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Clarifying the Research Purpose

Methodology, measurement, data analysis and interpretation, tools for evaluating the quality of medical education research, research support, competing interests, quantitative research methods in medical education.

Submitted for publication January 8, 2018. Accepted for publication November 29, 2018.

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John T. Ratelle , Adam P. Sawatsky , Thomas J. Beckman; Quantitative Research Methods in Medical Education. Anesthesiology 2019; 131:23–35 doi: https://doi.org/10.1097/ALN.0000000000002727

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There has been a dramatic growth of scholarly articles in medical education in recent years. Evaluating medical education research requires specific orientation to issues related to format and content. Our goal is to review the quantitative aspects of research in medical education so that clinicians may understand these articles with respect to framing the study, recognizing methodologic issues, and utilizing instruments for evaluating the quality of medical education research. This review can be used both as a tool when appraising medical education research articles and as a primer for clinicians interested in pursuing scholarship in medical education.

Image: J. P. Rathmell and Terri Navarette.

There has been an explosion of research in the field of medical education. A search of PubMed demonstrates that more than 40,000 articles have been indexed under the medical subject heading “Medical Education” since 2010, which is more than the total number of articles indexed under this heading in the 1980s and 1990s combined. Keeping up to date requires that practicing clinicians have the skills to interpret and appraise the quality of research articles, especially when serving as editors, reviewers, and consumers of the literature.

While medical education shares many characteristics with other biomedical fields, substantial particularities exist. We recognize that practicing clinicians may not be familiar with the nuances of education research and how to assess its quality. Therefore, our purpose is to provide a review of quantitative research methodologies in medical education. Specifically, we describe a structure that can be used when conducting or evaluating medical education research articles.

Clarifying the research purpose is an essential first step when reading or conducting scholarship in medical education. 1   Medical education research can serve a variety of purposes, from advancing the science of learning to improving the outcomes of medical trainees and the patients they care for. However, a well-designed study has limited value if it addresses vague, redundant, or unimportant medical education research questions.

What is the research topic and why is it important? What is unknown about the research topic? Why is further research necessary?

What is the conceptual framework being used to approach the study?

What is the statement of study intent?

What are the research methodology and study design? Are they appropriate for the study objective(s)?

Which threats to internal validity are most relevant for the study?

What is the outcome and how was it measured?

Can the results be trusted? What is the validity and reliability of the measurements?

How were research subjects selected? Is the research sample representative of the target population?

Was the data analysis appropriate for the study design and type of data?

What is the effect size? Do the results have educational significance?

Fortunately, there are steps to ensure that the purpose of a research study is clear and logical. Table 1   2–5   outlines these steps, which will be described in detail in the following sections. We describe these elements not as a simple “checklist,” but as an advanced organizer that can be used to understand a medical education research study. These steps can also be used by clinician educators who are new to the field of education research and who wish to conduct scholarship in medical education.

Steps in Clarifying the Purpose of a Research Study in Medical Education

Literature Review and Problem Statement

A literature review is the first step in clarifying the purpose of a medical education research article. 2 , 5 , 6   When conducting scholarship in medical education, a literature review helps researchers develop an understanding of their topic of interest. This understanding includes both existing knowledge about the topic as well as key gaps in the literature, which aids the researcher in refining their study question. Additionally, a literature review helps researchers identify conceptual frameworks that have been used to approach the research topic. 2  

When reading scholarship in medical education, a successful literature review provides background information so that even someone unfamiliar with the research topic can understand the rationale for the study. Located in the introduction of the manuscript, the literature review guides the reader through what is already known in a manner that highlights the importance of the research topic. The literature review should also identify key gaps in the literature so the reader can understand the need for further research. This gap description includes an explicit problem statement that summarizes the important issues and provides a reason for the study. 2 , 4   The following is one example of a problem statement:

“Identifying gaps in the competency of anesthesia residents in time for intervention is critical to patient safety and an effective learning system… [However], few available instruments relate to complex behavioral performance or provide descriptors…that could inform subsequent feedback, individualized teaching, remediation, and curriculum revision.” 7  

This problem statement articulates the research topic (identifying resident performance gaps), why it is important (to intervene for the sake of learning and patient safety), and current gaps in the literature (few tools are available to assess resident performance). The researchers have now underscored why further research is needed and have helped readers anticipate the overarching goals of their study (to develop an instrument to measure anesthesiology resident performance). 4  

The Conceptual Framework

Following the literature review and articulation of the problem statement, the next step in clarifying the research purpose is to select a conceptual framework that can be applied to the research topic. Conceptual frameworks are “ways of thinking about a problem or a study, or ways of representing how complex things work.” 3   Just as clinical trials are informed by basic science research in the laboratory, conceptual frameworks often serve as the “basic science” that informs scholarship in medical education. At a fundamental level, conceptual frameworks provide a structured approach to solving the problem identified in the problem statement.

Conceptual frameworks may take the form of theories, principles, or models that help to explain the research problem by identifying its essential variables or elements. Alternatively, conceptual frameworks may represent evidence-based best practices that researchers can apply to an issue identified in the problem statement. 3   Importantly, there is no single best conceptual framework for a particular research topic, although the choice of a conceptual framework is often informed by the literature review and knowing which conceptual frameworks have been used in similar research. 8   For further information on selecting a conceptual framework for research in medical education, we direct readers to the work of Bordage 3   and Irby et al. 9  

To illustrate how different conceptual frameworks can be applied to a research problem, suppose you encounter a study to reduce the frequency of communication errors among anesthesiology residents during day-to-night handoff. Table 2 10 , 11   identifies two different conceptual frameworks researchers might use to approach the task. The first framework, cognitive load theory, has been proposed as a conceptual framework to identify potential variables that may lead to handoff errors. 12   Specifically, cognitive load theory identifies the three factors that affect short-term memory and thus may lead to communication errors:

Conceptual Frameworks to Address the Issue of Handoff Errors in the Intensive Care Unit

Intrinsic load: Inherent complexity or difficulty of the information the resident is trying to learn ( e.g. , complex patients).

Extraneous load: Distractions or demands on short-term memory that are not related to the information the resident is trying to learn ( e.g. , background noise, interruptions).

Germane load: Effort or mental strategies used by the resident to organize and understand the information he/she is trying to learn ( e.g. , teach back, note taking).

Using cognitive load theory as a conceptual framework, researchers may design an intervention to reduce extraneous load and help the resident remember the overnight to-do’s. An example might be dedicated, pager-free handoff times where distractions are minimized.

The second framework identified in table 2 , the I-PASS (Illness severity, Patient summary, Action list, Situational awareness and contingency planning, and Synthesis by receiver) handoff mnemonic, 11   is an evidence-based best practice that, when incorporated as part of a handoff bundle, has been shown to reduce handoff errors on pediatric wards. 13   Researchers choosing this conceptual framework may adapt some or all of the I-PASS elements for resident handoffs in the intensive care unit.

