 more weeks, they implemented it at the second school. And after 2 more weeks, they implemented it at the third school. They found that the number of aggressive behaviors exhibited by each student dropped shortly after the program was implemented at his or her school. Notice that if the researchers had only studied one school or if they had introduced the treatment at the same time at all three schools, then it would be unclear whether the reduction in aggressive behaviors was due to the bullying program or something else that happened at about the same time it was introduced (e.g., a holiday, a television program, a change in the weather). But with their multiple-baseline design, this kind of coincidence would have to happen three separate times—an unlikely occurrence—to explain their results.

Data Analysis in Single-Subject Research

In addition to its focus on individual participants, single-subject research differs from group research in the way the data are typically analyzed. As we have seen throughout the book, group research involves combining data across participants. Inferential statistics are used to help decide whether the result for the sample is likely to generalize to the population. Single-subject research, by contrast, relies heavily on a very different approach called visual inspection . This means plotting individual participants’ data as shown throughout this chapter, looking carefully at those data, and making judgments about whether and to what extent the independent variable had an effect on the dependent variable. Inferential statistics are typically not used.

In visually inspecting their data, single-subject researchers take several factors into account. One of them is changes in the level of the dependent variable from condition to condition. If the dependent variable is much higher or much lower in one condition than another, this suggests that the treatment had an effect. A second factor is trend , which refers to gradual increases or decreases in the dependent variable across observations. If the dependent variable begins increasing or decreasing with a change in conditions, then again this suggests that the treatment had an effect. It can be especially telling when a trend changes directions—for example, when an unwanted behavior is increasing during baseline but then begins to decrease with the introduction of the treatment. A third factor is latency , which is the time it takes for the dependent variable to begin changing after a change in conditions. In general, if a change in the dependent variable begins shortly after a change in conditions, this suggests that the treatment was responsible.

In the top panel of Figure 7.7 , there are fairly obvious changes in the level and trend of the dependent variable from condition to condition. Furthermore, the latencies of these changes are short; the change happens immediately. This pattern of results strongly suggests that the treatment was responsible for the changes in the dependent variable. In the bottom panel of Figure 7.7 , however, the changes in level are fairly small. And although there appears to be an increasing trend in the treatment condition, it looks as though it might be a continuation of a trend that had already begun during baseline. This pattern of results strongly suggests that the treatment was not responsible for any changes in the dependent variable—at least not to the extent that single-subject researchers typically hope to see.

Figure 7.7: Visual inspection of the data suggests an effective treatment in the top panel but an ineffective treatment in the bottom panel.

The results of single-subject research can also be analyzed using statistical procedures—and this is becoming more common. There are many different approaches, and single-subject researchers continue to debate which are the most useful. One approach parallels what is typically done in group research. The mean and standard deviation of each participant’s responses under each condition are computed and compared, and inferential statistical tests such as the t test or analysis of variance are applied ( Fisch, 2001 ) . (Note that averaging across participants is less common.) Another approach is to compute the percentage of nonoverlapping data (PND) for each participant ( Scruggs & Mastropieri, 2021 ) . This is the percentage of responses in the treatment condition that are more extreme than the most extreme response in a relevant control condition. In the study of Hall and his colleagues, for example, all measures of Robbie’s study time in the first treatment condition were greater than the highest measure in the first baseline, for a PND of 100%. The greater the percentage of nonoverlapping data, the stronger the treatment effect. Still, formal statistical approaches to data analysis in single-subject research are generally considered a supplement to visual inspection, not a replacement for it.

The Single-Subject Versus Group “Debate”

Single-subject research is similar to group research—especially experimental group research—in many ways. They are both quantitative approaches that try to establish causal relationships by manipulating an independent variable, measuring a dependent variable, and controlling extraneous variables. As we will see, single-subject research and group research are probably best conceptualized as complementary approaches.

Data Analysis

One set of disagreements revolves around the issue of data analysis. Some advocates of group research worry that visual inspection is inadequate for deciding whether and to what extent a treatment has affected a dependent variable. One specific concern is that visual inspection is not sensitive enough to detect weak effects. A second is that visual inspection can be unreliable, with different researchers reaching different conclusions about the same set of data ( Danov & Symons, 2008 ) . A third is that the results of visual inspection—an overall judgment of whether or not a treatment was effective—cannot be clearly and efficiently summarized or compared across studies (unlike the measures of relationship strength typically used in group research).

In general, single-subject researchers share these concerns. However, they also argue that their use of the steady state strategy, combined with their focus on strong and consistent effects, minimizes most of them. If the effect of a treatment is difficult to detect by visual inspection because the effect is weak or the data are noisy, then single-subject researchers look for ways to increase the strength of the effect or reduce the noise in the data by controlling extraneous variables (e.g., by administering the treatment more consistently). If the effect is still difficult to detect, then they are likely to consider it neither strong enough nor consistent enough to be of further interest. Many single-subject researchers also point out that statistical analysis is becoming increasingly common and that many of them are using it as a supplement to visual inspection—especially for the purpose of comparing results across studies ( Scruggs & Mastropieri, 2021 ) .

Turning the tables, some advocates of single-subject research worry about the way that group researchers analyze their data. Specifically, they point out that focusing on group means can be highly misleading. Again, imagine that a treatment has a strong positive effect on half the people exposed to it and an equally strong negative effect on the other half. In a traditional between-subjects experiment, the positive effect on half the participants in the treatment condition would be statistically cancelled out by the negative effect on the other half. The mean for the treatment group would then be the same as the mean for the control group, making it seem as though the treatment had no effect when in fact it had a strong effect on every single participant!

But again, group researchers share this concern. Although they do focus on group statistics, they also emphasize the importance of examining distributions of individual scores. For example, if some participants were positively affected by a treatment and others negatively affected by it, this would produce a bimodal distribution of scores and could be detected by looking at a histogram of the data. The use of within-subjects designs is another strategy that allows group researchers to observe effects at the individual level and even to specify what percentage of individuals exhibit strong, medium, weak, and even negative effects.

External Validity

The second issue about which single-subject and group researchers sometimes disagree has to do with external validity—the ability to generalize the results of a study beyond the people and situation actually studied. In particular, advocates of group research point out the difficulty in knowing whether results for just a few participants are likely to generalize to others in the population. Imagine, for example, that in a single-subject study, a treatment has been shown to reduce self-injury for each of two developmentally disabled children. Even if the effect is strong for these two children, how can one know whether this treatment is likely to work for other developmentally disabled children?

Again, single-subject researchers share this concern. In response, they note that the strong and consistent effects they are typically interested in—even when observed in small samples—are likely to generalize to others in the population. Single-subject researchers also note that they place a strong emphasis on replicating their research results. When they observe an effect with a small sample of participants, they typically try to replicate it with another small sample—perhaps with a slightly different type of participant or under slightly different conditions. Each time they observe similar results, they rightfully become more confident in the generality of those results. Single-subject researchers can also point to the fact that the principles of classical and operant conditioning—most of which were discovered using the single-subject approach—have been successfully generalized across an incredibly wide range of species and situations.

And again turning the tables, single-subject researchers have concerns of their own about the external validity of group research. One extremely important point they make is that studying large groups of participants does not entirely solve the problem of generalizing to other individuals. Imagine, for example, a treatment that has been shown to have a small positive effect on average in a large group study. It is likely that although many participants exhibited a small positive effect, others exhibited a large positive effect, and still others exhibited a small negative effect. When it comes to applying this treatment to another large group , we can be fairly sure that it will have a small effect on average. But when it comes to applying this treatment to another individual , we cannot be sure whether it will have a small, a large, or even a negative effect. Another point that single-subject researchers make is that group researchers also face a similar problem when they study a single situation and then generalize their results to other situations. For example, researchers who conduct a study on the effect of cell phone use on drivers on a closed oval track probably want to apply their results to drivers in many other real-world driving situations. But notice that this requires generalizing from a single situation to a population of situations. Thus the ability to generalize is based on much more than just the sheer number of participants one has studied. It requires a careful consideration of the similarity of the participants and situations studied to the population of participants and situations that one wants to generalize to ( Shadish et al., 2002 ) .

Single-Subject and Group Research as Complementary Methods

As with quantitative and qualitative research, it is probably best to conceptualize single-subject research and group research as complementary methods that have different strengths and weaknesses and that are appropriate for answering different kinds of research questions ( Kazdin, 2019 ) . Single-subject research is particularly good for testing the effectiveness of treatments on individuals when the focus is on strong, consistent, and biologically or socially important effects. It is especially useful when the behavior of particular individuals is of interest. Clinicians who work with only one individual at a time may find that it is their only option for doing systematic quantitative research.

Group research, on the other hand, is good for testing the effectiveness of treatments at the group level. Among the advantages of this approach is that it allows researchers to detect weak effects, which can be of interest for many reasons. For example, finding a weak treatment effect might lead to refinements of the treatment that eventually produce a larger and more meaningful effect. Group research is also good for studying interactions between treatments and participant characteristics. For example, if a treatment is effective for those who are high in motivation to change and ineffective for those who are low in motivation to change, then a group design can detect this much more efficiently than a single-subject design. Group research is also necessary to answer questions that cannot be addressed using the single-subject approach, including questions about independent variables that cannot be manipulated (e.g., number of siblings, extroversion, culture).

• Single-subject research—which involves testing a small number of participants and focusing intensively on the behavior of each individual—is an important alternative to group research in psychology.
• Single-subject studies must be distinguished from case studies, in which an individual case is described in detail. Case studies can be useful for generating new research questions, for studying rare phenomena, and for illustrating general principles. However, they cannot substitute for carefully controlled experimental or correlational studies because they are low in internal and external validity.
• Single-subject research designs typically involve measuring the dependent variable repeatedly over time and changing conditions (e.g., from baseline to treatment) when the dependent variable has reached a steady state. This approach allows the researcher to see whether changes in the independent variable are causing changes in the dependent variable.
• Single-subject researchers typically analyze their data by graphing them and making judgments about whether the independent variable is affecting the dependent variable based on level, trend, and latency.
• Differences between single-subject research and group research sometimes lead to disagreements between single-subject and group researchers. These disagreements center on the issues of data analysis and external validity (especially generalization to other people). Single-subject research and group research are probably best seen as complementary methods, with different strengths and weaknesses, that are appropriate for answering different kinds of research questions.
• Does positive attention from a parent increase a child’s toothbrushing behavior?
• Does self-testing while studying improve a student’s performance on weekly spelling tests?
• Does regular exercise help relieve depression?
• Practice: Create a graph that displays the hypothetical results for the study you designed in Exercise 1. Write a paragraph in which you describe what the results show. Be sure to comment on level, trend, and latency.
• Discussion: Imagine you have conducted a single-subject study showing a positive effect of a treatment on the behavior of a man with social anxiety disorder. Your research has been criticized on the grounds that it cannot be generalized to others. How could you respond to this criticism?
• Discussion: Imagine you have conducted a group study showing a positive effect of a treatment on the behavior of a group of people with social anxiety disorder, but your research has been criticized on the grounds that “average” effects cannot be generalized to individuals. How could you respond to this criticism?

7.6 Glossary

The simplest reversal design, in which there is a baseline condition (A), followed by a treatment condition (B), followed by a return to baseline (A).

applied behavior analysis

A subfield of psychology that uses single-subject research and applies the principles of behavior analysis to real-world problems in areas that include education, developmental disabilities, organizational behavior, and health behavior.

A condition in a single-subject research design in which the dependent variable is measured repeatedly in the absence of any treatment. Most designs begin with a baseline condition, and many return to the baseline condition at least once.

A detailed description of an individual case.

experimental analysis of behavior

A subfield of psychology founded by B. F. Skinner that uses single-subject research—often with nonhuman animals—to study relationships primarily between environmental conditions and objectively observable behaviors.

group research

A type of quantitative research that involves studying a large number of participants and examining their behavior in terms of means, standard deviations, and other group-level statistics.

interrupted time-series design

A research design in which a series of measurements of the dependent variable are taken both before and after a treatment.

item-order effect

The effect of responding to one survey item on responses to a later survey item.

Refers collectively to extraneous developmental changes in participants that can occur between a pretest and posttest or between the first and last measurements in a time series. It can provide an alternative explanation for an observed change in the dependent variable.

multiple-baseline design

A single-subject research design in which multiple baselines are established for different participants, different dependent variables, or different contexts and the treatment is introduced at a different time for each baseline.

naturalistic observation

An approach to data collection in which the behavior of interest is observed in the environment in which it typically occurs.

nonequivalent groups design

A between-subjects research design in which participants are not randomly assigned to conditions, usually because participants are in preexisting groups (e.g., students at different schools).

nonexperimental research

Research that lacks the manipulation of an independent variable or the random assignment of participants to conditions or orders of conditions.

open-ended item

A questionnaire item that asks a question and allows respondents to respond in whatever way they want.

percentage of nonoverlapping data

A statistic sometimes used in single-subject research. The percentage of observations in a treatment condition that are more extreme than the most extreme observation in a relevant baseline condition.

pretest-posttest design

A research design in which the dependent variable is measured (the pretest), a treatment is given, and the dependent variable is measured again (the posttest) to see if there is a change in the dependent variable from pretest to posttest.

quasi-experimental research

Research that involves the manipulation of an independent variable but lacks the random assignment of participants to conditions or orders of conditions. It is generally used in field settings to test the effectiveness of a treatment.

rating scale

An ordered set of response options to a closed-ended questionnaire item.

The statistical fact that an individual who scores extremely on one occasion will tend to score less extremely on the next occasion.

A term often used to refer to a participant in survey research.

reversal design

A single-subject research design that begins with a baseline condition with no treatment, followed by the introduction of a treatment, and after that a return to the baseline condition. It can include additional treatment conditions and returns to baseline.

single-subject research

A type of quantitative research that involves examining in detail the behavior of each of a small number of participants.

single-variable research

Research that focuses on a single variable rather than on a statistical relationship between variables.

social validity

The extent to which a single-subject study focuses on an intervention that has a substantial effect on an important behavior and can be implemented reliably in the real-world contexts (e.g., by teachers in a classroom) in which that behavior occurs.

Improvement in a psychological or medical problem over time without any treatment.

steady state strategy

In single-subject research, allowing behavior to become fairly consistent from one observation to the next before changing conditions. This makes any effect of the treatment easier to detect.

survey research

A quantitative research approach that uses self-report measures and large, carefully selected samples.

testing effect

A bias in participants’ responses in which scores on the posttest are influenced by simple exposure to the pretest

visual inspection

The primary approach to data analysis in single-subject research, which involves graphing the data and making a judgment as to whether and to what extent the independent variable affected the dependent variable.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Indian J Psychol Med
• v.43(5); 2021 Sep

The Limitations of Quasi-Experimental Studies, and Methods for Data Analysis When a Quasi-Experimental Research Design Is Unavoidable

Chittaranjan andrade.