Note that both of the conceptual frameworks outlined above provide researchers with a structured approach to addressing the issue of handoff errors; one is not necessarily better than the other. Indeed, it is possible for researchers to use both frameworks when designing their study. Ultimately, we provide this example to demonstrate the necessity of selecting conceptual frameworks to clarify the research purpose. 3 , 8   Readers should look for conceptual frameworks in the introduction section and should be wary of their omission, as commonly seen in less well-developed medical education research articles. 14  

Statement of Study Intent

After reviewing the literature, articulating the problem statement, and selecting a conceptual framework to address the research topic, the final step in clarifying the research purpose is the statement of study intent. The statement of study intent is arguably the most important element of framing the study because it makes the research purpose explicit. 2   Consider the following example:

This study aimed to test the hypothesis that the introduction of the BASIC Examination was associated with an accelerated knowledge acquisition during residency training, as measured by increments in annual ITE scores. 15  

This statement of study intent succinctly identifies several key study elements including the population (anesthesiology residents), the intervention/independent variable (introduction of the BASIC Examination), the outcome/dependent variable (knowledge acquisition, as measure by in In-training Examination [ITE] scores), and the hypothesized relationship between the independent and dependent variable (the authors hypothesize a positive correlation between the BASIC examination and the speed of knowledge acquisition). 6 , 14  

The statement of study intent will sometimes manifest as a research objective, rather than hypothesis or question. In such instances there may not be explicit independent and dependent variables, but the study population and research aim should be clearly identified. The following is an example:

“In this report, we present the results of 3 [years] of course data with respect to the practice improvements proposed by participating anesthesiologists and their success in implementing those plans. Specifically, our primary aim is to assess the frequency and type of improvements that were completed and any factors that influence completion.” 16  

The statement of study intent is the logical culmination of the literature review, problem statement, and conceptual framework, and is a transition point between the Introduction and Methods sections of a medical education research report. Nonetheless, a systematic review of experimental research in medical education demonstrated that statements of study intent are absent in the majority of articles. 14   When reading a medical education research article where the statement of study intent is absent, it may be necessary to infer the research aim by gathering information from the Introduction and Methods sections. In these cases, it can be useful to identify the following key elements 6 , 14 , 17   :

Population of interest/type of learner ( e.g. , pain medicine fellow or anesthesiology residents)

Independent/predictor variable ( e.g. , educational intervention or characteristic of the learners)

Dependent/outcome variable ( e.g. , intubation skills or knowledge of anesthetic agents)

Relationship between the variables ( e.g. , “improve” or “mitigate”)

Occasionally, it may be difficult to differentiate the independent study variable from the dependent study variable. 17   For example, consider a study aiming to measure the relationship between burnout and personal debt among anesthesiology residents. Do the researchers believe burnout might lead to high personal debt, or that high personal debt may lead to burnout? This “chicken or egg” conundrum reinforces the importance of the conceptual framework which, if present, should serve as an explanation or rationale for the predicted relationship between study variables.

Research methodology is the “…design or plan that shapes the methods to be used in a study.” 1   Essentially, methodology is the general strategy for answering a research question, whereas methods are the specific steps and techniques that are used to collect data and implement the strategy. Our objective here is to provide an overview of quantitative methodologies ( i.e. , approaches) in medical education research.

The choice of research methodology is made by balancing the approach that best answers the research question against the feasibility of completing the study. There is no perfect methodology because each has its own potential caveats, flaws and/or sources of bias. Before delving into an overview of the methodologies, it is important to highlight common sources of bias in education research. We use the term internal validity to describe the degree to which the findings of a research study represent “the truth,” as opposed to some alternative hypothesis or variables. 18   Table 3   18–20   provides a list of common threats to internal validity in medical education research, along with tactics to mitigate these threats.

Threats to Internal Validity and Strategies to Mitigate Their Effects

Experimental Research

The fundamental tenet of experimental research is the manipulation of an independent or experimental variable to measure its effect on a dependent or outcome variable.

True Experiment

True experimental study designs minimize threats to internal validity by randomizing study subjects to experimental and control groups. Through ensuring that differences between groups are—beyond the intervention/variable of interest—purely due to chance, researchers reduce the internal validity threats related to subject characteristics, time-related maturation, and regression to the mean. 18 , 19  

Quasi-experiment

There are many instances in medical education where randomization may not be feasible or ethical. For instance, researchers wanting to test the effect of a new curriculum among medical students may not be able to randomize learners due to competing curricular obligations and schedules. In these cases, researchers may be forced to assign subjects to experimental and control groups based upon some other criterion beyond randomization, such as different classrooms or different sections of the same course. This process, called quasi-randomization, does not inherently lead to internal validity threats, as long as research investigators are mindful of measuring and controlling for extraneous variables between study groups. 19  

Single-group Methodologies

All experimental study designs compare two or more groups: experimental and control. A common experimental study design in medical education research is the single-group pretest–posttest design, which compares a group of learners before and after the implementation of an intervention. 21   In essence, a single-group pre–post design compares an experimental group ( i.e. , postintervention) to a “no-intervention” control group ( i.e. , preintervention). 19   This study design is problematic for several reasons. Consider the following hypothetical example: A research article reports the effects of a year-long intubation curriculum for first-year anesthesiology residents. All residents participate in monthly, half-day workshops over the course of an academic year. The article reports a positive effect on residents’ skills as demonstrated by a significant improvement in intubation success rates at the end of the year when compared to the beginning.

This study does little to advance the science of learning among anesthesiology residents. While this hypothetical report demonstrates an improvement in residents’ intubation success before versus after the intervention, it does not tell why the workshop worked, how it compares to other educational interventions, or how it fits in to the broader picture of anesthesia training.

Single-group pre–post study designs open themselves to a myriad of threats to internal validity. 20   In our hypothetical example, the improvement in residents’ intubation skills may have been due to other educational experience(s) ( i.e. , implementation threat) and/or improvement in manual dexterity that occurred naturally with time ( i.e. , maturation threat), rather than the airway curriculum. Consequently, single-group pre–post studies should be interpreted with caution. 18  

Repeated testing, before and after the intervention, is one strategy that can be used to reduce the some of the inherent limitations of the single-group study design. Repeated pretesting can mitigate the effect of regression toward the mean, a statistical phenomenon whereby low pretest scores tend to move closer to the mean on subsequent testing (regardless of intervention). 20   Likewise, repeated posttesting at multiple time intervals can provide potentially useful information about the short- and long-term effects of an intervention ( e.g. , the “durability” of the gain in knowledge, skill, or attitude).

Observational Research

Unlike experimental studies, observational research does not involve manipulation of any variables. These studies often involve measuring associations, developing psychometric instruments, or conducting surveys.

Association Research

Association research seeks to identify relationships between two or more variables within a group or groups (correlational research), or similarities/differences between two or more existing groups (causal–comparative research). For example, correlational research might seek to measure the relationship between burnout and educational debt among anesthesiology residents, while causal–comparative research may seek to measure differences in educational debt and/or burnout between anesthesiology and surgery residents. Notably, association research may identify relationships between variables, but does not necessarily support a causal relationship between them.

Psychometric and Survey Research

Psychometric instruments measure a psychologic or cognitive construct such as knowledge, satisfaction, beliefs, and symptoms. Surveys are one type of psychometric instrument, but many other types exist, such as evaluations of direct observation, written examinations, or screening tools. 22   Psychometric instruments are ubiquitous in medical education research and can be used to describe a trait within a study population ( e.g. , rates of depression among medical students) or to measure associations between study variables ( e.g. , association between depression and board scores among medical students).

Psychometric and survey research studies are prone to the internal validity threats listed in table 3 , particularly those relating to mortality, location, and instrumentation. 18   Additionally, readers must ensure that the instrument scores can be trusted to truly represent the construct being measured. For example, suppose you encounter a research article demonstrating a positive association between attending physician teaching effectiveness as measured by a survey of medical students, and the frequency with which the attending physician provides coffee and doughnuts on rounds. Can we be confident that this survey administered to medical students is truly measuring teaching effectiveness? Or is it simply measuring the attending physician’s “likability”? Issues related to measurement and the trustworthiness of data are described in detail in the following section on measurement and the related issues of validity and reliability.