1 Dept. of Clinical Psychopharmacology and Neurotoxicology, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India.

A quasi-experimental (QE) study is one that compares outcomes between intervention groups where, for reasons related to ethics or feasibility, participants are not randomized to their respective interventions; an example is the historical comparison of pregnancy outcomes in women who did versus did not receive antidepressant medication during pregnancy. QE designs are sometimes used in noninterventional research, as well; an example is the comparison of neuropsychological test performance between first degree relatives of schizophrenia patients and healthy controls. In QE studies, groups may differ systematically in several ways at baseline, itself; when these differences influence the outcome of interest, comparing outcomes between groups using univariable methods can generate misleading results. Multivariable regression is therefore suggested as a better approach to data analysis; because the effects of confounding variables can be adjusted for in multivariable regression, the unique effect of the grouping variable can be better understood. However, although multivariable regression is better than univariable analyses, there are inevitably inadequately measured, unmeasured, and unknown confounds that may limit the validity of the conclusions drawn. Investigators should therefore employ QE designs sparingly, and only if no other option is available to answer an important research question.

If we wish to study how antidepressant drug treatment affects outcomes in pregnancy, we should ideally randomize depressed pregnant women to receive an antidepressant drug or placebo; this is a randomized controlled trial (RCT) research design. However, because ethics committees are unlikely to approve such RCTs, researchers can only examine pregnancy outcomes (prospectively or retrospectively) in women who did versus did not receive antidepressant drugs; this is a quasi-experimental (QE) research design. A QE study is one that compares outcomes between intervention groups where, for reasons related to ethics or feasibility, participants are not randomized to their respective interventions.

QE studies are problematic because, when participants are not randomized to intervention versus control groups, systematic biases may influence group membership. For example, women who are prescribed and who accept antidepressant medications during pregnancy are likely to be more severely ill than those who are not prescribed or those who do not accept antidepressant medications during pregnancy. So, if adverse pregnancy outcomes are commoner in the antidepressant group, they may be consequences of genetic, physiological, and/or behavioral features that characterize severe depression rather than the antidepressant treatment, itself.

A statistical approach to dealing with such confounds is to perform a regression analysis where pregnancy outcome is the dependent variable and antidepressant treatment, age, sex, socioeconomic status, medical history, family history, smoking history, drinking history, history of use of other substances, nutrition, history of infection during pregnancy, and dozens of other important variables that can influence pregnancy outcomes are independent variables. In such a regression, antidepressant treatment is the independent variable of interest, and the remaining independent variables are confounders that are adjusted for in the regression so that the unique effect of antidepressant treatment on pregnancy outcomes can be better identified. Propensity score matching refines the approach to analysis. 1

Many investigators use QE designs to answer their research questions, though not necessarily as an “experiment” with an intervention. For example, Thomas et al. 2 compared psychosocial dysfunction and family burden between outpatients diagnosed with schizophrenia and those diagnosed with obsessive-compulsive disorder (OCD). Obviously, it is not feasible to randomize patients to have schizophrenia or OCD. So, in their analysis, Thomas et al. 2 first examined whether the two groups were comparable on important sociodemographic and clinical variables. They found that the groups did not differ on, for example, age, family income, and duration of illness (but here, and in other QE studies, as well, these baseline comparisons would almost certainly have been underpowered); however, the schizophrenia group was overrepresented for males and for a history of substance abuse. In further analysis, Thomas et al. 2 used t tests to compare dysfunction and burden between the two groups; they found that both dysfunction and burden were greater in schizophrenia than in OCD.

Now, because patients had not been randomized to their respective diagnoses, it is obvious that the groups could have differed in many ways and not in diagnosis, alone. So, separate regressions should have been conducted with dysfunction and with burden as the dependent variable, and with diagnosis, age, sex, socioeconomic status, duration of illness, history of substance abuse, and others as the independent variables. Such an analysis would allow the investigators to understand not only the unique impact of the diagnosis but also the impact of the other sociodemographic and clinical variables on dysfunction and burden.

Note that inadequately measured, unmeasured, and unknown confounds would still have plagued the results. For example, in this study, 2 severity of illness was an unmeasured confound. What if the authors had, by chance, sampled more severely ill schizophrenia patients and less severely ill OCD patients? Then, illness severity rather than clinical diagnosis would have explained the greater dysfunction and burden observed in the schizophrenia group. Had they obtained a global rating of illness, they could have included it as an additional, important independent variable in the regression.

In another study with a QE design, Harave et al., 3 like Thomas et al., 2 used univariate tests to compare neurocognitive functioning between unaffected first-degree relatives of schizophrenia patients and healthy controls. More correctly, because there are likely to be systematic differences between schizophrenia relatives and healthy controls, they should have performed multivariable regressions with neurocognitive measures as the dependent variables, and with group and confounders as independent variables. Confounders that could have been considered include age, sex, education, family income, a measure of stress, history of smoking, drinking, other substance use, and so on, all of which can directly or indirectly influence neurocognitive performances.

This multivariable regression approach to data analysis in QE designs requires the a priori identification and measurement of all important confounding variables. In such analyses, the sample size for a continuous dependent variable should ideally be at least 10–15 times the number of independent variables. 4 Given that the number of confounding variables to be included is likely to be large, a very large sample will become necessary. Additionally, because studies are never perfect, it would be impossible to adjust for inadequately measured, unmeasured, and unknown confounds (but adjusting for whatever is known and measured is better than making no adjustments, at all). All said and done, the QE research design is best avoided because it is flawed and because even the best statistical approaches to data analysis would be imperfect. The QE design should be considered only when no other options are available. Readers are referred to Harris et al. 5 for a further discussion on QE studies.

Declaration of Conflicting Interests: The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author received no financial support for the research, authorship, and/or publication of this article.

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Chapter 7: Nonexperimental Research

Quasi-Experimental Research

Learning Objectives

• Explain what quasi-experimental research is and distinguish it clearly from both experimental and correlational research.
• Describe three different types of quasi-experimental research designs (nonequivalent groups, pretest-posttest, and interrupted time series) and identify examples of each one.

The prefix  quasi  means “resembling.” Thus quasi-experimental research is research that resembles experimental research but is not true experimental research. Although the independent variable is manipulated, participants are not randomly assigned to conditions or orders of conditions (Cook & Campbell, 1979). [1] Because the independent variable is manipulated before the dependent variable is measured, quasi-experimental research eliminates the directionality problem. But because participants are not randomly assigned—making it likely that there are other differences between conditions—quasi-experimental research does not eliminate the problem of confounding variables. In terms of internal validity, therefore, quasi-experiments are generally somewhere between correlational studies and true experiments.

Quasi-experiments are most likely to be conducted in field settings in which random assignment is difficult or impossible. They are often conducted to evaluate the effectiveness of a treatment—perhaps a type of psychotherapy or an educational intervention. There are many different kinds of quasi-experiments, but we will discuss just a few of the most common ones here.

Nonequivalent Groups Design

Recall that when participants in a between-subjects experiment are randomly assigned to conditions, the resulting groups are likely to be quite similar. In fact, researchers consider them to be equivalent. When participants are not randomly assigned to conditions, however, the resulting groups are likely to be dissimilar in some ways. For this reason, researchers consider them to be nonequivalent. A  nonequivalent groups design , then, is a between-subjects design in which participants have not been randomly assigned to conditions.

Imagine, for example, a researcher who wants to evaluate a new method of teaching fractions to third graders. One way would be to conduct a study with a treatment group consisting of one class of third-grade students and a control group consisting of another class of third-grade students. This design would be a nonequivalent groups design because the students are not randomly assigned to classes by the researcher, which means there could be important differences between them. For example, the parents of higher achieving or more motivated students might have been more likely to request that their children be assigned to Ms. Williams’s class. Or the principal might have assigned the “troublemakers” to Mr. Jones’s class because he is a stronger disciplinarian. Of course, the teachers’ styles, and even the classroom environments, might be very different and might cause different levels of achievement or motivation among the students. If at the end of the study there was a difference in the two classes’ knowledge of fractions, it might have been caused by the difference between the teaching methods—but it might have been caused by any of these confounding variables.

Of course, researchers using a nonequivalent groups design can take steps to ensure that their groups are as similar as possible. In the present example, the researcher could try to select two classes at the same school, where the students in the two classes have similar scores on a standardized math test and the teachers are the same sex, are close in age, and have similar teaching styles. Taking such steps would increase the internal validity of the study because it would eliminate some of the most important confounding variables. But without true random assignment of the students to conditions, there remains the possibility of other important confounding variables that the researcher was not able to control.

Pretest-Posttest Design

In a  pretest-posttest design , the dependent variable is measured once before the treatment is implemented and once after it is implemented. Imagine, for example, a researcher who is interested in the effectiveness of an antidrug education program on elementary school students’ attitudes toward illegal drugs. The researcher could measure the attitudes of students at a particular elementary school during one week, implement the antidrug program during the next week, and finally, measure their attitudes again the following week. The pretest-posttest design is much like a within-subjects experiment in which each participant is tested first under the control condition and then under the treatment condition. It is unlike a within-subjects experiment, however, in that the order of conditions is not counterbalanced because it typically is not possible for a participant to be tested in the treatment condition first and then in an “untreated” control condition.

If the average posttest score is better than the average pretest score, then it makes sense to conclude that the treatment might be responsible for the improvement. Unfortunately, one often cannot conclude this with a high degree of certainty because there may be other explanations for why the posttest scores are better. One category of alternative explanations goes under the name of  history . Other things might have happened between the pretest and the posttest. Perhaps an antidrug program aired on television and many of the students watched it, or perhaps a celebrity died of a drug overdose and many of the students heard about it. Another category of alternative explanations goes under the name of  maturation . Participants might have changed between the pretest and the posttest in ways that they were going to anyway because they are growing and learning. If it were a yearlong program, participants might become less impulsive or better reasoners and this might be responsible for the change.

Another alternative explanation for a change in the dependent variable in a pretest-posttest design is  regression to the mean . This refers to the statistical fact that an individual who scores extremely on a variable on one occasion will tend to score less extremely on the next occasion. For example, a bowler with a long-term average of 150 who suddenly bowls a 220 will almost certainly score lower in the next game. Her score will “regress” toward her mean score of 150. Regression to the mean can be a problem when participants are selected for further study  because  of their extreme scores. Imagine, for example, that only students who scored especially low on a test of fractions are given a special training program and then retested. Regression to the mean all but guarantees that their scores will be higher even if the training program has no effect. A closely related concept—and an extremely important one in psychological research—is  spontaneous remission . This is the tendency for many medical and psychological problems to improve over time without any form of treatment. The common cold is a good example. If one were to measure symptom severity in 100 common cold sufferers today, give them a bowl of chicken soup every day, and then measure their symptom severity again in a week, they would probably be much improved. This does not mean that the chicken soup was responsible for the improvement, however, because they would have been much improved without any treatment at all. The same is true of many psychological problems. A group of severely depressed people today is likely to be less depressed on average in 6 months. In reviewing the results of several studies of treatments for depression, researchers Michael Posternak and Ivan Miller found that participants in waitlist control conditions improved an average of 10 to 15% before they received any treatment at all (Posternak & Miller, 2001) [2] . Thus one must generally be very cautious about inferring causality from pretest-posttest designs.

Does Psychotherapy Work?

Early studies on the effectiveness of psychotherapy tended to use pretest-posttest designs. In a classic 1952 article, researcher Hans Eysenck summarized the results of 24 such studies showing that about two thirds of patients improved between the pretest and the posttest (Eysenck, 1952) [3] . But Eysenck also compared these results with archival data from state hospital and insurance company records showing that similar patients recovered at about the same rate  without  receiving psychotherapy. This parallel suggested to Eysenck that the improvement that patients showed in the pretest-posttest studies might be no more than spontaneous remission. Note that Eysenck did not conclude that psychotherapy was ineffective. He merely concluded that there was no evidence that it was, and he wrote of “the necessity of properly planned and executed experimental studies into this important field” (p. 323). You can read the entire article here: Classics in the History of Psychology .

Fortunately, many other researchers took up Eysenck’s challenge, and by 1980 hundreds of experiments had been conducted in which participants were randomly assigned to treatment and control conditions, and the results were summarized in a classic book by Mary Lee Smith, Gene Glass, and Thomas Miller (Smith, Glass, & Miller, 1980) [4] . They found that overall psychotherapy was quite effective, with about 80% of treatment participants improving more than the average control participant. Subsequent research has focused more on the conditions under which different types of psychotherapy are more or less effective.

Interrupted Time Series Design

A variant of the pretest-posttest design is the  interrupted time-series design . A time series is a set of measurements taken at intervals over a period of time. For example, a manufacturing company might measure its workers’ productivity each week for a year. In an interrupted time series-design, a time series like this one is “interrupted” by a treatment. In one classic example, the treatment was the reduction of the work shifts in a factory from 10 hours to 8 hours (Cook & Campbell, 1979) [5] . Because productivity increased rather quickly after the shortening of the work shifts, and because it remained elevated for many months afterward, the researcher concluded that the shortening of the shifts caused the increase in productivity. Notice that the interrupted time-series design is like a pretest-posttest design in that it includes measurements of the dependent variable both before and after the treatment. It is unlike the pretest-posttest design, however, in that it includes multiple pretest and posttest measurements.

Figure 7.3 shows data from a hypothetical interrupted time-series study. The dependent variable is the number of student absences per week in a research methods course. The treatment is that the instructor begins publicly taking attendance each day so that students know that the instructor is aware of who is present and who is absent. The top panel of  Figure 7.3 shows how the data might look if this treatment worked. There is a consistently high number of absences before the treatment, and there is an immediate and sustained drop in absences after the treatment. The bottom panel of  Figure 7.3 shows how the data might look if this treatment did not work. On average, the number of absences after the treatment is about the same as the number before. This figure also illustrates an advantage of the interrupted time-series design over a simpler pretest-posttest design. If there had been only one measurement of absences before the treatment at Week 7 and one afterward at Week 8, then it would have looked as though the treatment were responsible for the reduction. The multiple measurements both before and after the treatment suggest that the reduction between Weeks 7 and 8 is nothing more than normal week-to-week variation.