Measurement refers to “the assigning of numbers to individuals in a systematic way as a means of representing properties of the individuals.” 23   Research data can only be trusted insofar as we trust the measurement used to obtain the data. Measurement is of particular importance in medical education research because many of the constructs being measured ( e.g. , knowledge, skill, attitudes) are abstract and subject to measurement error. 24   This section highlights two specific issues related to the trustworthiness of data: the validity and reliability of measurements.

Validity regarding the scores of a measurement instrument “refers to the degree to which evidence and theory support the interpretations of the [instrument’s results] for the proposed use of the [instrument].” 25   In essence, do we believe the results obtained from a measurement really represent what we were trying to measure? Note that validity evidence for the scores of a measurement instrument is separate from the internal validity of a research study. Several frameworks for validity evidence exist. Table 4 2 , 22 , 26   represents the most commonly used framework, developed by Messick, 27   which identifies sources of validity evidence—to support the target construct—from five main categories: content, response process, internal structure, relations to other variables, and consequences.

Sources of Validity Evidence for Measurement Instruments

Reliability

Reliability refers to the consistency of scores for a measurement instrument. 22 , 25 , 28   For an instrument to be reliable, we would anticipate that two individuals rating the same object of measurement in a specific context would provide the same scores. 25   Further, if the scores for an instrument are reliable between raters of the same object of measurement, then we can extrapolate that any difference in scores between two objects represents a true difference across the sample, and is not due to random variation in measurement. 29   Reliability can be demonstrated through a variety of methods such as internal consistency ( e.g. , Cronbach’s alpha), temporal stability ( e.g. , test–retest reliability), interrater agreement ( e.g. , intraclass correlation coefficient), and generalizability theory (generalizability coefficient). 22 , 29  

Example of a Validity and Reliability Argument

This section provides an illustration of validity and reliability in medical education. We use the signaling questions outlined in table 4 to make a validity and reliability argument for the Harvard Assessment of Anesthesia Resident Performance (HARP) instrument. 7   The HARP was developed by Blum et al. to measure the performance of anesthesia trainees that is required to provide safe anesthetic care to patients. According to the authors, the HARP is designed to be used “…as part of a multiscenario, simulation-based assessment” of resident performance. 7  

Content Validity: Does the Instrument’s Content Represent the Construct Being Measured?

To demonstrate content validity, instrument developers should describe the construct being measured and how the instrument was developed, and justify their approach. 25   The HARP is intended to measure resident performance in the critical domains required to provide safe anesthetic care. As such, investigators note that the HARP items were created through a two-step process. First, the instrument’s developers interviewed anesthesiologists with experience in resident education to identify the key traits needed for successful completion of anesthesia residency training. Second, the authors used a modified Delphi process to synthesize the responses into five key behaviors: (1) formulate a clear anesthetic plan, (2) modify the plan under changing conditions, (3) communicate effectively, (4) identify performance improvement opportunities, and (5) recognize one’s limits. 7 , 30  

Response Process Validity: Are Raters Interpreting the Instrument Items as Intended?

In the case of the HARP, the developers included a scoring rubric with behavioral anchors to ensure that faculty raters could clearly identify how resident performance in each domain should be scored. 7  

Internal Structure Validity: Do Instrument Items Measuring Similar Constructs Yield Homogenous Results? Do Instrument Items Measuring Different Constructs Yield Heterogeneous Results?

Item-correlation for the HARP demonstrated a high degree of correlation between some items ( e.g. , formulating a plan and modifying the plan under changing conditions) and a lower degree of correlation between other items ( e.g. , formulating a plan and identifying performance improvement opportunities). 30   This finding is expected since the items within the HARP are designed to assess separate performance domains, and we would expect residents’ functioning to vary across domains.

Relationship to Other Variables’ Validity: Do Instrument Scores Correlate with Other Measures of Similar or Different Constructs as Expected?

As it applies to the HARP, one would expect that the performance of anesthesia residents will improve over the course of training. Indeed, HARP scores were found to be generally higher among third-year residents compared to first-year residents. 30  

Consequence Validity: Are Instrument Results Being Used as Intended? Are There Unintended or Negative Uses of the Instrument Results?

While investigators did not intentionally seek out consequence validity evidence for the HARP, unanticipated consequences of HARP scores were identified by the authors as follows:

“Data indicated that CA-3s had a lower percentage of worrisome scores (rating 2 or lower) than CA-1s… However, it is concerning that any CA-3s had any worrisome scores…low performance of some CA-3 residents, albeit in the simulated environment, suggests opportunities for training improvement.” 30  

That is, using the HARP to measure the performance of CA-3 anesthesia residents had the unintended consequence of identifying the need for improvement in resident training.

Reliability: Are the Instrument’s Scores Reproducible and Consistent between Raters?

The HARP was applied by two raters for every resident in the study across seven different simulation scenarios. The investigators conducted a generalizability study of HARP scores to estimate the variance in assessment scores that was due to the resident, the rater, and the scenario. They found little variance was due to the rater ( i.e. , scores were consistent between raters), indicating a high level of reliability. 7  

Sampling refers to the selection of research subjects ( i.e. , the sample) from a larger group of eligible individuals ( i.e. , the population). 31   Effective sampling leads to the inclusion of research subjects who represent the larger population of interest. Alternatively, ineffective sampling may lead to the selection of research subjects who are significantly different from the target population. Imagine that researchers want to explore the relationship between burnout and educational debt among pain medicine specialists. The researchers distribute a survey to 1,000 pain medicine specialists (the population), but only 300 individuals complete the survey (the sample). This result is problematic because the characteristics of those individuals who completed the survey and the entire population of pain medicine specialists may be fundamentally different. It is possible that the 300 study subjects may be experiencing more burnout and/or debt, and thus, were more motivated to complete the survey. Alternatively, the 700 nonresponders might have been too busy to respond and even more burned out than the 300 responders, which would suggest that the study findings were even more amplified than actually observed.

When evaluating a medical education research article, it is important to identify the sampling technique the researchers employed, how it might have influenced the results, and whether the results apply to the target population. 24  

Sampling Techniques

Sampling techniques generally fall into two categories: probability- or nonprobability-based. Probability-based sampling ensures that each individual within the target population has an equal opportunity of being selected as a research subject. Most commonly, this is done through random sampling, which should lead to a sample of research subjects that is similar to the target population. If significant differences between sample and population exist, those differences should be due to random chance, rather than systematic bias. The difference between data from a random sample and that from the population is referred to as sampling error. 24  

Nonprobability-based sampling involves selecting research participants such that inclusion of some individuals may be more likely than the inclusion of others. 31   Convenience sampling is one such example and involves selection of research subjects based upon ease or opportuneness. Convenience sampling is common in medical education research, but, as outlined in the example at the beginning of this section, it can lead to sampling bias. 24   When evaluating an article that uses nonprobability-based sampling, it is important to look for participation/response rate. In general, a participation rate of less than 75% should be viewed with skepticism. 21   Additionally, it is important to determine whether characteristics of participants and nonparticipants were reported and if significant differences between the two groups exist.

Interpreting medical education research requires a basic understanding of common ways in which quantitative data are analyzed and displayed. In this section, we highlight two broad topics that are of particular importance when evaluating research articles.