Combination Designs

A type of quasi-experimental design that is generally better than either the nonequivalent groups design or the pretest-posttest design is one that combines elements of both. There is a treatment group that is given a pretest, receives a treatment, and then is given a posttest. But at the same time there is a control group that is given a pretest, does  not  receive the treatment, and then is given a posttest. The question, then, is not simply whether participants who receive the treatment improve but whether they improve  more  than participants who do not receive the treatment.

Imagine, for example, that students in one school are given a pretest on their attitudes toward drugs, then are exposed to an antidrug program, and finally are given a posttest. Students in a similar school are given the pretest, not exposed to an antidrug program, and finally are given a posttest. Again, if students in the treatment condition become more negative toward drugs, this change in attitude could be an effect of the treatment, but it could also be a matter of history or maturation. If it really is an effect of the treatment, then students in the treatment condition should become more negative than students in the control condition. But if it is a matter of history (e.g., news of a celebrity drug overdose) or maturation (e.g., improved reasoning), then students in the two conditions would be likely to show similar amounts of change. This type of design does not completely eliminate the possibility of confounding variables, however. Something could occur at one of the schools but not the other (e.g., a student drug overdose), so students at the first school would be affected by it while students at the other school would not.

Finally, if participants in this kind of design are randomly assigned to conditions, it becomes a true experiment rather than a quasi experiment. In fact, it is the kind of experiment that Eysenck called for—and that has now been conducted many times—to demonstrate the effectiveness of psychotherapy.

Key Takeaways

• Quasi-experimental research involves the manipulation of an independent variable without the random assignment of participants to conditions or orders of conditions. Among the important types are nonequivalent groups designs, pretest-posttest, and interrupted time-series designs.
• Quasi-experimental research eliminates the directionality problem because it involves the manipulation of the independent variable. It does not eliminate the problem of confounding variables, however, because it does not involve random assignment to conditions. For these reasons, quasi-experimental research is generally higher in internal validity than correlational studies but lower than true experiments.
• Practice: Imagine that two professors decide to test the effect of giving daily quizzes on student performance in a statistics course. They decide that Professor A will give quizzes but Professor B will not. They will then compare the performance of students in their two sections on a common final exam. List five other variables that might differ between the two sections that could affect the results.
• regression to the mean
• spontaneous remission

Image Descriptions

Figure 7.3 image description: Two line graphs charting the number of absences per week over 14 weeks. The first 7 weeks are without treatment and the last 7 weeks are with treatment. In the first line graph, there are between 4 to 8 absences each week. After the treatment, the absences drop to 0 to 3 each week, which suggests the treatment worked. In the second line graph, there is no noticeable change in the number of absences per week after the treatment, which suggests the treatment did not work. [Return to Figure 7.3]

• Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design & analysis issues in field settings . Boston, MA: Houghton Mifflin. ↵
• Posternak, M. A., & Miller, I. (2001). Untreated short-term course of major depression: A meta-analysis of studies using outcomes from studies using wait-list control groups. Journal of Affective Disorders, 66 , 139–146. ↵
• Eysenck, H. J. (1952). The effects of psychotherapy: An evaluation. Journal of Consulting Psychology, 16 , 319–324. ↵
• Smith, M. L., Glass, G. V., & Miller, T. I. (1980). The benefits of psychotherapy . Baltimore, MD: Johns Hopkins University Press. ↵

A between-subjects design in which participants have not been randomly assigned to conditions.

The dependent variable is measured once before the treatment is implemented and once after it is implemented.

A category of alternative explanations for differences between scores such as events that happened between the pretest and posttest, unrelated to the study.

An alternative explanation that refers to how the participants might have changed between the pretest and posttest in ways that they were going to anyway because they are growing and learning.

The statistical fact that an individual who scores extremely on a variable on one occasion will tend to score less extremely on the next occasion.

The tendency for many medical and psychological problems to improve over time without any form of treatment.

A set of measurements taken at intervals over a period of time that are interrupted by a treatment.

Research Methods in Psychology - 2nd Canadian Edition Copyright © 2015 by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

Experimental vs Quasi-Experimental Design: Which to Choose?

Here’s a table that summarizes the similarities and differences between an experimental and a quasi-experimental study design:

What is a quasi-experimental design?

A quasi-experimental design is a non-randomized study design used to evaluate the effect of an intervention. The intervention can be a training program, a policy change or a medical treatment.

Unlike a true experiment, in a quasi-experimental study the choice of who gets the intervention and who doesn’t is not randomized. Instead, the intervention can be assigned to participants according to their choosing or that of the researcher, or by using any method other than randomness.

Having a control group is not required, but if present, it provides a higher level of evidence for the relationship between the intervention and the outcome.

(for more information, I recommend my other article: Understand Quasi-Experimental Design Through an Example ) .

Examples of quasi-experimental designs include:

• One-Group Posttest Only Design
• Static-Group Comparison Design
• One-Group Pretest-Posttest Design
• Separate-Sample Pretest-Posttest Design

What is an experimental design?

An experimental design is a randomized study design used to evaluate the effect of an intervention. In its simplest form, the participants will be randomly divided into 2 groups:

• A treatment group: where participants receive the new intervention which effect we want to study.
• A control or comparison group: where participants do not receive any intervention at all (or receive some standard intervention).

Randomization ensures that each participant has the same chance of receiving the intervention. Its objective is to equalize the 2 groups, and therefore, any observed difference in the study outcome afterwards will only be attributed to the intervention – i.e. it removes confounding.

(for more information, I recommend my other article: Purpose and Limitations of Random Assignment ).

Examples of experimental designs include:

• Posttest-Only Control Group Design
• Pretest-Posttest Control Group Design
• Solomon Four-Group Design
• Matched Pairs Design
• Randomized Block Design

When to choose an experimental design over a quasi-experimental design?

Although many statistical techniques can be used to deal with confounding in a quasi-experimental study, in practice, randomization is still the best tool we have to study causal relationships.

Another problem with quasi-experiments is the natural progression of the disease or the condition under study — When studying the effect of an intervention over time, one should consider natural changes because these can be mistaken with changes in outcome that are caused by the intervention. Having a well-chosen control group helps dealing with this issue.

So, if losing the element of randomness seems like an unwise step down in the hierarchy of evidence, why would we ever want to do it?

This is what we’re going to discuss next.

When to choose a quasi-experimental design over a true experiment?

The issue with randomness is that it cannot be always achievable.

So here are some cases where using a quasi-experimental design makes more sense than using an experimental one:

• If being in one group is believed to be harmful for the participants , either because the intervention is harmful (ex. randomizing people to smoking), or the intervention has a questionable efficacy, or on the contrary it is believed to be so beneficial that it would be malevolent to put people in the control group (ex. randomizing people to receiving an operation).
• In cases where interventions act on a group of people in a given location , it becomes difficult to adequately randomize subjects (ex. an intervention that reduces pollution in a given area).
• When working with small sample sizes , as randomized controlled trials require a large sample size to account for heterogeneity among subjects (i.e. to evenly distribute confounding variables between the intervention and control groups).

Further reading

• Statistical Software Popularity in 40,582 Research Papers
• Checking the Popularity of 125 Statistical Tests and Models
• Objectives of Epidemiology (With Examples)
• 12 Famous Epidemiologists and Why

Quasi-Experimental Research Design & Analysis (SOC 258B)

UCCS Community

• Current Students
• Faculty Staff
• Alumni & Friends
• Parents & Families

Schools and Colleges

• College of Business
• College of Education
• College of Engineering and Applied Science
• College of Letters, Arts & Sciences
• College of Public Service
• Graduate School
• Helen and Arthur E. Johnson Beth-El College of Nursing and Health Sciences

Quick Links

• Search for Programs & Careers
• Academic Advising
• Ent Center for the Arts
• Kraemer Family Library
• Military and Veteran Affairs
• myUCCS Portal
• Campus Email
• Microsoft 365
• Mountain Lion Connect
• Support Network: Students
• Support Network: Faculty
• Account Help

Effect Size Calculators

Dr. Lee A. Becker

• Content, Part 1
• Content, Part 2
• Research Tools

Statistical Analysis of Quasi-Experimental Designs:

I. apriori selection techniques.

Content, part II

I. Overview

Random assignment is used in experimental designs to help assure that different treatment groups are equivalent prior to treatment. With small n 's randomization is messy, the groups may not be equivalent on some important characteristic.

In general, matching is used when you want to make sure that members of the various groups are equivalent on one or more characteristics. If you are want to make absolutely sure that the treatment groups are equivalent on some attribute you can use matched random assignment.

When you can't randomly assign to conditions you can still use matching techniques to try to equate groups on important characteristics. This set of notes makes the distinction between normative group matching and normative group equivalence. In normative group matching you select an exact match from normative comparison group for each participant in the treatment group. In normative group equivalence you select a comparison group that has approximately equivalent characteristics to the treatment group.

II. Matching in Experimental Designs: Matched Random Assignment

In an experimental design, matched random sampling can be used to equate the groups on one or more characteristics. Whitley (in chapter 8) uses an example of matching on IQ.

The Matching Process

1. Obtain scores on the variable of interest (e.g., IQ) and rank order participants according to that score. The scores in Table 1 have been ranked according to IQ scores. 2. Take the people with the top two scores (Block 1) and randomly assign them the control and experimental conditions. Take the people with next highest scores (Block 2) and randomly assign them to the control and experimental group.  Continue until all participants have been assigned to conditions.  Expand as necessary according to the design of your study.  For example, in a 2 x 3 factorial design, take the people with the top 6 IQ scores (Block 1) and randomly assign them to each of the six cells in the design.   This procedure will assure that each of the treatment and control groups are equivalent on IQ (or whatever characteristic(s) is(are) matched).

IQ Block Condition 133 1 Tx 132 1 Ctl 130 2 Ctl 130 2 Tx 129 3 Ctl 128 3 Tx 128 4 Tx 125 4 Ctl Note: Tx = Treatment group, Ctl = Control Group. Analysis of a Matched Random Assignment Design If the matching variable is related to the dependent variable, (e.g., IQ is related to almost all studies of memory and learning), then you can incorporate the matching variable as a blocking variable in your analysis of variance. That is, in the 2 x 3 example, the first 6 participants can be entered as IQ block #1, the second 6 participants as IQ block #2. This removes the variance due to IQ from the error term, increasing the power of the study. The analysis is treated as a repeated measures design where the measures for each block of participants are considered to be repeated measures. For example, in setting up the data for a two-group design (experimental vs. control) the data would look like this: 1 132 133 27 32 2 130 130 19 17 3 129 128 30 35 4 125 128 . 37 etc          Note: tx  = Treatment Group; ctl = Control Group  The analysis would be run as a repeated measures design with group (control vs. experimental) as a within-subjects factor. If you were interested in analyzing the equivalence of the groups on the IQ score variable you could enter the IQ scores as separate variables.  An analysis of variance of  the IQ scores with treatment group (Treatment vs. Control) as a within-subjects factor should show no mean differences between the two groups. Entering the IQ data would allow you to find the correlation between IQ and performance scores within each treatment group. One of the problems with this type of analysis is that if any score is missing then the entire block is set to missing.  None of the performance data from Block 4 in Table 2 would be included in the analysis because the performance score is missing for the person in the control group. If you had a 6 cells in your design you would loose the data on all 6 people in a block that had only one missing data point. I understand that Dr. Klebe has been writing a new data analysis program to take care of this kind of missing data problem. SPSS Note  The SPSS syntax commands for running the data in Table 2 as a repeated measures analysis of variance are shown in Table 3.  The SPSS syntax commands for running the data in Table 2 as a paired t test are shown in Table 4.  GLM ps_ctl ps_tx /WSFACTOR = group 2  /EMMEANS = TABLES(group) /PRINT = DESCRIPTIVE /WSDESIGN = group . T-TEST PAIRS= ps_ctl WITH ps_tx (PAIRED). III. Matching in Quasi-Experimental Designs: Normative Group Matching Suppose that you have a quasi-experiment where you want to compare an experimental group (e.g., people who have suffered mild head injury) with a sample from a normative population. Suppose that there are several hundred people in the normative population. One strategy is to randomly select the same number of people from the normative population as you have in your experimental group. If the demographic characteristics of the normative group approximate those of your experimental group, then this process may be appropriate. But, what if the normative group contains equal numbers of males and females ranging in age from 6 to 102, and people in your experimental condition are all males ranging in age from 18 to 35? Then it is unlikely that the demographic characteristics of the people sampled from the normative group will match those of your experimental group. For that reason, simple random selection is rarely appropriate when sampling from a normative population. The Normative Group Matching Procedure Determine the relevant characteristics (e.g., age, gender, SES, etc.) of each person in your experimental group. E.g., Exp person #1 is a 27 year-old male. Then randomly select one of the 27 year-old males from the normative population as a match for Exp person #1. Exp person #2 is a 35 year-old male, then randomly select one of the 35 year-old males as a match for Exp person #2. If you have done randomize normative group matching then the matching variable should be used as a blocking factor in the ANOVA. If you have a limited number of people in the normative group then you can do caliper matching . In caliper matching you select the matching person based a range of scores, for example, you can caliper match within a range of 3 years. Exp person #1 would be randomly selected from males whose age ranged from 26 to 27 years. If you used a five year caliper for age then for exp person #1 you randomly select a males from those whose age ranged from 25 to 29 years old. You would want a narrower age caliper for children and adolescents than for adults. This procedure becomes very difficult to accomplish when you try to start matching on more than one variable. Think of the problems of finding exact matches when several variables are used, e.g., an exact match for a 27-year old, white female with an IQ score of 103 and 5 children. Analysis of a Normative Group Matching Design The analysis is the same as for a matched random assignment design. If the matching variable is related to the dependent variable, then you can incorporate the matching variable as a blocking variable in your analysis of variance. III. Matching in Quasi-Experimental Designs: Normative Group Equivalence Because of the problems in selecting people in a normative group matching design and the potential problems with the data analysis of that design, you may want to make the normative comparison group equivalent on selected demographic characteristics. You might want the same proportion of males and females, and the mean age (and SD) of the normative group should be the same as those in the experimental group. If the ages of the people in the experimental group ranged from 18 to 35, then your normative group might contain an equal number of participants randomly selected from those in the age range from 18 to 35 in the normative population. Analysis of a Normative Group Equivalence Design In the case of normative group equivalence there is no special ANOVA procedure as there is in Normative Group Matching. In general, demographic characteristics themselves rarely predict the d.v., so you haven’t lost anything by using the group equivalence method. A Semantic Caution The term "matching" implies a one-to-one matching and it implies that you have incorporated that matched variable into your ANOVA design. Please don’t use the term "matching" when you mean mere "equivalence." Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click here . Loading metrics Open Access Peer-reviewed Research Article Effect of an educational intervention based on self-efficacy theory and health literacy skills on preventive behaviors of urinary tract infection in pregnant women: A quasi-experimental study Roles Writing – original draft, Writing – review & editing Affiliations Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran, Faculty of Health, Department of Health, Safety, and Environment, Mashhad University of Medical Sciences, Mashhad, Iran Roles Data curation Affiliation Faculty of Health, Department of Health Education and Health Promotion, Mashhad University of Medical Sciences, Mashhad, Iran Roles Writing – review & editing Affiliation Department of Health Education and Health Promotion, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran Roles Methodology Affiliations Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran, Faculty of Health Sciences, Department of Epidemiology and Biostatistics, Mashhad University of Medical Sciences, Mashhad, Iran * E-mail: [email protected] Affiliations Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran, Department of Health Education and Health Promotion, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran Seyedeh Belin Tavakoly Sany,  Vajieh Eslami,  Elaheh lael-Monfared,  Vahid Ghavami,  Nooshin Peyman Published: August 13, 2024 https://doi.org/10.1371/journal.pone.0306558 Reader Comments The impact of self-efficacy and health literacy skills on pregnant women’s adherence to urinary tract infection (UTI) preventive behaviors is inadequately investigated. Thus, the present study explored whether an educational intervention based on self-efficacy and health literacy skills managed to improve UTI preventive behaviors among pregnant women. A quasi-experimental study was conducted from January to July 2021 among pregnant women residing in Mashhad, Iran. To this aim, 110 pregnant women at a gestational age of 12–18 weeks were randomly assigned to a control (n = 55) and an intervention group (n = 55) and completed all questionnaires during the intervention and the 3-month follow-up. The intervention group received the full training program, comprising six 2-hourly training sessions. Most women were from low-income families (69.1%), were housewives (74.5%) with high school education or lower (63.6%). The theory-based intervention had a significant effect (P < 0·05) on UTI preventive behavior outcomes (i.e., clothing habits, nutrition, urination, health, and sexual behaviors) in the intervention group compared with the control group after intervention, and in their variation from baseline to follow-up in all scores. Conclusions An educational intervention based on health literacy skills and self-efficacy could be an effective theory-based intervention to improve UTI preventive behaviors and reduce recurrent UTI and complications. Citation: Tavakoly Sany SB, Eslami V, lael-Monfared E, Ghavami V, Peyman N (2024) Effect of an educational intervention based on self-efficacy theory and health literacy skills on preventive behaviors of urinary tract infection in pregnant women: A quasi-experimental study. PLoS ONE 19(8): e0306558. https://doi.org/10.1371/journal.pone.0306558 Editor: Kahsu Gebrekidan, University of Oulu: Oulun Yliopisto, FINLAND Received: December 31, 2023; Accepted: June 18, 2024; Published: August 13, 2024 Copyright: © 2024 Tavakoly Sany et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: We confirm at this time Our submission contains all raw data required to replicate the results of our study. All relevant data are within the manuscript and its Supporting Information files. Funding: This research was funded by the Mashhad University of Medical Sciences (Project number: 980582). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Abbreviations: UTI, urinary tract infection; WHO, World Health Organization 1. Background Pregnancy is a natural physiological process in a woman’s life, accompanied by physiological and psychological changes. However, maternal comorbidities or unexpected diseases can complicate pregnancy and have adverse effects. Thus, a mother’s health before and during childbirth is very important for children’s health [ 1 , 2 ]. Urinary tract infection (UTI) is a common clinical disease marked by a continuous and active proliferation of bacteria inside the urinary tract [ 3 ], and involves the urinary tract, bladder and kidney infections. UTI may be symptomatic or asymptomatic [ 4 ] with the latter being of a particular importance due to the absence of any symptoms. Its complications account for about 150 million mortalities annually worldwide [ 5 ]. UTI is a common bacterial infection and the second main complication of pregnancy, after anemia. Anatomical and physiological changes of the urinary tracts during pregnancy increase the prevalence of UTI [ 6 ]. The prevalence of asymptomatic bacteriuria in the world is 2–15% [ 7 ]. In Iran, the prevalence of UTI in pregnant women is 8.7% [ 8 ]. In developing countries, pregnant women have a higher rate of UTI than counterparts in developed countries [ 9 ]. In a meta-analysis, the overall prevalence of UTI during pregnancy in Iran was estimated at 13%. In different parts of Iran, this rate varied greatly. For example, in Tehran and Arak, it is 2–13%, in Hamadan 10%, and in Torbat Heydarieh, it is reported to be 10% [ 10 ]. UTI is among the most widespread and costly medical complications in pregnancy which accounts for 10% of all hospitalizations during pregnancy [ 11 ]. As the existing literature shows, UTI in pregnant women begins at the 6 th week of pregnancy and reaches its peak in the 22 nd - 24 th week [ 2 , 8 ]. Besides the high cost of treatment and hospitalization, UTI during pregnancy has many lifelong maternal and fetal complications, including pyelonephritis, preeclampsia, shock, septicemia, anemia, and endometritis. Fetal complications of UTI during pregnancy include birth weight loss, premature birth, respiratory failure, fetal death, mental retardation, and lower intelligence quotient (IQ) [ 4 , 9 ]. The report of the World Health Organization (WHO) on premature birth shows that every year a million infants die due to premature birth [ 12 ] and that the probability of preeclampsia in pregnant women with UTI is 1.22 times as high as pregnant women without UTI [ 13 ]. Antibiotics are essential to fight UTIs during pregnancy [ 12 ], but an excessive use is a global health threat as it can develop antimicrobial resistance and increase the risk of spontaneous abortion and birth defects [ 14 , 15 ]. The consumption of safe antibiotics during pregnancy is limited due to their teratogenic potential [ 16 ]. In light of the aforementioned issues, several measures can be taken to prevent UTI during pregnancy, such as adherence to healthy behaviors in sexual activity, the clothing style, eating habits, urinary habits and cleaning, which are all among the predisposing factors for UTI [ 17 , 18 ]. Inadequate knowledge and skills can decrease the motivation to adopt preventive behaviors and can hinder a full prevention [ 17 ]. Health literacy skills and self-efficacy are effective factors to prevent infectious diseases [ 19 – 21 ]. In the related literature, health literacy is “a set of reading, listening, analysis and decision-making skills, and the ability to apply these skills in health-related conditions” [ 22 , 23 ]. American Center for Health Care Strategies reported that people with higher health literacy have more chances of using spoken and written information provided by professionals; therefore, they have a better state of health. Health literacy skills improve the acquisition of knowledge about health issues, correct decisions about health, and benefits of healthcare services [ 24 , 25 ]. Problems related to lifestyle changes require a high level of self-confidence. Achieving high self-efficacy, and improving self-efficacy and health literacy is possible through active education [ 26 ]. Choosing a behavior change model for health education is the first step to a planning process [ 18 , 27 ]. A prominent educational theory used to predict and describe behavior is the self-efficacy theory, commonly used in behavior changing programs[ 28 ]. According to Bandura, there are four main sources of self-efficacy including mastery experiences, vicarious experiences, verbal persuasion, and physiological and affective states” [ 29 ]. Self-efficacy is a major prerequisite for behavior change [ 30 ]. Individuals with inadequate self-efficacy are less likely to make efforts to show a new healthy behavior or to change the former unhealthy behavior [ 31 ]. There is research evidence that self-efficacy is an important psychological construct directly and indirectly affecting disease-controlling health behaviors. Self-efficacy can transform knowledge and information related to health promotion and educational interventions in behavioral performance [ 32 ]. Health literacy has been included as a predictor of self-efficacy [ 32 , 33 ]. Although a body of research in Iran shows that women’s awareness of UTI prevention is at a satisfactory level, the prevalence of UTI in pregnant women is still increasing [ 8 , 17 , 34 ]. It seems that only raising the level of knowledge cannot lead to the prevention of UTI [ 35 – 37 ], and there is a need for recognition of other factors affecting on UTI preventive behaviors [ 38 ]. Research evidence shows that self-efficacy and health literacy skills are effective in improving health behaviors. Yet, the relationship between UTI preventive behaviors and health literacy and self-efficacy in pregnant women has not been investigated. Considering the high prevalence of UTI during pregnancy and the serious risks that threaten the mother’s and fetus’ health, the present study aimed to investigate the effect of a health education intervention based on the self-efficacy theory and health literacy skills on pregnant women’s UTI preventive behaviors. The present findings can help decision-makers develop a comprehensive educational program to promote UTI preventive behaviors. Preventive behaviors against UTI helps reduce the excessive use of antibiotics, especially in pregnant women. 2. Materials and methods 2.1. participants and sampling. λ : ratio of sample size in group 2 to group 1 v : number of measures before intervention w : number of measures after intervention p t : correlation coefficient of repeated measures Δ plan : standardized expected effect size Due to the lack of data required to estimate the sample size in the existing literature (e.g., the absence of standard deviation of scores after the intervention in the intervention group), the information about control group in the study by Tehrani et al. [ 26 ] was used. The equality of variance of two groups was assumed. Cohen’s standard effect size was 0.56. The first type error was 0.05 and the test power was 80%. λ , v, w and p t. were, respectively, 1, 1, 2 and 0. 