The Nature of the Measurement Variable

Measurement variables in quantitative research generally fall into three categories: nominal, ordinal, or interval. 24   Nominal variables (sometimes called categorical variables) involve data that can be placed into discrete categories without a specific order or structure. Examples include sex (male or female) and professional degree (M.D., D.O., M.B.B.S., etc .) where there is no clear hierarchical order to the categories. Ordinal variables can be ranked according to some criterion, but the spacing between categories may not be equal. Examples of ordinal variables may include measurements of satisfaction (satisfied vs . unsatisfied), agreement (disagree vs . agree), and educational experience (medical student, resident, fellow). As it applies to educational experience, it is noteworthy that even though education can be quantified in years, the spacing between years ( i.e. , educational “growth”) remains unequal. For instance, the difference in performance between second- and third-year medical students is dramatically different than third- and fourth-year medical students. Interval variables can also be ranked according to some criteria, but, unlike ordinal variables, the spacing between variable categories is equal. Examples of interval variables include test scores and salary. However, the conceptual boundaries between these measurement variables are not always clear, as in the case where ordinal scales can be assumed to have the properties of an interval scale, so long as the data’s distribution is not substantially skewed. 32  

Understanding the nature of the measurement variable is important when evaluating how the data are analyzed and reported. Medical education research commonly uses measurement instruments with items that are rated on Likert-type scales, whereby the respondent is asked to assess their level of agreement with a given statement. The response is often translated into a corresponding number ( e.g. , 1 = strongly disagree, 3 = neutral, 5 = strongly agree). It is remarkable that scores from Likert-type scales are sometimes not normally distributed ( i.e. , are skewed toward one end of the scale), indicating that the spacing between scores is unequal and the variable is ordinal in nature. In these cases, it is recommended to report results as frequencies or medians, rather than means and SDs. 33  

Consider an article evaluating medical students’ satisfaction with a new curriculum. Researchers measure satisfaction using a Likert-type scale (1 = very unsatisfied, 2 = unsatisfied, 3 = neutral, 4 = satisfied, 5 = very satisfied). A total of 20 medical students evaluate the curriculum, 10 of whom rate their satisfaction as “satisfied,” and 10 of whom rate it as “very satisfied.” In this case, it does not make much sense to report an average score of 4.5; it makes more sense to report results in terms of frequency ( e.g. , half of the students were “very satisfied” with the curriculum, and half were not).

Effect Size and CIs

In medical education, as in other research disciplines, it is common to report statistically significant results ( i.e. , small P values) in order to increase the likelihood of publication. 34 , 35   However, a significant P value in itself does necessarily represent the educational impact of the study results. A statement like “Intervention x was associated with a significant improvement in learners’ intubation skill compared to education intervention y ( P < 0.05)” tells us that there was a less than 5% chance that the difference in improvement between interventions x and y was due to chance. Yet that does not mean that the study intervention necessarily caused the nonchance results, or indicate whether the between-group difference is educationally significant. Therefore, readers should consider looking beyond the P value to effect size and/or CI when interpreting the study results. 36 , 37  

Effect size is “the magnitude of the difference between two groups,” which helps to quantify the educational significance of the research results. 37   Common measures of effect size include Cohen’s d (standardized difference between two means), risk ratio (compares binary outcomes between two groups), and Pearson’s r correlation (linear relationship between two continuous variables). 37   CIs represent “a range of values around a sample mean or proportion” and are a measure of precision. 31   While effect size and CI give more useful information than simple statistical significance, they are commonly omitted from medical education research articles. 35   In such instances, readers should be wary of overinterpreting a P value in isolation. For further information effect size and CI, we direct readers the work of Sullivan and Feinn 37   and Hulley et al. 31  

In this final section, we identify instruments that can be used to evaluate the quality of quantitative medical education research articles. To this point, we have focused on framing the study and research methodologies and identifying potential pitfalls to consider when appraising a specific article. This is important because how a study is framed and the choice of methodology require some subjective interpretation. Fortunately, there are several instruments available for evaluating medical education research methods and providing a structured approach to the evaluation process.

The Medical Education Research Study Quality Instrument (MERSQI) 21   and the Newcastle Ottawa Scale-Education (NOS-E) 38   are two commonly used instruments, both of which have an extensive body of validity evidence to support the interpretation of their scores. Table 5 21 , 39   provides more detail regarding the MERSQI, which includes evaluation of study design, sampling, data type, validity, data analysis, and outcomes. We have found that applying the MERSQI to manuscripts, articles, and protocols has intrinsic educational value, because this practice of application familiarizes MERSQI users with fundamental principles of medical education research. One aspect of the MERSQI that deserves special mention is the section on evaluating outcomes based on Kirkpatrick’s widely recognized hierarchy of reaction, learning, behavior, and results ( table 5 ; fig .). 40   Validity evidence for the scores of the MERSQI include its operational definitions to improve response process, excellent reliability, and internal consistency, as well as high correlation with other measures of study quality, likelihood of publication, citation rate, and an association between MERSQI score and the likelihood of study funding. 21 , 41   Additionally, consequence validity for the MERSQI scores has been demonstrated by its utility for identifying and disseminating high-quality research in medical education. 42  

Kirkpatrick’s hierarchy of outcomes as applied to education research. Reaction = Level 1, Learning = Level 2, Behavior = Level 3, Results = Level 4. Outcomes become more meaningful, yet more difficult to achieve, when progressing from Level 1 through Level 4. Adapted with permission from Beckman and Cook, 2007. 2  

The Medical Education Research Study Quality Instrument for Evaluating the Quality of Medical Education Research

The NOS-E is a newer tool to evaluate the quality of medication education research. It was developed as a modification of the Newcastle-Ottawa Scale 43   for appraising the quality of nonrandomized studies. The NOS-E includes items focusing on the representativeness of the experimental group, selection and compatibility of the control group, missing data/study retention, and blinding of outcome assessors. 38 , 39   Additional validity evidence for NOS-E scores includes operational definitions to improve response process, excellent reliability and internal consistency, and its correlation with other measures of study quality. 39   Notably, the complete NOS-E, along with its scoring rubric, can found in the article by Cook and Reed. 39  

A recent comparison of the MERSQI and NOS-E found acceptable interrater reliability and good correlation between the two instruments 39   However, noted differences exist between the MERSQI and NOS-E. Specifically, the MERSQI may be applied to a broad range of study designs, including experimental and cross-sectional research. Additionally, the MERSQI addresses issues related to measurement validity and data analysis, and places emphasis on educational outcomes. On the other hand, the NOS-E focuses specifically on experimental study designs, and on issues related to sampling techniques and outcome assessment. 39   Ultimately, the MERSQI and NOS-E are complementary tools that may be used together when evaluating the quality of medical education research.

Conclusions

This article provides an overview of quantitative research in medical education, underscores the main components of education research, and provides a general framework for evaluating research quality. We highlighted the importance of framing a study with respect to purpose, conceptual framework, and statement of study intent. We reviewed the most common research methodologies, along with threats to the validity of a study and its measurement instruments. Finally, we identified two complementary instruments, the MERSQI and NOS-E, for evaluating the quality of a medical education research study.

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Tavakol M, Sandars J: Quantitative and qualitative methods in medical education research: AMEE Guide No 90: Part II. Medical teacher. 2014; 36(10):838–48.

Support was provided solely from institutional and/or departmental sources.

The authors declare no competing interests.

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53 Quantitative research methods in medical education

• Published: October 2013
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Quantitative research in medical education tends to be predominantly observational research based on survey or correlational studies. As researchers strive towards making inferences about the impact of education interventions, a shift towards experimental research designs may enhance the quality and conclusions made in medical education. The establishment of experiment research designs, where interventions (i.e. curriculum, teaching or assessment interventions) are tested with an experimental group and either a comparison or controlled group of learners, may allow researchers to overcome validity concerns and infer potential cause–effect generalizations. There are a number of internal and external validity concerns that researchers need to be conscious of when designing their own or looking at others’ experimental research studies. The selection of a research design for any study should fit within the parameters of the stated research question or hypothesis. In quantitative research, the findings will reflect the reliability and validity (psychometric characteristics) of the measured outcomes or dependent variables (such as changes in knowledge, skills, or attitudes) used to assess the effectiveness of the medical education intervention (the independent variable of interest). It is important to remember that not all quantitative research involves experimental studies—important results can also be drawn from quantitative observational studies. This chapter outlines commonly used quantitative methods in medical education research. It explains their theoretical underpinnings, the evidence base for their use, and gives practical guidance on their application. It concludes with a section on the role of meta-analyses of quantitative research in medical education.