5. The estimated sample size, with an attrition rate of 10%, for each group, was 55. The participants were randomly assigned to the intervention and control groups. In the present study, the data collection was done based on a test of functional health literacy in adults [ 28 , 30 ], and the general self-efficacy questionnaire [ 39 ]. Also, a researcher-made questionnaire was developed to measure UTI preventive behaviors. This questionnaire included demographic information (occupation, age, education, husband’s education and occupation, body mass index (BMI), vomiting during pregnancy, and income) as well as the five domains of UTI prevention behaviors. The questionnaires were completed before, immediately after and three months after the educational intervention in the health centers. All participants were informed about the purpose of study and their demographic information was recorded. Having signed an informed letter of consent, the participants completed the questionnaire of UTI prevention behaviors, test of functional health literacy in adults (TOFHLA) and Schwarzer’s self-efficacy scale. General self-efficacy questionnaire (GSE). Schwarzer’s general self-efficacy questionnaire was used to measure the participants’ self-efficacy. This scale contained 17 questions rated on a 4-point Likert scale ranging from strongly disagree to strongly agree. It was scored from 17 to 85, and a high score showed stronger self-efficacy. Three aspects of behavior, including the desire to initiate the behavior, resistance to barriers, and efforts to complete the task were measured using this test (e.g., “I am a self-reliant person.”, and “I avoid facing difficulties.”) ( S1 Table ). The reliability of scale was estimated at 0.84 in the study by Woodruff and Kashman, and 0.83 in the study by Asgharanjad, and Ahmadi Qutb Al-Dini [ 27 , 39 , 40 ]. Test of functional health literacy in adults (TOFHLA). This questionnaire consisted of two sections, calculations and reading comprehension. The calculations section assessed one’s ability to understand the doctor’s and health educators’ advice. This section required certain calculations, and the score could range between 0 and 50. The reading comprehension section assessed one’s ability to read and comprehend three passages entitled as instructions on preparing for imaging of the upper gastrointestinal tract, the patient responsibilities and rights about the standard hospital consent form and insurance forms. This score ranged from 0 to 50. Thus, the overall health literacy score obtained from these two sections could range between 0 and 100. There were three levels of interpretation of scores: insufficient (0–59), borderline (60–74) and sufficient (75–100) [ 31 ]. The validity and reliability of this questionnaire in Iran were measured by Raisi et al. The reliability was estimated at 0.79 for the calculations section and 0.88 for the reading comprehension section. Its content validity ratio (CVR) was higher than 0.56. and the content validity index (CVI) was estimated at 0.79 [ 28 – 30 ]. Urinary tract infection preventive behaviors questionnaire. In this study, a researcher-made questionnaire was used to measure UTI preventive behaviors in pregnant women. This questionnaire includes demographic information and five dimensions of UTI preventive behaviors, including 25 questions on clothing style (4 questions), eating habits (6 questions), urinary habits (2 questions), cleanliness (7 questions) and sexual behavioral habits (6 questions) ( S2 Table ). In this instrument, the questions were rated based on a Likert scale ranging from never (0) to always (4), with a minimum score of 25 and a maximum score of 100. To check the content validity of the researcher-made questionnaire, it was provided to six eminent professors of health promotion, two distinguished professors of reproductive health, two gynecologists and five midwifery experts. Thus, the content validity (CVR) was measured and substantiated. For the overall instrument, the CVR was estimated at 0.94. Having made the suggested revisions, the content validity index (CVI) for all scales was increased to 0.94. To check internal consistency, Cronbach’s alpha test was used, and the estimated value was 0.72. Also, to check the reliability, a test-retest method was used for 20 pregnant mothers at a time interval of two weeks, based on which the intra-cluster correlation coefficient (ICC) was estimated at 0.97, indicating an acceptable reliability [ 41 ]. 2.2. Intervention The present quasi-experimental study involved two groups, an intervention, and a control. The intervention was made from January 2021 to July 2021 based on a consort checklist and the Template for Intervention Description and Replication checklist (TIDieR) ( Table 1 ) [ 42 ]. Four health centers were selected randomly from a list of centers, and were assigned to the intervention group (n = 2) and the control (n = 2). Then, a list was made of pregnant women’s names based on their demographic information and health history, and a number was assigned to each using a table of random numbers. Two hundred pregnant women were randomly selected, of whom 84 women were not included in the intervention because they did not meet the inclusion criteria. Six women failed to attend the training or follow-up because of travelling, COVID-19 lock-down, and work-related problems. Finally, 110 women completed all stages of study (before intervention, immediately after intervention, and three months after intervention) ( Fig 1 ). PPT PowerPoint slide PNG larger image TIFF original image https://doi.org/10.1371/journal.pone.0306558.g001 https://doi.org/10.1371/journal.pone.0306558.t001 The educational intervention was conducted for the intervention group. All women underwent a training program of six two-hour sessions every 7 days. From two centers of the control group, 55 pregnant women with similar conditions were randomly selected and considered as the control group, and the educational content was provided to them after the completion of the intervention. Due to the COVID-19 pandemic, the sensitivity of pregnant mothers’ condition and the health protocols against face-to-face group meetings, four training sessions were held face to face, and the remaining sessions were held online on WhatsApp as the mothers requested. In this study, different oral and combined methods (e.g., lectures with Q&As, brainstorming, group discussions, poster presentation and pamphlets) were used along with online sources (e.g., telephone and social networks to share videos, photos and group discussions in real-time class held in audio-only mode) ( Table 1 ). The intervention training program was designed based on Bandura’s self-efficacy theory [ 27 ] and health literacy skills (spoken communication, promotion and written communication, empowerment, improvement of support systems) [ 40 ] ( Table 2 ). https://doi.org/10.1371/journal.pone.0306558.t002 In this educational program, according to the participants’ age and literacy level and the objectives of the educational program, there were three cognitive, attitudinal, and functional domains to address, for which visual and auditory media were used such as educational slides, overhead projectors, whiteboards, and pamphlets in this program. Face-to-face training and phone-mediated training were used along with video, photo and voice records in non-face-to-face training. Trainings were conducted by a health education specialist and a gynecologist. During the study, the control group did not receive any special training from the researcher, and after the completion of the intervention, the training was provided as e-learning to the control group. Questionnaires in both groups were completed once before the intervention, and twice more immediately after and three months after the educational intervention. This was done face to face in the first session and online via sharing the questionnaire link in the group to complete. 2.3. Data statistical analysis Having collected the data, to analyze the descriptive data, the questionnaires were coded and punched into SPSS21. After a careful checking and ensuring of the accuracy of data entry, descriptive statistics of the central tendency and variability indices, such as the mean and standard deviation of values related to the interval variables, and the distribution of frequency and percentage of non-parametric variables were used. To check the normality of distribution of interval variables in the treatment and control groups, Kolmogorov-Smirnov test was used. As the results showed, appropriate parametric tests were used for interval variables and appropriate non-parametric tests were used for non-interval variables. To test the relationship between interval variables, Spearman and Pearson correlation coefficients were used according to the abnormal distribution of data. Mann-Whitney U-test, and Kruskal-Wallis tests were used to test the relationship between interval and non-interval variables according to the number of classes of qualitative variables. Chi-square test was used to explore the relationship between non-interval variables. To compare the two groups before, immediately after and 3 months after intervention in terms of interval variables, repeated measure analysis of variance was used. Friedman’s test was used for non-interval variables. The significance level in all tests was 0.05 and SPSS 21 was used to describe and analyze the data. Ethics approval and consent to participate. The study protocol was approved by the Ethics Committee of Mashhad University of Medical Sciences (#IR.MUMS.REC.1398.268) after obtaining the required permit for the research. The participants provided a written informed consent and were assured of confidentiality of data. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee with the 1964 Helsinki declaration. Before intervention, there were no significant differences (P>0.05) between the intervention and control groups in terms of demographic characteristics (i.e., age, gestational age, education, income, employment status, BMI, history of pre-pregnancy UTI, and vomiting during pregnancy). In this sense, the variables were homogeneous in both groups. The mean (±SD) of age, gestational age, and BMI were 24.80 (±4.92), 13.69 (± 3.82) and 24.93 (±3.18), respectively. Most eligible women were housekeepers (74.5%), low-income families (69.1%) with high school diploma or below (63.6%) ( Table 3 ). https://doi.org/10.1371/journal.pone.0306558.t003 At the baseline, all UTI preventive behavioral constructs, total preventive behaviors, and self-efficacy were homogeneous in both groups. The results related to UTI preventive behaviors showed a significant improvement (P < 0.05) in all constructs (clothing habits, nutrition, urination, health, and sexual behaviors) in the intervention group at the follow-up, and in all scores changing from baseline to the follow-up. The results of testing self-efficacy showed a significant change (P < 0.05) in the intervention group compared to the control group in the follow-up, and in changes from the baseline to follow-up ( Table 4 ). https://doi.org/10.1371/journal.pone.0306558.t004 The mean health literacy score immediately after the intervention and three months later was significantly different in the intervention group. The mean score of health literacy was significantly different within the intervention group (p < 0.001). There was no significant difference (P > 0.05) in the change of UTI preventive behavior constructs, total preventive behaviors, self-efficacy, and self-efficacy in the control group at the follow-up ( Table 4 ). The results presented in S3 Table showed that the incidence of UTI three months after the intervention in the control group was 25.4%. The control group significantly had more cases with UTI than the intervention group. In this section, a generalized estimating equation ( GEE ) model was used to simultaneously measure the effect of intervention, time, self-efficacy, and health literacy on UTI preventive behaviors. The results of the GEE model were in line with the bivariate analysis that showed significant interactions between groups and time. S4 and S5 Tables showed the impact of intervention based on health literacy and self-efficacy on improving UTI preventive behaviors in different groups and times. Changes in UTI preventive behavior score within the intervention group were significantly higher than the control (P = 0 <0.001), and UTI preventive behaviors were increased considerably across time in the baseline through follow-up among participants in the intervention group compared with the control (P = 0 <0.001). As the results showed, changes in self-efficacy (p = 0.043) and health literacy (p = 0.042) were significantly associated with UTI preventive behaviors. 4. Discussion Due to the prevalence of UTI in pregnant women, UTI is considered a major concern in public health public health [ 41 , 43 , 44 ]. The present finding suggests that conducting an educational intervention based on the self-efficacy theory and health literacy skills among pregnant women is an effective intervention to control and prevent UTI because women in the intervention group represented a lower risk of UTI and better preventive behaviors compared with participants in the control group. The present findings showed a significant increase in the level of preventive behaviors in the intervention group. Before the intervention, there was no significant difference between the intervention and control groups. However, after the educational intervention, this difference was statistically significant. The present study showed that the educational intervention based self-efficacy and health literacy skills and the use of educational strategies and programs such as the mastery of alternative behavior and verbal persuasion, educational methods such as goal-setting and role-play were effective in improving preventive behaviors. As the present findings showed, the use of the self-efficacy theory can be effective in improving perceived self-efficacy in individuals. It seems that women with adequate self-efficacy and health literacy may well find and use health information and engage in their care [ 18 , 45 ]. In this study, pregnant women in the intervention group showed a significant change in the mean score of self-efficacy after the intervention. All women learned how to break complex tasks into smaller and simpler activities and set realistic goals to modify their action and commitment to conduct UTI preventive behaviors despite conflicting conditions. Likewise, the present researchers tried to improve mother’s self-confidence and self-monitoring to perform certain behaviors. The existing literature shows that individuals with low self-efficacy use fewer resources of health information and health literacy to improve their health or change the habitual behaviors [ 33 ]. The findings reported by Osborn et al., 2011 are consistent with the present findings, as individuals with higher perceived self-efficacy had a better understanding of their health state and used health information and health literacy to improve their health and show self-care behaviors [ 34 , 46 ]. The results of the present study are in line with a body of research by Hejazi et al. [ 47 ], Abdullahi et al. [ 48 ], which showed a significant effect of self-efficacy on adopting, initiating, and maintaining healthy behavior. They found that self-efficacy acted as a moderator to link healthy behaviors with motivation and knowledge [ 49 , 50 ]. The results of the present study showed statistically significant differences in the change of health literacy skills in the intervention group at 3-months follow-up, and in changes from baseline to follow-up in all scores. Health literacy is the main skill to influence one’s ability to use health information, make well-informed decisions, and maintain good health [ 38 , 40 , 42 , 43 ]. Before the intervention, women had difficulty finding and comprehending health information and healthcare services to make well-health decisions. Likewise, a significant improvement in the health literacy score was found in the intervention group in post-intervention and follow-up. This could be due to the improved women’s willingness and ability to involve in behaviors and care that improve their health. In the present study, a supportive and reliable environment was created to address health information and measures that contribute to a higher stage of well-health decisions and commitments among pregnant women to modify their UTI preventive behaviors [ 4 , 6 ]. Therefore, it is essential to promote health literacy skills in community, as high health literacy is associated with better health outcomes among patients. Therefore, it is necessary to plan and implement model-based educational programs based on the self-efficacy theory and health literacy skills to increase pregnant women’s self-efficacy and health literacy. The results of the present study are in line with a body of research. In a descriptive study conducted on 140 pregnant women in Zahedan based on the Health Belief Model (HBM), Rahimi et al. showed that self-efficacy was the strongest predictor of preventive behaviors against UTI. It seems that the reasons for the greater effect of self-efficacy are women’s self-confidence and awareness of the effect of simple behaviors and measures to control UTI [ 45 , 46 ]. In a quasi-experimental study conducted on 60 mothers to children under 6 years of age, Hashemiparast et al. showed the mean self-efficacy score was increased in the intervention group after the intervention. In this study, self-efficacy implied confidence in one’s ability to perform UTI preventive behaviors [ 47 ]. Eshghi Mutlaq et al. (2016) found that their educational intervention had a significant effect on improving self-care behaviors in mothers with prediabetes during pregnancy, who felt more self-efficacious and capable of understanding their positive state of health. They also showed showed diabetes self-care behaviors in their daily life [ 48 ]. In line with the present study, Ebrahimipour et al. (1994) conducted some research on the effect of an educational intervention based on the self-efficacy theory on the adoption of HIV-AIDS preventive behaviors in high-risk women. This study showed that the educational intervention based on self-efficacy strategies could significantly increase the adoption of self-care behaviors in the intervention group (P<0.001) [ 49 ]. Ha et al. also showed that the educational intervention and the use of educational strategies and programs such as mastery of alternative behavior and verbal persuasion, educational methods such as goal-setting and role-play were effective in improving self-care [ 45 ]. As the present study showed, the use of this theory proved effective in improving perceived self-efficacy in individuals. The findings emphasized the importance of self-efficacy in preventive behaviors as a suitable educational alternative for UTI self-care and prevention in pregnant women. Therefore, it is necessary to plan and implement model-based educational programs to increase pregnant women’s self-efficacy. It seems that women with adequate self-efficacy and health literacy may well find and use health information and well engage in their care [ 18 , 46 , 50 ]. Limited studies exist, investigating the role of self-efficacy and health literacy pandemic conditions influencing awareness and health behaviors among pregnant women. Therefore, further studies need to be conducted on enhancing women’s capability to improve health prevention behaviors toward the different diseases, and focusing on health literacy skills and self-efficacy strategies cause persistent and long-term health behaviors. The strengths of our findings lie in determining the role of self-efficacy and health literacy using valid instrument among the pregnant women as the groups at risk. Our findings highlighted self-efficacy and health literacy skills as the main modifiable determinants to control and manage unborn child’s health and mother’s health because an adequate level of health literacy and self-efficacy improved individual’s healthy behaviors and health outcomes. Future research on intervention-based health literacy and self-efficacy skills will continue in Iran because this type of training for individuals empowers communities to engage in their self-care, improve the healthy behavior, and can increase valuable health outcome in strengthening healthcare delivery. Therefore, it would be worthwhile to study the modifying health literacy and self-efficacy as a long-term measure. In this study, the data collection instrument was self-reporting, which can cause problems such as recall and distraction. Due to the COVID-19 pandemic, the questionnaires were not filled face to face. Instead, the questionnaire hyperlink was shared with the pregnant women to fill out the questionnaires in their convenience. In this type of questionnaire completion, errors occur more often, and the researcher has no control over the respondents, which reduces the number of visits by pregnant women to the health centers, as well as attendance to face-to-face training sessions. Finally, the results of the present study showed that the educational intervention based on the self-efficacy theory and health literacy skills can be effective in improving UTI preventive behaviors. The promotion of UTI preventive behaviors in pregnant women after the intervention showed that holding training sessions based on the self-efficacy theory and health literacy was useful. Such a training can improve preventive behaviors. The results of this research can be used to increase UTI preventive behaviors in all sex and age groups and can reduce recurrent UTI complications. Also, the present findings can help health system managers formulate intervention programs specifically for employees to prevent office infection and increase health indices while maintaining the health of the mother and the fetus. Development of educational programs by managers for health workers aiming to raise the awareness of women visiting health centers can reduce economic and psychological costs imposed on society. Supporting information S1 table. scherer general self-efficacy questionnaire.. https://doi.org/10.1371/journal.pone.0306558.s001 S2 Table. Distribution of urinary tract infection prevention behaviors. https://doi.org/10.1371/journal.pone.0306558.s002 S3 Table. UTI ratio in control and intervention groups at follow-up. https://doi.org/10.1371/journal.pone.0306558.s003 S4 Table. Effectiveness of the intervention on improving the UTI preventive behaviors via self-efficacy in different group and time period. https://doi.org/10.1371/journal.pone.0306558.s004 S5 Table. Effectiveness of the intervention on improving the UTI preventive behaviors via Health literacy in different group and time period. https://doi.org/10.1371/journal.pone.0306558.s005 Acknowledgments The authors wish to express their gratitude towards the vice president of research in Mashhad University of Medical Sciences, the chiefs and staffs of the health centers and the esteemed participants. View Article Google Scholar PubMed/NCBI 13. Ratzan S. and Parker R., Health literacy. National library of medicine current bibliographies in medicine. Bethesda: National Institutes of Health, US Department of Health and Human Services, 2000. Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices. Book Title: Graduate research methods in social work Subtitle: A project-based approach Authors: Matthew DeCarlo; Cory Cummings; and Kate Agnelli Book Description: Our textbook guides graduate social work students step by step through the research process from conceptualization to dissemination. We center cultural humility, information literacy, pragmatism, and ethics and values as core components of social work research. Book Information Graduate research methods in social work Copyright © 2021 by Matthew DeCarlo, Cory Cummings, Kate Agnelli is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted. Social work Skip to main content Skip to FDA Search Skip to in this section menu Skip to footer links The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you're on a federal government site. The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely. U.S. Food and Drug Administration   Search   Menu Science and Research | Drugs How computational analysis of a 3D mucociliary clearance model can help predict drug uptake and lead to more generic nasal drug products In 2020, CDER’s Office of Generic Drugs (OGD) and several partner researchers quantified the effect of drug solubility and partition coefficient on the dissolution and subsequent uptake of drugs in a realistic nasal cavity model. The results provided insight into the possible effects of formulation variables such as solubility, partition coefficient, and particle size on systemic exposure inside the nasal cavity. Complex locally-acting generic drug products, such as some orally inhaled or nasal drug products, can be more challenging for generic drug developers to copy, often leading to a lack of generic competition even after patents and exclusivities no longer block generic drug approval. Accurate and realistic predictions from computer simulations about deposition and absorption of nasally inhaled drugs can provide a deeper understanding of complex fluid-particle dynamics in the nasal cavity which may help OGD clarify regulatory expectations early in the drug development process and during application assessment. While most nasal drug products target local drug delivery to nasal tissues, there is an interest within industry for developing products that target blood-brain barrier (BBB) for rapid delivery to the central nervous system (Pardeshi et al., 2013). As nasal drug products with BBB targeting enter the market, there will be a need for understanding how to assess bioequivalence for proposed generic versions of these products. Currently available models for predicting drug deposition and absorption of nasal drug products such as the model developed by Rygg et al. (2016) have shown promise but are incapable of accurately predicting local absorption. To facilitate accurate local nasal deposition predictions, a three-dimensional (3D) model using computational fluid dynamics (CFD) was developed for this study that includes a paired mucus layer model. Determining How to Enhance Drug Uptake through Clearance, Dissolution, and Absorption The noninvasive nature of intranasal drug administration makes it a widely adopted technique for local and systemic delivery of therapeutic agents. The nasal mucosa, unlike other mucosae, is easily accessible. Intranasal application circumvents the issues of gastrointestinal degradation and hepatic first pass metabolism of the drug (Bitter et al., 2011). However, drugs intended to hit a target site within the nasal cavity are also trapped in the highly viscous gel layer reducing the efficacy of the drug. Soluble drugs, however, dissolve in the mucus layer, diffuse across the gel and sol layers, and are eventually absorbed by the richly vascularized nasal epithelium. This enables a drug to enter the systemic regions through the blood stream without losing efficacy. A computational 3D mucociliary clearance (MCC) model was developed for this study, with the goal of realistically quantifying the effects of drug solubility and partition coefficient on the dissolution and subsequent uptake of drugs in the nasal cavity to achieve a desired therapeutic effect. The results of the study provide insight into the effects of formulation variables like solubility and partition coefficient on systemic exposure inside the nasal cavity. The goal is to eventually enhance drug uptake through a combination of clearance, dissolution, and absorption, as well as drug targeting, to maximize drug uptake. The open-source CFD flow solver toolbox, OpenFOAM version 1706 ( www.openfoam.com ), was employed for the development of the computer simulation model. As part of the design, a novel 3D meshing technique allows the model to smoothly capture the relatively large flow domain as well as the micron-size mucus layer. This efficient meshing strategy drastically reduces the overall meshing time from hours to a matter of minutes. Segmental concentration contours as a visualization tool explain regional trends in cumulative drug uptake. The effects of pharmacokinetic characteristics of hypothetical drugs on the dissolution, subsequent uptake, and clearance were analyzed. A method to impose boundary-driven flow velocity that mimics the beating of the cilia was introduced. Rather than selecting specific drugs, the model was supplied with ranges of parameters that provide a general understanding of how drugs are absorbed in the nasal cavity. Several drug specific parameters, such as solubility, partition coefficient, and particle size, were considered. The effects of particle distribution on MCC and uptake were simulated as well. Velocity Profiles To validate the accuracy of the velocity field obtained from the proposed 3D mucus model, inert particle clearance data from the nose obtained from simulations was compared with in vivo data reported by Shah et al. (2015). In this in vivo study, radiolabel tracers were injected as nasal sprays, which deposited on the walls of the nasal cavities. After 15 minutes, they found that 60% of the tracers were removed, consistent with other studies (Naclerio et al., 2003; Bacon et al., 2010) that reported a similar removal percentage. Since the administered radiolabel tracers were not absorbed, they concluded that this removal must be from MCC, confirming deposition to the ciliated posterior regions beyond the nasal valve. The initial positions of the tracers reported by Shah et al. (2015) were used to define the initial positions of inert particles for simulations conducted in this study. About 60% of the inert particles were injected from the posterior, ciliated region and 40% were introduced from the non-ciliated NV region. A transient simulation tracking the trajectory of these inert particles was then run for a duration of 6 hours, consistent with the in vivo study. The mass remaining in the computational domain was calculated and compared with the value reported in the in vivo study. The computational values compare well with those observed in the experimental study. Based on this evidence, it can be concluded that the velocity field obtained from the CFD model is expected to accurately represent MCC, including the effects of local changes in the velocity profile required to maintain a constant ASL thickness. Velocity magnitude is computed at several slices by dividing each slice into the major nasal segments (inferior meatus (IM), inferior turbinate (IT), middle meatus (MM), middle turbinate (MT), olfactory region (OLF)). Each segment is then divided into several evenly spaced subdivisions and the average velocity is calculated at each segment of the slice (see Figure 1). Figure 1: Mucus layer is divided into several slices. Left - Each slice is subdivided into a number of segments: inferior meatus (IM), inferior turbinate (IT), middle meatus (MM), middle turbinate (MT) & olfactory region (OLF). For visualization of the simulation results, the nasal cavity model was divided into three main regions: the anterior nasal vestibule (NV) which is unciliated, the middle passages (MPs), and the posterior nasopharynx. The MPs are further subdivided into different anatomical regions viz. IM, IT, MM, MT, OLF, and the septum (see Figure 2). Figure 2: Middle passages (MP) divided into seven segmental regions The drug concentration and uptake in the subsequent sections were analyzed at a slice taken at approximately 50 mm from the nostril (Figure 3). This slice has a large cross section area which make it relatively easier to study trends in the concentration and uptake profiles across the thickness of the mucus layer. In addition, this slice is positioned in such a way that all seven sections of the MP detailed above are encapsulated. Its location closer to the nasopharynx helps in accounting for particles that might escape or get swallowed which otherwise would be difficult to estimate on a slice closer to NV. Figure 3: Position of the slice (50mm from the nostrils) Findings Highlights Increasing the oil-in-water partition coefficient (Ko/w) from 5e-3 to 2 resulted in faster uptake of the dissolved drug in the epithelium. This relatively quicker uptake suggests that drugs with a higher value of Ko/w are more effective when targeting proximal regions in the nasal cavity. On the other hand, decreasing the partition coefficient of a drug leads to increased absorption in the posterior regions of the nasal cavity. However, reducing the partition coefficient drastically can lead to a large amount of drug being swallowed by the patient, which may then be absorbed through the gastrointestinal tract. Thus, based on the intended target site in the mucus layer, drugs with either high or low partition coefficients may be preferred. Particle size is another aspect that influences drug uptake in the epithelium. We compared two different particle sizes (3 µm and 5 µm) and studied their effect on drug uptake in the epithelium. The 3 µm particles dissolved faster than the 5 µm particles and consequently were more readily available for uptake. Also, the larger particles deposited more in the anterior third of the nasal cavity, which is predominantly lined with squamous epithelium, due to inertial impaction. For these particles to be absorbed, the dissolved drug must reach the posterior part of the nasal cavity which is ciliated and lined with columnar epithelial cells. Drug solubility also plays an important role on a drug’s subsequent uptake. As solubility increases, the rate of dissolution and subsequent uptake of the drug also increase. Two different drug solubility values (0.02 mg/ml and 0.2 mg/ml) were considered as model inputs. It was observed that the cumulative uptake for the 0.2 mg/ml drug was appreciably higher than the drug with a 0.02 mg/ml solubility. Typically, drugs with higher solubility tend to dissolve completely. This ensures a higher uptake in the epithelium, but it also makes it difficult for the drug to reach the distal regions of the nasal cavity. Finally, the effect of initial particle deposition location on cumulative drug uptake was studied. Particle deposition trends in the nasal cavity depend on the initial particle size. Larger particles tend to deposit more in the anterior third of the nasal cavity owing to inertial impaction whereas smaller particles (< 5 µm) show a more distributed spread in their initial deposition. Thus, if the goal is to achieve a high uptake rate, smaller sized particles must be selected at the drug formulation stage. Based on these observations, a number of drug formulation parameters can be tweaked to attain the desired rate of uptake. The present results indicate that drug deposition in the unciliated anterior third of the nose precludes active translocation of these drugs to the posterior, distal regions via MCC. Thus, present nasal drug delivery mechanisms may benefit from modification. One potential modification may be to combine current bio-adhesive approaches with targeted drug delivery to pre-determined sites in the nasal cavities. Although the current CFD model lays a solid foundation for drug uptake predictions through the intranasal route, it has certain limitations. The airway surface layer (ASL) in this study consists of a gel layer and a sol layer. The gel layer is modeled as a Newtonian fluid with a high viscosity compared to the sol layer whose viscosity is 10,000 times lower than that in the gel layer. Although the gel layer may exhibit rheological complexities, a Newtonian approximation can be assumed as the local shear rates are relatively small. Future Work The model in its current form calculates the cumulative mass of the dissolved drug that is absorbed at the epithelium but does not calculate the plasma concentration profiles. This makes it difficult to fully assess the efficacy of the drug due to MCC in the different systemic regions. To address this limitation, a physiologically-based pharmacokinetics (PBPK) model is planned to simulate the amount of drug that enters both the epithelial cells and the systemic circulation. It will allow for a one-to-one comparison of nasal deposition profiles and blood concentration of the inhaled drugs. This Impact Story is based on the work of Sriram Chari, Karthik Sridhar, Ross Walenga, Clement Kleinstreuer, “Computational analysis of a 3D mucociliary clearance model predicting nasal drug uptake,” in the Journal of Aerosol Science, Volume 155, 2021, https://doi.org/10.1016/j.jaerosci.2021.105757 How does this work advance drug development? The mucociliary clearance model developed by CDER and its research partners can help us to rationally predict the effects of changes in the properties of nasally delivered drug candidates on drug exposures in patients. This capability can aid in the development of new safe and effective nasally inhaled drugs as well as generics of these complex products. Bacon R, Newman S, Rankin L, Pitcairn G, Whiting R. 2012. Pulmonary and nasal deposition of ketorolac tromethamine solution (SPRIX) following intranasal administration. International journal of pharmaceutics 431(1-2):39-44. Bitter C, Suter-Zimmermann K, Surber C. 2011. Nasal drug delivery in humans. Karger Publishers, Topical Applications and the Mucosa; p. 20-35. Blake J. 1972. A model for the micro-structure in ciliated organisms. Journal of Fluid Mechanics 55(1):1-23. Fulford GR, Blake JR. 1986. Muco-ciliary transport in the lung. Journal of theoretical biology 121(4):381-402. Funk JR, Hall GW, Crandall JR, Pilkey WD. 2000. Linear and quasi-linear viscoelastic characterization of ankle ligaments. Journal of Biomechanical Engineering 122(1):15-22. King M, Agarwal M, Shukla JB. 1993. A planar model for mucociliary transport: effect of mucus viscoelasticity. Biorheology 30(1):49-61. Naclerio RM, Baroody FM, Bidani N, Marcy De T, Penney BC. 2003. A comparison of nasal clearance after treatment of perennial allergic rhinitis with budesonide and mometasone. Otolaryngology Head and Neck Surgery 128(2):220-227. Pardeshi CV, Belgamwar VS. 2013. Direct nose to brain drug delivery via integrated nerve pathways bypassing the blood–brain barrier: an excellent platform for brain targeting. Expert opinion on drug delivery 10(7):957-72. Sedaghat MH, Shahmardan MM, Norouzi M, Nazari M, Jayathilake PG. 2016. On the effect of mucus rheology on the muco-ciliary transport. Mathematical biosciences 272:44-53. Shah SA, Berger RL, McDermott J, Gupta P, Monteith D, Connor A, Lin W, editors. 