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Review article: Medical education research: an overview of methods

Article de synthèse: La recherche en éducation médicale: un aperçu des méthodes

• Review Article/Brief Review
• Published: 04 January 2012
• Volume 59 , pages 159–170, ( 2012 )

Cite this article

• Sylvain Boet MD 1 ,
• Saroo Sharma MBChB 2 ,
• Joanne Goldman MSc 3 &
• Scott Reeves PhD 4 , 5 , 6  

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This article provides clinician-teachers with an overview of the process necessary to move from an initial idea to the conceptualization and implementation of an empirical study in the field of medical education. This article will allow clinician-teachers to become familiar with educational research methodology in order to a) critically appraise education research studies and apply evidence-based education more effectively to their practice and b) initiate or collaborate in medical education research.

This review uses relevant articles published in the fields of medicine, education, psychology, and sociology before October 2011.

Principal findings

The focus of the majority of research in medical education has been on reporting outcomes related to participants. There has been less assessment of patient care outcomes, resulting in informing evidence-based education to only a limited extent. This article explains the process necessary to develop a focused and relevant education research question and emphasizes the importance of theory in medical education research. It describes a range of methodologies, including quantitative, qualitative, and mixed methods, and concludes with a discussion of dissemination of research findings. A majority of studies currently use quantitative methods. This article highlights how further use of qualitative methods can provide insight into the nuances and complexities of learning and teaching processes.

Conclusions

Research in medical education requires several successive steps, from formulating the correct research question to deciding the method for dissemination. Each approach has advantages and disadvantages and should be chosen according to the question being asked and the specific goal of the study. Well-conducted education research should allow progression towards the important goal of using evidence-based education in our teaching and institutions.

Cet article fournit aux cliniciens enseignants un aperçu du processus qu’il faut suivre pour passer de l’idée initiale à la conceptualisation puis à la mise en œuvre d’une étude empirique dans le domaine de l’éducation médicale. Cet article permettra aux cliniciens enseignants de se familiariser avec la méthodologie de la recherche en éducation (a) pour évaluer de façon critique les études sur la recherche en éducation et appliquer de façon plus efficace dans leur pratique un enseignement basé sur des données probantes et (b) pour mettre en œuvre un projet de recherche en éducation médicale ou y collaborer.

Cette synthèse utilise des articles pertinents parus dans les domaines de la médecine, de l’éducation, de la psychologie et de la sociologie avant octobre 2011.

Constatations principales

La majorité de la recherche en éducation médicale s’est concentrée sur la description des résultats obtenus par les participants. Il y a eu moins d’évaluations concernant l’impact sur les soins aux patients, ce qui n’a pu renseigner que de façon limitée l’éducation fondée sur les données probantes. Cet article explique le processus nécessaire pour développer une question de recherche en éducation, orientée et pertinente, et il insiste sur l’importance de la théorie dans la recherche en éducation médicale. Il décrit une gamme de méthodologies, dont des méthodes quantitatives, qualitatives ou mixtes, et se termine par une discussion sur la communication des résultats de la recherche. La majorité des études utilise actuellement des méthodes quantitatives. Cet article souligne combien l’utilisation de méthodes qualitatives peut apporter d’informations sur les nuances et la complexité des processus d’apprentissage et d’enseignement.

La recherche en éducation médicale nécessite plusieurs étapes successives, de la formulation de la bonne question de recherche à la décision d’une méthode de communication des résultats. Chaque approche a ses avantages et inconvénients et doit être choisie en fonction de la question posée et de l’objectif spécifique de l’étude. Une recherche en éducation bien conduite doit permettre une évolution vers l’important objectif qu’est l’utilisation de l’éducation basée sur les données probantes dans notre enseignement et nos établissements.

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The increasing focus on evidence, accountability, and quality in healthcare during the past two decades is also evident in the field of medical education. During this time, numerous educational stakeholders have advocated for movement from opinion-based to evidence-based education whereby educational curricula are based on research findings rather than historical and culturally engrained traditions. 1 , 2 Consequently, there has been a continued increase in medical educational research publications. 3 In the meantime, the profile of researchers has evolved. Geoff Norman has recently identified three generations in the history of medical education research: the first generation came randomly from unrelated disciplines; the second generation came with high-level academic training from related disciplines, and the third generation are healthcare professionals with additional training in education. 4 Medical education is now a stand-alone discipline which faces the challenges of developing the third generation of researchers and of continuing to recruit actively from other disciplines to enrich the field. 4

While the field of medical education research has developed during the last years with some improvement in methodological rigour, there are many opportunities for further advancement. 5 For example, the focus of the majority of research undertaken has been on reporting outcomes related to participants rather than on assessment of patient care outcomes. Those lower impact results can inform evidence-based education to only a limited extent. In addition, a majority of studies use quantitative methods; further use of qualitative methods can provide insight into the nuances and complexities of the learning and teaching processes in medical education.

An understanding of educational research methodology is important not only for researchers but also for all clinicians involved in undergraduate, postgraduate, and continuing education. This knowledge is valuable for two reasons. First, it is important for clinicians to have the skills and knowledge to initiate, or collaborate in, high quality research. Second, clinicians with these skills are able to appraise research effectively and critically and apply evidence-based findings to their teaching and practice.

This article aims to provide clinician-teachers with an overview of the process necessary to move from an initial idea, or hunch, which may arise in their day-to-day teaching, to the conceptualization and implementation of an empirical study. Specifically, the article begins with guidance on how to develop a focused and relevant research question. This is followed by descriptions of a range of both familiar and less familiar research methodologies, an explanation of the importance of theory in medical education research, and lastly, a discussion of dissemination of research findings.

As for clinical research, medical education research is a highly structured process that involves careful protocol development based on a clear question, subject recruitment, data analysis, reporting, and dissemination of results. The key points on educational research are summarized at the end of the text. Following is an overview of these steps and issues.

Design issues

Formulating research question( s ).

The formulation of relevant research question(s) is the cornerstone of good educational studies that need to address key practical or theoretical concepts and issues.

A research question can arise from a clinical issue or from a theoretical perspective. For example, based on observations and experience of medical residents’ schedules, one might ask: ‘How does sleep deprivation affect resident-physicians’ professional lives and personal well-being?’ 11 On the other hand, a research question can arise from a literature review of a particular topic where further research is deemed valuable after the identification of content, methodological, and theoretical gaps. 13 For instance, a review of the literature on education formats might lead one to ask: ‘Can learning style predict student satisfaction with different instruction methods and academic achievement in medical education?’ 7 Research studies can advance the practice and theory of education, and ideally, they should address both of these aims. 13 , 14 For instance, a study comparing the effectiveness of instructor debriefing with self-debriefing (debriefing without an instructor) 6 addressed a practical question (Are the logistics of having an instructor on site worth the effort?) as well as a theoretical question (Is formative self-assessment effective in improving performance?). To contribute to the existing literature, a research question should address a gap in the literature.

The nature of the research question will determine whether a quantitative, qualitative, or mix-method s approach is appropriate to use. A study that aims to predict outcome through specific hypotheses testing will use quantitative (comparative, correlational, etc.) methods. For example, Arora et al. designed a quantitative study hypothesizing that subjects’ performance would be negatively correlated with their stress level. 15 An exploratory study that aims to examine the nature of a phenomenon lends itself to qualitative methods (ethnography, phenomenology, grounded theory, etc). For example, Wetzel et al. used a qualitative design to determine the surgical stressors, the perceived effects of stress on performance during surgery, and coping strategies. 16 The first step in planning a research study should be the formulation of a research question that addresses key theoretical concepts and practical issues rather than a decision about methods (“I want to do research in medical education; let’s do a survey!”).

A research question should be suitable for examination and should be meaningful, clear, and relevant to advance both the practice and theory of education. 17 , 18 A broad question, e.g., ‘Does simulation-based education work?’ is usually challenging to examine in a single research study. This type of question would be more appropriate for a systematic review that could synthesize the results of a number of studies on this topic. 19 A narrow and focused research question intends to increase the signal:noise ratio when using quantitative research methods. The measure of the signal (outcome) is more likely to appear if there are fewer confounders and variations on the measures (noise). A common initial step to refine the research question is to narrow it down as much as possible until the question becomes appropriate and answerable (Figure). 20

The evolution of a question from a broad idea to an appropriate and answerable research question. 20

Thinking about frameworks

A conceptual framework (also called a model) can organize and connect the different facets of a research study or research program into a single coherent structure. Importantly, the use of a framework can help classify what type of research study is being designed once the research question has been formulated (see above). Four popular frameworks/models in medical education are discussed below.

Kirkpatrick framework

The conceptual framework most widely used in education is the Kirkpatrick classification that categorizes the impact levels of an educational intervention. 21 The original classification has four levels of educational intervention outcomes: level 1- reaction; level 2- learning; level 3-behaviour; level 4 - results. In the context of medical education, level 2 refers to the learning of skills and knowledge in either a clinical or a non-clinical setting (e.g., simulated environment) ; level 3 refers to behavioural change of healthcare providers in the clinical setting ; and level 4 refers to improved patient outcome. 22

Researchers have also used a modified six-level Kirkpatrick classification 3 , 22 in which levels 2 and 4 are divided into levels 2a and 2b and 4a and 4b, as indicated in the Table. 23 It has been suggested that the fifth level is the cost-effectiveness of the educational intervention. 24

During the past few years, stakeholders have become aware of the need to shift from studying the impact of medical educational interventions on learners’ satisfaction and changes in their attitudes to studying the impact on health care processes and outcomes. This move entails greater rigour in the quality of medical education research. In its original design, the Kirkpatrick levels were not intended to be hierarchical; it is now recognized that research should target the higher levels (i.e., how an educational intervention affects patient outcome or cost effectiveness).

Translational science

Knowledge translation aims to “promote the uptake of evidence-based practices”. 25 Applied to medical education, knowledge translation aims to uptake evidence-based findings in education into educational practice. Medical education research may transfer to practice according to the three translational science levels: T1, T2, and T3. 26 Level T1 refers to a study of an educational intervention in which the outcome is measured in a laboratory setting (e.g., simulation room). Level T2 refers to a study that evaluates the impact of an educational intervention on patient care, as measured by improvements in healthcare providers’ performance in the clinical setting. Level T3 refers to studies that demonstrate improvements in patient outcome as the result of an education intervention.

The 3-P model (Presage-Process-Product) conceptualizes teaching and learning from the perspective of the learner. It supports the learner centeredness movement in contrast to the traditional teacher centeredness model. The general concept of the 3-P model is that learning outcomes result from interactions between the presage (the student and teacher contexts) and the process (the educational intervention). Student context refers to students’ motivation, values and expectations, learning styles, and prior knowledge and skills. Teacher context refers to the class or institutional teaching environment, structure and content of the course and curriculum, and teaching methods and evaluations. The interaction between those two contexts produces a specific approach to learning called process, which can be either deep or surface. In the deep process, students use multiple techniques, such as discussion, reading, and reflection, to create connections between pieces of information learned. Conversely, in the surface process, students reproduce the learning only to pass the assessment. The deep or surface type of process contributes to the product (the learning outcome). The 3-P model provides a useful structure to deepen reflection when developing a research project. 27 The 3-P model can help researchers to consider presage issues (e.g., contexts), how they affect process issues (e.g., learner interactions), and how these in turn can impact on product (e.g., reported outcomes from an intervention).

Cook classification

Cook et al. devised a hierarchical classification of medical education research based on the purpose of the study. The three main categories, which are independent of the method and educational outcome, are description, justification, and clarification. 28 The description category, which is the lowest level, refers to studies that present an innovation, such as a new assessment tool or curriculum, where there is no available comparison. The middle category, justification, involves studies that compare the effectiveness of educational interventions. The main question is: ‘Which intervention is better?’ The top category, clarification, advances the field of medical education by asking the questions: ‘How does it work?’ and ‘Why does it work?’ The few studies in this category use findings from previous research and rely on a conceptual theoretical framework that will be tested. Cook argues that the clarification studies in this top category “advance far more understanding of medical education” than the other categories. 28

The research team

The primary investigator has the responsibility of forming the winning team . The winning team is a well-functioning group that has the competencies required to achieve the study goals. One strategy to prevent team conflicts is to determine authorship and contributions at an early stage of the project, and perhaps even to sign a contract. 29 – 31 If the researcher lacks experience in medical education research, approaching experienced colleagues for advice and assistance should be considered. Novices should not hesitate to contact their “academic idol”.

The team should draw on the expertise of a range of relevant education researchers who may not be clinicians but experts in fields such as psychology and social science. Although these researchers may be unfamiliar with specific clinical contexts, they have a wealth of knowledge in theory and methodology that can inform the design and implementation of relevant education research questions. Experienced medical education researchers can support research by providing advice or detailed consultation or by collaborating with the research team. Novices may consider joining an experienced research team. This can be a useful way to receive support and guidance throughout the research process and avoid the pitfalls made by many novice education researchers.

Methods and designs

As previously stated, the research question will determine the study methodology. A methodology underpins how a study will progress, namely, the assumptions, principles, and procedures. There are various methodologies (e.g., experimental inquiry, quasi-experimental inquiry, ethnography, action research) that can be used, and the methodology, in turn, informs the design and methods, including the data collection and analysis strategies. For example, the randomized controlled trial methodology may use questionnaires to gather quantitative data; while in contrast, an ethnographic methodology will utilize observation and interview methods to collect data. 32 , 33 Following is a range of different designs that can be employed (Box 1 ).

Quantitative approaches

Surveys: Surveys add important information to findings from other types of research. They are inexpensive and they can be convenient. Many novice educational researchers consider using a survey - a decision which is often based on the misleading assumption that it is an easier method than others. In fact, it is challenging to devise a valid survey that advances the field. Moreover, institutional ethics approval is required as with any other type of research. 34 Several issues require attention when undertaking a survey. The phrasing of questions should be deliberated carefully as it can influence participants’ responses. 35 Sampling should be based on an appropriate sample size calculation, and use of stratification should be well thought out to optimize the efficiency of sampling. 36 Stratification is a sampling technique allowing subjects to be distributed equally in all study groups accounting for one or several parameters in the population. The limitation of the non-response bias is key, and the description of the non-responders is mandatory to make the study valid and reliable. 35 In addition, researchers need to attend to the logistical challenges of obtaining access to the studied population and the ethical issue of incentive. 37 Surveys are employed regularly in many of the research designs described below.