2015. Regional deposition of mometasone furoate nasal spray suspension in humans. Allergy & Asthma Proceedings. Sleigh MA, Blake JR, Liron N. 1988. The propulsion of mucus by cilia. American Review of Respiratory Disease 137(3):726-741. Smith DJ, Gaffney EA, Blake JR. 2008. Modelling mucociliary clearance. Respiratory physiology & neurobiology 163(1-3):178-188. Iowa Reading Research Center Research Article of the Month: August 2024 This blog post is part of our  Research Article of the Month series. For this month, we highlight “ Teacher Professional Development and Student Reading Achievement: A Meta-Analytic Review of the Effects ,” an article published in Journal of Research on Educational Effectiveness in 2019. Important words related to research are bolded, and definitions of these terms are included at the end of the article in the “Terms to Know” section. Why Did We Pick This Paper? Research suggests that high-quality teachers play a significant role in student achievement—higher than any other school factor (Hattie, 2009). One way to build teacher capacity is through professional development (PD)—training on current, effective, evidence-based instructional methods. PD can take different forms, including workshops, professional learning communities, coaching, online training, or conference attendance.  High-quality PD can improve teachers’ knowledge and skills and change their attitudes and beliefs. This, in turn, can affect their instruction and practices, which is likely to positively impact student learning (Desimone, 2009). This study measures the effects of teacher PD on student reading outcomes. Researchers also examine moderators  that may influence these outcomes, including characteristics of the study design (e.g., experimental design, student outcomes measured), PD opportunities (e.g., intensity, delivery method, level of collaboration, format), teachers (e.g., years of experience, certifications and degrees), and students (e.g., disability status, grade level).  Findings from this study may help districts identify and provide high-quality PD that builds teacher knowledge and supports student reading achievement. What Are the Research Questions or Purpose? The researchers examined the impact of teacher PD on student reading outcomes by addressing the following research questions: What are the effects of PD on reading achievement for students in Grades K–8? What elements of study design are potential moderators of effects? What characteristics of professional development are potential moderators of effects, what characteristics of participants, both teacher and student, are potential moderators of effects, what methodology do the authors employ. The authors conducted a meta-analysis of 28 quantitative research studies that examined the impacts of teacher PD on student reading outcomes. To be included in the analysis, the studies needed to: Be conducted in a K–8 setting Examine teacher PD as the independent variable Examine student reading achievement (e.g., phonological awareness, decoding, word identification, fluency, vocabulary, or comprehension) as the dependent variable   Utilize an experimental or quasi-experimental design Include effect sizes or the ability to calculate them Be published in a peer-reviewed journal in English between 1975 and 2017 For each of the included studies, researchers examined the students’ performance on a reading outcome. These outcomes were classified as either code-focused (e.g., phonological awareness, decoding, word identification, fluency), meaning-focused (e.g., comprehension, vocabulary), or general reading ability.  Researchers also took into account other variables in the studies that could affect the outcomes of PD. These variables included: Experimental design ( randomized control trials , quasi-experimental design) Student outcomes measured (code-focused or meaning-focused) Intensity (number of hours of PD) Delivery method (district staff, researchers, online) Level of collaboration and active participation Format (whole group, summer workshop, professional learning community, coaching) Average years of teaching experience Percentage of teachers with advanced degrees Disability status Grade level The researchers estimated effect sizes of teacher PD on student reading outcomes using a random effects model . They also examined the relationships between potential moderators (i.e., study, PD, teacher, and student characteristics) and student outcomes.  What Are the Key Findings? What are the effects of pd on reading achievement for students in grades k – 8. Overall, the analysis results of the study indicate that teacher PD positively impacted student reading outcomes in reading (g = 0.18). However, this is the average effect size, and there is notable variation in effect sizes reported from the primary studies included in this meta-analysis. This indicates that some kinds of PD were more effective than others. For example, Teacher Study Group (TSG), a PD model, had a medium to large positive effect on student reading outcomes (Gersten et al., 2010) whereas other PD models had small or no effect.  For randomized controlled trials (g = 0.18) and quasi-experimental design studies (g = 0.19), teacher PD significantly improved student outcomes in reading. Teacher PD significantly improved both code-focused student outcomes (g = 0.22) and meaning-focused student outcomes (g = 0.17). The PD characteristics examined did not significantly moderate the effect between PD and reading outcomes. The teacher and student characteristics examined did not significantly moderate the effect between teacher PD and reading outcomes. What Are the Practical Applications of Key Findings? Findings suggest that teacher PD generally has a positive effect on student reading achievement in Grades K–8. It is worth noting that the average total length of teacher PD was around 52 hours, with a range of 4 to 295 hours across studies, and these PD opportunities were associated with varying levels of impact on student outcomes. This wide range indicates that a certain level of intensity and duration may be necessary for teacher PD to have a significant effect on student outcomes, although there is no agreement among researchers on the level of intensity required to effectively enhance teacher knowledge and practices and produce improved student outcomes. When providing reading and literacy PD for teachers, it is important to ensure that the content is both meaningful and relevant to the teacher’s instructional needs. Additionally, allocating sufficient time to PD can help maximize its benefits for teachers and students.  What Are the Limitations of This Paper? When examining the relationships between the moderators and the student reading outcomes, teacher characteristics such as teaching experience and advanced degrees were included in the moderator analysis. However, teacher knowledge and skills, another important part of teacher characteristics, was not considered or included in the analysis. Teacher knowledge and skills has been shown to potentially influence student learning and, ultimately, their reading outcomes (Soodla, Jogi & Kikas, 2017;  Porter et al., 2023). Future research should incorporate this characteristic to provide a more comprehensive understanding of the factors that contribute to student outcomes, as the primary goal of teacher PD programs is to enhance both teacher knowledge and skills.  In addition, there is a lack of data on students with disabilities and secondary students across studies. Only one study included in the meta-analysis examined reading outcomes for students with reading difficulties, and there were few studies at the secondary level. It remains unclear whether teacher PD could effectively address the needs of students with disabilities, who require high-quality, specialized instructional strategies to support their reading. Future studies should explore the impact of PD on teacher’s ability to support these students.  Terms to Know Moderator: Moderators are variables that affect the relationship between two other variables. For example, the relationship between the length of a reading intervention and reading comprehension may be stronger for students who are at risk for reading disabilities versus students who are not at risk. In this case, at-risk status would be a moderator. Independent variable: An independent variable is a factor that influences dependent variables in experimental studies. For example, the length of a reading intervention in total minutes (independent variable) may affect a student’s composite reading score (dependent variable). They are called “independent” because they are manipulated by the experimenter and therefore independent of other influences. Dependent variable: Dependent variables are factors that may change in response to an independent variable. For example, a student’s composite reading score (dependent variable) may change in response to the length of reading intervention they receive in total minutes (independent variable). Experimental: Experimental research aims to determine whether a certain treatment influences a measurable outcome—for example, whether a certain instructional method influences students’ reading comprehension scores. To do this, participants are divided into two groups: an experimental group, which receives the treatment, and a control group, which does not receive the treatment. In an experimental study, these groups are randomly assigned, meaning each participant has equal probability of being in either the treatment or the control group. Both groups are tested before and after the treatment, and their results are compared. Because participants are randomly assigned to a control group, this kind of study is also known as a randomized control trial . Quasi-experimental: A quasi-experimental study is similar to an experimental study except that participants are not randomly assigned to groups. In educational research, groups often are assigned by classroom rather than through random assignment, making this kind of research quasi-experimental. Effect size: In statistics, effect size is a measure of the strength of the relationship between two variables in statistical analyses. A commonly used interpretation is to refer to effect size as small (g = 0.2), medium (g = 0.5), and large (g = 0.8) based on the benchmarks suggested by Cohen (1988), where “g” refers to Hedge’s g, a statistical measure of effect size. Peer-reviewed journal: When an author submits an article to a peer-reviewed journal , the article is reviewed by scholars in the field. They make sure that the article is accurate, relevant, high quality, and well written. Randomized control trial:  See experimental.  Random effects model: A random effects model is a type of statistical model that measures how an independent variable affects a dependent variable across a number of different samples or studies. Unlike a fixed effects model, a random effects model accounts for variability between different groups in a dataset. Cohen, J. (1988). Statistical power analysis for the behavioral sciences . Routledge. Desimone, L. M. (2009). Improving impact studies of teachers’ professional development: Toward better conceptualizations and measures. Educational Researcher , 38 (3), 181–199.  https://doi.org/10.3102/0013189X08331140   Didion, L., Toste, J. R., & Filderman, M. J. (2019). Teacher professional development and student reading achievement: A meta-analytic review of the effects. Journal of Research on Educational Effectiveness , 13 (1), 29–66.  https://doi.org/10.1080/19345747.2019.1670884   Hattie, J. A. (2009). Visible learning: A synthesis of 800+ meta-analyses on achievement . Routledge. Gersten, R., Dimino, J., Jayanthi, M., Kim, J. S., & Santoro, L. E. (2010). Teacher study group: Impact of the professional development model on reading instruction and student outcomes in first grade classrooms.  American Educational Research Journal ,  47 (3), 694-739.  https://doi.org/10.3102/0002831209361208   Porter, S. B., Odegard, T. N., Farris, E. A., & Oslund, E. L. (2023). Effects of teacher knowledge of early reading on students’ gains in reading foundational skills and comprehension.  Reading and Writing , 1-17.  https://doi.org/10.1007/s11145-023-10448-w   Soodla, P., Jõgi, A. L., & Kikas, E. (2017). Relationships between teachers’ metacognitive knowledge and students’ metacognitive knowledge and reading achievement.  European Journal of Psychology of Education ,  32 , 201-218. professional development professional learning communities Research Article of the Month Research Article of the Month: June 2024 Research Article of the Month: May 2024 Research Article of the Month: April 2024 2024 Externally Published Research Center for Financial Research Researchers Research Fellows Senior Advisor Special Advisor 2023 Fellows 2022 Fellows Visiting Scholar Working Papers 23rd Bank Research Conference Conference Videos Speaker Information Poster Videos Poster Session Videos Previous Conference Programs Other Conferences 2021-2022 FDIC Academic Challenge 2020-2021 FDIC Academic Challenge Prior Years Research Assistants Internships Visiting Scholars Published 2024   By George F. Shoukry (Federal Deposit Insurance Corporation)  , March 2024, Volume 59, Issue 2, 896-932   By Levent Güntay (Özyeğin University), Stefan Jacewitz (Federal Reserve Bank of Kansas City), and Jonathan Pogach (Federal Deposit Insurance Corporation)  , March-April 2024, Volume 56, Issue 2-3, 537-538   Ryan M. Goodstein (Federal Deposit Insurance Corporation) and Mark J. Kutzbach (Federal Deposit Insurance Corporation)  , Forthcoming   Sumit Agarwal (National University of Singapore), Souphala Chomisisengphet (Office of the Comptroller of the Currency), Hua Kiefer (Federal Deposit Insurance Corporation), Leonard C. Kiefer (Freddie Mac), and Paolina C. Medina (Texas A&M University)  , Forthcoming   Christopher Martin (Federal Deposit Insurance Corporation), Manju Puri (Duke University, Federal Deposit Insurance Corporation, National Bureau of Economic Research), and Alexander Ufier (Federal Deposit Insurance Corporation) , Forthcoming Last Updated: August 4, 2024 Information Author Services Initiatives You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader. All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess . Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications. Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers. Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal. Original Submission Date Received: . Active Journals Find a Journal Proceedings Series For Authors For Reviewers For Editors For Librarians For Publishers For Societies For Conference Organizers Open Access Policy Institutional Open Access Program Special Issues Guidelines Editorial Process Research and Publication Ethics Article Processing Charges Testimonials Preprints.org SciProfiles Encyclopedia Article Menu Subscribe SciFeed Recommended Articles Google Scholar on Google Scholar Table of Contents Find support for a specific problem in the support section of our website. Please let us know what you think of our products and services. Visit our dedicated information section to learn more about MDPI. JSmol Viewer Experimental research on the low-cycle fatigue crack growth rate for a stiffened plate of eh36 steel for use in ship structures. 1. Introduction 2. low cycle fatigue crack growth experiment for stiffened plate, 3. result and discussion, 3.1. experimental results of stiffened plates with single-edge crack, 3.2. experimental results of stiffened plates with central crack, 4. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest. Dong, Q.; Xu, G.; Zhao, J.; Hu, Y. Experimental and numerical study on crack propagation of cracked plates under low cycle fatigue loads. J. Mar. Sci. Eng. 2023 , 11 , 1436. [ Google Scholar ] [ CrossRef ] Dong, Q.; Lu, X.; Xu, G. Experimental study on low-cycle fatigue characteristics of marine structural steel. J. Mar. Sci. Eng. 2024 , 12 , 651. [ Google Scholar ] [ CrossRef ] Gan, J.; Sun, D.; Deng, H.; Wang, Z.; Wang, X.; Yao, L.; Wu, W. Fatigue characteristics of designed T-type specimen under two-step repeating variable amplitude load with low-amplitude load below the fatigue limit. J. Mar. Sci. Eng. 2021 , 9 , 107. [ Google Scholar ] [ CrossRef ] Wang, Q.; Huber, N.; Liu, X.; Kashaev, N. On the analysis of plasticity induced crack closure in welded specimens: A mechanism controlled by the stress intensity factor resulting from residual stresses. Int. J. Fatigue 2022 , 162 , 106940. [ Google Scholar ] [ CrossRef ] Sistaninia, M.; Kolednik, O. A novel approach for determining the stress intensity factor for cracks in multilayered cantilevers. Eng. Fract. Mech. 2022 , 266 , 108386. [ Google Scholar ] [ CrossRef ] Nassiraei, H.; Rezadoost, P. Stress concentration factors in tubular T-joints reinforced with external ring under in-plane bending moment. Ocean. Eng. 2022 , 266 , 112551. [ Google Scholar ] [ CrossRef ] Nassiraei, H.; Rezadoost, P. Probabilistic analysis of the SCFs in tubular T/Y-joints reinforced with FRP under axial, in-plane bending, and out-of-plane bending loads. Structures 2022 , 35 , 1078–1097. [ Google Scholar ] [ CrossRef ] Nassiraei, H.; Rezadoost, P. Stress concentration factors in tubular T/Y-connections reinforced with FRP under in-plane bending load. Mar. Struct. 2021 , 76 , 102871. [ Google Scholar ] [ CrossRef ] Nassiraei, H.; Rezadoost, P. Static capacity of tubular X-joints reinforced with fiber reinforced polymer subjected to compressive load. Eng. Struct. 