Post-course designs: Post-course design is popular in medical education research where data collection occurs at the end of an educational intervention. Typically, surveys are employed that usually comprise closed and open-ended questions to elicit both numerical and text-based data. This design has the main advantages of being inexpensive, straightforward, quick to conduct and analyze, and often with high response rates. This is largely because there is only one point of data collection; participant investment of time is relatively small; contacting potential participants presents few problems; and data can be analyzed readily. However, Skeff et al . have written, “when training influences participants’ criteria for their self-ratings (response shift), the validity of the traditional pre/post comparisons is suspect”. 38 Instead, they propose an alternative model called retrospective pre/post self-assessment ratings in which pre and post self-rating occurs only after the teaching intervention. They found this model to be more accurate than the traditional one.

Even with this type of model, a post-course design is a weak design, and as there is no collection of baseline data, it is difficult to account for reported change convincingly. Also, if data collection occurs in the final session of medical education activity, as is frequently the case, the longer-term impact of the education on practice cannot be assessed. Short post-course questionnaires devised for these studies are sometimes described as “happy sheets” because they capture little more than participants’ immediate reactions to a learning experience.

Before and after studies: Another popular design is the before and after study where the researcher collects data shortly before and after a learning opportunity. Again, the use of surveys (and sometimes interviews) is commonplace. This design is more robust than a post-course design, as it can detect changes resulting from a learning activity more accurately because there is data collection at two points in time, i.e., before and after the activity. If possible, obtaining paired data (where a respondent’s pre- and post-course responses can be linked) for numerical measures or ranks permits the use of more powerful statistical tests than obtaining unpaired data alone. The close proximity of data collection to course delivery makes tracking participants easier than in studies that also collect follow-up data.

Despite gathering data at two time points, a before and after study design is still limited in providing a rigorous understanding of change as it cannot state accurately whether the change was attributable to the intervention or some other confounding influence. This is where the use of a control group is helpful (see below). Also, by using the before and after study design, you cannot ascertain whether positive (or negative) change is sustained over time.

Controlled before and after studies: The controlled before and after research design is a quasi-experimental technique that can help detect whether a change occurred as a result of an intervention or some confounding influence, i.e., unrelated changes in the practice or learning environment. Controlled before and after designs provide a more robust understanding of outcomes than the post-course and before and after designs described above, but controlled before and after studies still have a number of limitations. Ensuring the equivalence of control and intervention groups regarding important learner characteristics demands careful attention to prevent the design of the study and the analysis of findings from being compromised. Also, the inclusion of a control group increases the amount of data collection and analysis, and hence cost. While controlled before and after studies can measure change robustly, they share the same limitations as before and after studies, namely, an inability to assess whether reported outcomes are sustained over time as well as problems ensuring that respondents complete questionnaires or attend interviews at both time points. Loss to follow-up may be greater in control groups, especially when the control group is relatively disengaged by not having received the intervention.

Randomized controlled trials: Controlled before and after studies can be redesigned to become randomized controlled trials (RCTs) by randomly selecting learners for inclusion in either the intervention or the control groups. Randomized controlled trials can provide a more robust understanding of the nature of change associated with an intervention. The randomization of participants in a course means that bias related to selection or recruitment is minimized. Although RCTs are used widely in clinical research—in which they are often considered the gold standard—they are not common in educational research. 19 Randomized controlled trials require a precise sample size based on the hypotheses to be tested. Attempts to randomize individuals and control for confounding variables may encounter objections that one group is favoured over the other , for example, a situation where a new teaching intervention is tested against a control that receives no teaching whatsoever.

Longitudinal studies: Longitudinal design can be employed to assess the impact of a medical education activity over time and to understand how this type of learning translates into clinical practice. In studies that use a longitudinal design, data is collected (over months or years) following an intervention. Longitudinal research is particularly helpful to overcome problems understanding the longer-term effects of medical education associated with the post-course, before and after, controlled before and after, and RCT designs described above. Longitudinal research is a good design to establish the relevance of education to subsequent clinical practice. Nevertheless, undertaking a longitudinal study can be difficult as learners often change jobs and move location over time. Attrition rates can be high, and the longer the time period a study tracks participants, the higher the attrition rate may be. Moreover, long-term data collection may become increasingly intrusive or burdensome to participants.

Qualitative approaches

Although used widely in social science research, qualitative methods are used less often in clinical research. 39 Qualitative research methods seek deep understanding of a phenomenon rather than aim to predict an outcome, 40 and these methods have contributed to our understanding of important clinical and educational issues. 39 The differences between quantitative and qualitative research are more complex than the presence or absence of numbers.

Where the focus of quantitative research is to answer questions of causality, the focus of qualitative research is to answer the whys and the hows. 39 Qualitative research allows for the generation of rich data and the exploration of real-life behaviour. 39 Qualitative studies often aim to “study things in their natural setting, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them”, and they use “a holistic perspective which preserves the complexities of human behaviour”. 40

Qualitative studies usually involve smaller sample sizes due to the more in-depth nature of data collection and analysis, which, while time-consuming, allows for the generation of rich data. Rather than absolute sample sizes, the concept of saturation (expanded later in the article) is used often to determine when data collection is complete (see Box 2 for information about this and other key qualitative terms). There are several qualitative methodologies (described below) that guide study design and methods.

Ethnography: Ethnography uses observations of social groups in their real environment as well as interviews and document analysis, rather than quantification, to focus on the meanings of actions and explanations of phenomena. 44 The goal of ethnography is to develop a depiction of the phenomena under study which is plausible yet allows for the inductive development of more general theories. 45 , 46

Phenomenology: Phenomenology uses observation and interviews as well as personal documents, such as diaries, to gain insight into subjects’ life experiences. 47 Studies that use phenomenology concentrate on exploring how individuals make sense of the world in terms of the meanings and classifications they employ. 47

Grounded theory: This approach can employ a variety of qualitative data collection methods, such as observations, interviews, and/or focus groups. Its overall aim is to generate theories about a social phenomenon from the collection and analysis of qualitative data. 43 Researchers develop a theory from their data using this grounded (or inductive) approach. 48

Case studies: Case studies examine a particular unit, e.g., individuals, groups, organizations, events, roles, or relationships. 49 Case studies allow the investigation of complex phenomena, 50 and data are collected to provide an in-depth picture of the case under study. Thematic data analysis is then carried out throughout the cases in order to draw meaning. 51

Action research studies: In action research studies, researchers work together with participants through cycles of action and research to plan change, guide participants through change, and study the change that occurs. 43 Researchers help participants to develop, deliver, study, and ultimately improve their practice. In action research studies, the researcher is more active and responsive in problem solving during the study than when employing other research designs where s/he records problems and reports on them.

Mixing quantitative and qualitative approaches

A mixed-methods study involves the use of both qualitative and quantitative methods. Many authors have argued for years about the superiority of quantitative vs qualitative approaches (and vice versa). 52 The combination of both methods is recognized increasingly as a useful technique. 53 , 54 Combining qualitative and quantitative methods can provide a more detailed understanding of the processes and outcomes associated with a medical education activity. 43 This approach has been advocated for the opportunities it provides to address different questions in a research study and thus to present a more comprehensive understanding of particular phenomena. On the other hand, mixed-methods study has also been noted as a problematic endeavour because each approach is based on competing considerations. Data triangulation, which refers to the comparison of findings about the same research question using different methods (this concept will be expanded upon later in the article), can occur between the different sources of data. Mixed-methods studies can be more resource intensive in terms of cost, time, and work. Mixed-methods studies remain rare in medicine, perhaps because they are more complex and require greater expertise. 55

Implementation issues

Securing ethical approval.