2021 , 236 , 112041. [ Google Scholar ] [ CrossRef ] Nassiraei, H.; Rezadoost, P. Stress concentration factors in tubular T/Y-joints strengthened with FRP subjected to compressive load in offshore structures. Int. J. Fatigue 2020 , 140 , 105719. [ Google Scholar ] [ CrossRef ] Dong, Q.; Yang, P.; Xu, G. Low cycle fatigue crack growth analysis by CTOD under variable amplitude loading for AH32 steel. Mar. Struct. 2019 , 63 , 257–268. [ Google Scholar ] [ CrossRef ] Dowling, N.E. Geometry effects and the J-integral approach to elastic-plastic fatigue crack growth. In Cracks and Fracture ; Swedlow, J., Williams, M., Eds.; ASTM STP 601; American Society for Testing and Materials: Philadelphia, PA, USA, 1976; pp. 19–32. [ Google Scholar ] Gonzales, G.L.G.; González, J.A.O.; Antunes, F.V.; Neto, D.M.; Díaz, F.A. Experimental determination of the reversed plastic zone around fatigue crack using digital image correlation. Theor. Appl. Fract. Mech. 2023 , 125 , 103901. [ Google Scholar ] [ CrossRef ] Dunham, F.W. Fatigue Testing of Large-Scale Models of Submarine Structural Details. Mar. Technol. SNAME News 1965 , 2 , 299–307. [ Google Scholar ] [ CrossRef ] Chen, L.; Chen, X. The low cycle fatigue tests on submarine structures. Ship Sci. Technol. 1991 , 2 , 19–20. [ Google Scholar ] Li, C. Research on low cycle fatigue properties of several types of steel for submarine pressure shell. Dev. Appl. Mater. 1986 , 12 , 28–37. [ Google Scholar ] Liu, Y.; Zhu, X.; Huang, X. Experimental research on low frequency fatigue crack propagation rate of 921A hull steel structure. J. Nav. Univ. Eng. 2008 , 20 , 69–74. [ Google Scholar ] Jandejsek, I.; Gajdoš, L.; Šperl, M.; Vavřík, D. Analysis of standard fracture toughness test based on digital image correlation data. Eng. Fract. Mech. 2017 , 182 , 607–620. [ Google Scholar ] [ CrossRef ] Zhang, W.; Liu, Y. Plastic zone size estimation under cyclic loadings using in situ optical microscopy fatigue testing. Fatigue Fract. Eng. Mater. Struct. 2011 , 34 , 717–727. [ Google Scholar ] [ CrossRef ] Vasco-Olmo, J.M.; Díaz, F.A.; Antunes, F.V.; James, M.N. Characterization of fatigue crack growth using digital image correlation measurements of plastic CTOD. Theor. Appl. Fract. Mech. 2019 , 101 , 332–341. [ Google Scholar ] [ CrossRef ] Belytschko, T.; Black, T. Elastic crack growth in finite elements with minimal remeshing. Int. J. Numer. Methods Eng. 1999 , 45 , 601–620. [ Google Scholar ] [ CrossRef ] Melenk, J.M.; Babuška, I. The partition of unity finite element method: Basic theory and applications. Comput. Methods Appl. Mech. 1996 , 139 , 289–314. [ Google Scholar ] [ CrossRef ] He, L.; Liu, Z.; Gu, J.; Wang, J.; Men, K. Fatigue crack propagation path and life prediction based on XFEM. J. Northwestern Polytech. Univ. 2019 , 37 , 737–743. [ Google Scholar ] [ CrossRef ] Tu, W.; Yue, J.; Xie, H.; Tang, W. Fatigue crack propagation behavior of high-strength steel under variable amplitude loading. Eng. Fract. Mech. 2021 , 247 , 107642. [ Google Scholar ] [ CrossRef ] Huang, X.; Zhang, X.; Bai, G.; Xu, W.; Wang, H. Residual strength analysis of thin-walled structures with multiple site damage based on crack tip opening angle method. J. Shanghai Jiao Tong Univ. 2013 , 47 , 519–524+531. [ Google Scholar ] Hwang, J.H.; Kim, H.T.; Kim, Y.J.; Nam, H.S.; Kim, J.W. Crack tip fields at crack initiation and growth under monotonic and large amplitude cyclic loading: Experimental and FE analyses. Int. J. Fatigue 2020 , 141 , 105889. [ Google Scholar ] [ CrossRef ] Xiong, K.; Deng, J.; Pei, Z.; Yang, P.; Dong, Q. Analysis of accumulative plasticity and fracture behavior of hull cracked plates subjected to biaxial low-cycle fatigue loading. J. Ship Mech. 2022 , 26 , 113–124. [ Google Scholar ] Wei, X. Fatigue Reliability Analysis of Ship Stiffened Panel Structure Subjected to Multiple Cracks. Master’s Thesis, Harbin Engineering University, Harbin, China, 2017. [ Google Scholar ] Soares, C.G.; Garbatov, Y.; Safety, S. Fatigue reliability of the ship hull girder accounting for inspection and repair. Reliab. Eng. 1996 , 51 , 341–351. [ Google Scholar ] [ CrossRef ] Duncheva, G.; Maximov, J.; Ganev, N.; Ivanova, M. Fatigue life enhancement of welded stiffened S355 steel plates with noncircular openings. J. Constr. Steel Res. 2015 , 112 , 93–107. [ Google Scholar ] [ CrossRef ] Lei, J.; Yue, J.; Xu, Z.; Fang, X.; Liu, H. Theoretical and Experimental Analysis on Low-Cycle Fatigue Crack Initiation for High Strength Steel Stiffened Plates. In Proceedings of the ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering, Hamburg, Germany, 5–10 June 2022. [ Google Scholar ] Dexter, R.J.; Pilarski, P.J. Crack propagation in welded stiffened panels. J. Constr. Steel Res. 2002 , 58 , 1081–1102. [ Google Scholar ] [ CrossRef ] Jiang, W.; Yang, P. Experimental studies on crack propagation and accumulative mean strain of cracked stiffened plates under low-cycle fatigue loads. Ocean. Eng. 2020 , 214 , 107744. [ Google Scholar ] [ CrossRef ] Song, Y.; Yang, P.; Hu, K.; Jiang, W.; Zhang, G. Study of low-cycle fatigue crack growth behavior of central-cracked stiffened plates. Ocean. Eng. 2021 , 241 , 110083. [ Google Scholar ] [ CrossRef ] Dong, Q.; Yang, P.; Deng, J.L.; Wang, D. The theoretical and numerical research on CTOD for ship plate under cyclic loading considering accumulative plastic strain. J. Ship Mech. 2015 , 19 , 1507–1516. [ Google Scholar ] Deng, J.; Yang, P.; Dong, Q.; Wang, D. Research on CTOD for low cycle fatigue analysis of central through cracked plates considering accumulative plastic strain. Eng. Fract. Mech. 2016 , 154 , 128–139. [ Google Scholar ] [ CrossRef ] Dong, Q.; Yang, P.; Xu, G.; Deng, J.L. Mechanisms and modeling of low cycle fatigue crack propagation in a pressure vessel steel Q345. Int. J. Fatigue 2016 , 89 , 2–10. [ Google Scholar ] [ CrossRef ] Click here to enlarge figure Elastic Modulus/GPa Poisson’s Ratio Yield Stress/MPa Ultimate Tensile Strength/MPa 206 0.3 434.94 548.91 Specimen Number P /kN R = P /P Nominal Stress/MPa Crack Location Stiffener Height P1 84.24 −1 120 single-edge crack 30 mm P2 90.72 −1 130 single-edge crack 30 mm P3 97.20 −1 140 single-edge crack 30 mm P4 384.00 0.031 280 central crack 30 mm P5 420.00 0.2 300 central crack 30 mm P6 420.00 0.2 300 central crack 0 mm The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. Share and Cite Dong, Q.; Xu, G.; Chen, W. Experimental Research on the Low-Cycle Fatigue Crack Growth Rate for a Stiffened Plate of EH36 Steel for Use in Ship Structures. J. Mar. Sci. Eng. 2024 , 12 , 1365. https://doi.org/10.3390/jmse12081365 Dong Q, Xu G, Chen W. Experimental Research on the Low-Cycle Fatigue Crack Growth Rate for a Stiffened Plate of EH36 Steel for Use in Ship Structures. Journal of Marine Science and Engineering . 2024; 12(8):1365. https://doi.org/10.3390/jmse12081365 Dong, Qin, Geng Xu, and Wei Chen. 2024. "Experimental Research on the Low-Cycle Fatigue Crack Growth Rate for a Stiffened Plate of EH36 Steel for Use in Ship Structures" Journal of Marine Science and Engineering 12, no. 8: 1365. https://doi.org/10.3390/jmse12081365 Article Metrics Article access statistics, further information, mdpi initiatives, follow mdpi. Subscribe to receive issue release notifications and newsletters from MDPI journals 195 IMAGES PPT The Massive Quasi-Experimental Data Mining Method Quasi-Experiment With Null as a Research Hypothesis Frequentist PPT Schematic of Study Design (Quasi-Experimental). What is a Quasi-Experimental Design? COMMENTS Quasi-Experimental Design Revised on January 22, 2024. Like a true experiment, a quasi-experimental design aims to establish a cause-and-effect relationship between an independent and dependent variable. However, unlike a true experiment, a quasi-experiment does not rely on random assignment. Instead, subjects are assigned to groups based on non-random criteria. Statistical Analysis and Application of Quasi Experiments to In summary, 2-group tests, regression analysis, and time-series analysis can accommodate interrupted time-series quasi-experimental data. However, statistical validity depends on using appropriate methods for the study question, meeting data requirements, and verifying modeling assumptions. Quasi Experimental Design Overview & Examples A significant advantage of quasi-experimental research over purely observational studies and correlational research is that it addresses the issue of directionality, determining which variable is the cause and which is the effect. In quasi-experiments, an intervention typically occurs during the investigation, and the researchers record outcomes before and after it, increasing the confidence ... Quasi-Experimental Research Design Quasi-experimental design is a research method that seeks to evaluate the causal relationships between variables, but without the full control over the independent variable (s) that is available in a true experimental design. In a quasi-experimental design, the researcher uses an existing group of participants that is not randomly assigned to ... Quasi-experimental Research: What It Is, Types & Examples Quasi-experimental research is a quantitative researchmethod. It involves numerical data collection and statistical analysis. Quasi-experimental research compares groups with different circumstances or treatments to find cause-and-effect links. It draws statistical conclusions from quantitative data. How to Use and Interpret Quasi-Experimental Design A quasi-experimental study (also known as a non-randomized pre-post intervention) is a research design in which the independent variable is manipulated, but participants are not randomly assigned to conditions. Commonly used in medical informatics (a field that uses digital information to ensure better patient care), researchers generally use ... 7.3 Quasi-Experimental Research Describe three different types of quasi-experimental research designs (nonequivalent groups, pretest-posttest, and interrupted time series) and identify examples of each one. The prefix quasi means "resembling.". Thus quasi-experimental research is research that resembles experimental research but is not true experimental research. The Use and Interpretation of Quasi-Experimental Studies in Medical In medical informatics, the quasi-experimental, sometimes called the pre-post intervention, design often is used to evaluate the benefits of specific interventions. The increasing capacity of health care institutions to collect routine clinical data has led to the growing use of quasi-experimental study designs in the field of medical ... Chapter 7 Quasi-Experimental Research The prefix quasi means "resembling." Thus quasi-experimental research is research that resembles experimental research but is not true experimental research. Although the independent variable is manipulated, participants are not randomly assigned to conditions or orders of conditions (Cook et al., 1979).Because the independent variable is manipulated before the dependent variable is ... Selecting and Improving Quasi-Experimental Designs in Effectiveness and First, it can allow for data analysis strategies that can incorporate cyclical temporal trends (such as seasonality-mediated risk for the outcome, such as with flu or malaria) or other underlying temporal trends. ... "Experimental and Quasi-Experimental Designs for Research on Teaching." In Gage NL (ed.), Handbook of Research on Teaching ... Sage Research Methods Foundations Event History and Survival Analysis; Age-Period-Cohort Analysis; Qualitative Longitudinal Research; Attrition; Time Series Analysis; Dynamic Panel Data Models; Conditional Logit Model; Causal Analysis With Panel Data; Sequence Analysis; Latent Transition Analysis; Quasi-Experimental Designs; Yule, George Udny; Lazarsfeld, Paul F. Panel Data ... (PDF) Quasi-Experimental Research Designs Quasi-experimental research designs are the most widely used research approach employed to evaluate the outcomes of social work programs and policies. This new volume describes the logic, design ... Quasi-experiment A quasi-experiment is an empirical interventional study used to estimate the causal impact of an intervention on target population without random assignment. Quasi-experimental research shares similarities with the traditional experimental design or randomized controlled trial, but it specifically lacks the element of random assignment to ... Quasi-experimentation: A guide to design and analysis. Citation. Reichardt, C. S. (2019). Quasi-experimentation: A guide to design and analysis. The Guilford Press. Abstract. This volume explains the logic of both the design of quasi-experiments and the analysis of the data they produce to provide estimates of treatment effects that are as credible as can be obtained given the demanding constraints of research practice. The Limitations of Quasi-Experimental Studies, and Methods for Data All said and done, the QE research design is best avoided because it is flawed and because even the best statistical approaches to data analysis would be imperfect. The QE design should be considered only when no other options are available. Readers are referred to Harris et al. 5 for a further discussion on QE studies. PDF Quantitative Research Designs: Experimental, Quasi-Experimental, and The cardiometabolic risk factors that were measured included blood pressure, insulin resistance, triglyercides, and HDL. Additional data was collected such as BMI and blood levels of vitamin D as serum 25(OH)D. Baseline measurements were taken, as well as measurements at 6 months and. 12 months (end of study). Chapter 23 Quasi-experimental 23 Quasi-experimental. 23. Quasi-experimental. In most cases, it means that you have pre- and post-intervention data. Great resources for causal inference include Causal Inference Mixtape and Recent Advances in Micro, especially if you like to read about the history of causal inference as a field as well (codes for Stata, R, and Python ... The Limitations of Quasi-Experimental Studies, and Methods for Data Keywords: Quasi-experimental study, research design, univariable analysis, multivariable regression, confounding variables If we wish to study how antidepressant drug treatment affects outcomes in pregnancy, we should ideally randomize depressed pregnant women to receive an antidepressant drug or placebo; this is a randomized controlled trial ... Quasi-Experimental Research The prefix quasi means "resembling." Thus quasi-experimental research is research that resembles experimental research but is not true experimental research. Although the independent variable is manipulated, participants are not randomly assigned to conditions or orders of conditions (Cook & Campbell, 1979). [1] Because the independent variable is manipulated before the dependent variable ... Experimental vs Quasi-Experimental Design: Which to Choose? An experimental design is a randomized study design used to evaluate the effect of an intervention. In its simplest form, the participants will be randomly divided into 2 groups: A treatment group: where participants receive the new intervention which effect we want to study. A control or comparison group: where participants do not receive any ... Quasi-Experimental Research Design & Analysis (SOC 258B) This course surveys quantitative methods to make causal inferences in the absence of randomized experiment including the use of natural and quasi-experiments, instrumental variables, regression discontinuity, fixed effects estimators, and difference-in-differences. We emphasize the proper interpretation of these research designs and critical engagement with their key assumptions for applied ... Statistical Analysis of Quasi-Experimental Designs: III. Matching in Quasi-Experimental Designs: Normative Group Equivalence. Because of the problems in selecting people in a normative group matching design and the potential problems with the data analysis of that design, you may want to make the normative comparison group equivalent on selected demographic characteristics. You might want the same proportion of males and females, and the mean ... Effect of an educational intervention based on self-efficacy theory and A quasi-experimental study was conducted from January to July 2021 among pregnant women residing in Mashhad, Iran. To this aim, 110 pregnant women at a gestational age of 12-18 weeks were randomly assigned to a control (n = 55) and an intervention group (n = 55) and completed all questionnaires during the intervention and the 3-month follow-up. Book Title: Graduate research methods in social work 13.4 Quasi-experimental designs; 13.5 Non-experimental designs; 13.6 Critical, ethical, and cultural considerations; ... 17.3 Finding the right qualitative data to answer my research question; 17.4 How to gather a qualitative sample; 17.5 What should my sample look like? ... A survey of approaches to qualitative data analysis. 19.1 Ethical ... How computational analysis of a 3D mucociliary clearance model can help To validate the accuracy of the velocity field obtained from the proposed 3D mucus model, inert particle clearance data from the nose obtained from simulations was compared with in vivo data ... Research Article of the Month: August 2024 To be included in the analysis, the studies needed to: Be conducted in a K-8 setting; Examine teacher PD as the independent variable; Examine student reading achievement (e.g., phonological awareness, decoding, word identification, fluency, vocabulary, or comprehension) as the dependent variable ; Utilize an experimental or quasi-experimental ... 2024 Externally Published Research Insurance Pricing, Distortions, and Moral Hazard: Quasi-Experimental Evidence from Deposit Insurance By George F. Shoukry (Federal Deposit Insurance Corporation) Journal of Financial and Quantitative Analysis , March 2024, Volume 59, Issue 2, 896-932 JMSE To ensure the validity of the experimental data, three replicate ... Xu, Z.; Fang, X.; Liu, H. Theoretical and Experimental Analysis on Low-Cycle Fatigue Crack Initiation for High Strength Steel Stiffened Plates. In Proceedings of the ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering, Hamburg, Germany, 5-10 ... Peers in puppy love and student academic performance in middle school 6 Students, parents, HTs, and schools' data are matched to obtain the final data. 7 There is at least one parent with a master's degree or above. 8 The questionnaire allows students to self-report their degree of difficulty learning Chinese, Math, and English in the sixth grade, which can be used to measure students' previous academic ... essay homework paper phd project resume review speech study thesis