The principles related to the ethical conduct of research in medical education are no different than other types of research with humans. Issues to consider include potential vulnerability of students as participants due to a hierarchical relationship with the investigators, 14 , 56 informed consent, the absence of coercion, anonymized data, and confidentiality. 57 Egan-Lee et al . have published an article with specific tips for the application of ethics in educational research. 57 Procedures for obtaining consent will vary depending on the type of data involved . For example , the process for obtaining consent for an interview will involve different details than the process for obtaining consent for access to medical records, test scores, or prescribing data. Educational research studies require ethical approval from the organization where the research will be conducted and/or managed.

Accessing data

Data may be collected directly from participants or from existing sources, such as a database. Examples of the former include videotapes of simulation sessions, interviews and focus groups with health care providers who have participated in an educational program, or surveys of patients recently discharged from hospital. Examples of the latter include student examination results and medical residents’ assessments by staff clinicians. In both of these situations, the research team must negotiate access to the participants or to the data via gatekeepers. Negotiation usually requires support from key individuals, such as senior physicians, clinical or educational managers, program directors, deans, or committee leaders. Without support from such key people, participant recruitment and data collection will become an arduous, if not impossible, task. Generally, negotiation needs to precede application for ethical approval since evidence of support from key gatekeepers will be required.

Considering the resources

As for clinical research, securing resources to study medical education can be difficult. Nevertheless, wherever possible, the research team should seek to obtain funding for all stages of the research process, including literature review, question formulation, selection of methodology and methods, research instrument development, ethical approval, data collection/analysis, and dissemination of findings.

Addressing data collection issues

Numerous factors must be considered at the fieldwork (data collection) stage of a research study.

Researcher influence

Researchers need to acknowledge their own influences (e.g., preconceived ideas, paradigm in which they work, methods they use) in their research work, which are unrelated to the type of methodology they choose. In qualitative methodology, this is commonly referred to as reflexivity (see Box 2 ). In biomedical and clinical research, the positivist paradigm is the most common. In the positivist paradigm, there is an assumption of a single objective truth, and the aim of the research is to find disproval of testable hypotheses via deductive methods.

In the interpretive paradigm, there is a belief that reality is in a continuous process of construction, which allows for the existence of a plurality of meaning in content. In this approach, the researcher is immersed in the qualitative data to produce an inductive interpretation. Throughout both paradigms, researchers must keep in mind that they influence many aspects of the study, including the boundaries of the study, study design, data collection methods, measurement tools, and approach to data analysis.

Insider and outsider positions

Researchers should reflect on their internal or external (outsider) research approach. Each has advantages and disadvantages. Nowadays, many teachers and researchers in medical education are also healthcare professionals. 4 As insiders, they can benefit from extensive knowledge of the history and context of the program, but that can make it difficult for them to interpret the data in a neutral manner. Insider researchers may also suffer from lack of time and resources to undertake empirical work. The need to deliver the program nearly always overrides the need for empirical study. Nevertheless, insider researchers are well placed to contribute their findings to course development and to formulate relevant preliminary research questions.

In contrast, outsider researchers generally will have dedicated the time and resources for their purpose. It may be easier for outsiders to view an intervention from a more neutral viewpoint and to obtain more candid data from participants. However, they often need to spend time developing an in-depth understanding of presage and process issues related to the activity they are studying. External research studies are often accorded greater weight because they are seen as more impartial and/or more authoritative. The differentiation between an insider and an outsider position may not always be clear. Both insider and outsider views are important in the collection and interpretation of data if a comprehensive picture is to be obtained. 58

Rigour and quality issues

Any method of research is rigorous when well conducted. Researchers should be aware of all potential biases (defined as a systematic error in the study which makes the results differ from the truth) in order to prevent or avoid them.

Qualitative components

There is often an assumption that qualitative methodology maintains a bias toward verification, understood as a tendency to confirm the researcher’s preconceived notions. 59 Methodological criteria that apply to quantitative work, such as validity, reliability, and empirical generalizability, usually are not applied to qualitative work. 42 However, scientific rigour is also crucial in qualitative research. A number of techniques and concepts, such as reflexivity, representativeness, saturation, triangulation, and respondent validation (also called resonance), can be used to ensure rigour (see Box 2 for definitions). 39 , 42

Quantitative components

Some of the many common biases include selection, sampling, and randomization biases.

Two biases more specific to educational research are the halo and Hawthorne effects.

The Halo effect

The halo effect is defined as “the influence of a global evaluation on evaluation of individual attributes of a person”. 60 A century ago, Thorndike noticed that raters tended to rely on general perceptions even when they were asked to evaluate specific characteristics of individuals. Typically, a halo effect may be suspected when a rater gives the same score or similar scores to all the individual items of an assessment tool. A halo effect may affect the results and conclusion of the study, but it may also bring about an artificial increase in the inter-rater reliability or in the inter-item reliability of any assessment tool. 61 It has been suggested that at least part of the halo error could be removed by explaining the rationale of the assessment tool to the assessors through training them on the use of the scale. 62 Therefore, authors report training used to familiarize the raters with the assessment tool, for example, running a calibration session between raters of performances on videos. 6

The Hawthorne effect

The Hawthorne effect describes a phenomenon of positively changed behaviour or performance resulting from awareness of being a part of a study. 63 This phenomenon is also known as reactivity. Assessing the impact of the Hawthorne effect on one’s research work is difficult, but researchers need to acknowledge its potential presence. For example, self-training using the virtual fibreoptic intubation software has been shown to improve trainees fibreoptic intubation skills when compared with traditional teaching with no virtual training. 64 One could argue that the improved performance of the trainees who received the software might simply have resulted from a Hawthorne effect. However, where a researcher does become involved with participants for longer periods of time, for example, undertaking observations of medical students for several months, it has been argued that altered behaviour tends to revert to normal behaviour. 65

Using assessment scales

A separate review article in this theme issue of the Journal focuses specifically on Assessment in anesthesiology education 66 .

Dissemination Issues

Education research aims to improve patient care and/or better inform education activities. As for clinical research, this can be achieved only with dissemination of the results, which is the final step of a study. The range of dissemination strategies include local dissemination (e.g., feedback to participants in a study, research presentations), national or international conferences (posters or articles), peer reviewed articles indexed in international databases (e.g., Google Scholar, Web of Science®, and PubMed), book chapters, websites ( https://www.mededportal.org ), and on-line reports. The use of two or more dissemination strategies will facilitate a wider sharing of key research messages. 67 Given the importance of knowledge translation as integral to educational research, working with educational committees may help to inform educational leaders involved in educational changes and may help to disseminate the findings of research and reinforce evidence-based education.

A common question in medical education research is whether the study should be published in medical education journals or specialty journals. Medical education journals have the advantage of reaching a large community of educators across specialties, while specialty journals target mostly clinicians within a specialty. Medical Education ( http://www.wiley.com/bw/journal.asp?ref=0308-0110 ), Medical Teacher ( http://www.medicalteacher.org/ ), and Advances in Health Sciences Education ( http://www.springer.com/education+%26+language/journal/10459 ) are the leading English speaking medical education journals. Pédagogie Médicale ( http://www.pedagogie-medicale.org/ ) is published in French. A foremost criterion for authors is to publish their work in the journal where their article will have the most impact according to their objectives. The research team will decide on the most appropriate journal according to their target audience and their personal agenda/goals.

As for clinical research, research in medical education requires several successive steps, from the formulation of the correct research question to the decision regarding the method of dissemination. More specific to research in education, it relies on multiple types of rigorous methods that could be a challenge to master. It is important to recognize that even experienced clinicians and educators may not possess the necessary skills to conduct a rigorous well thought-out education research study. Each method has its advantages and disadvantages and should be chosen according to the research question and the specific goal of the study. This article scratches merely the surface of the many methodologies and conceptual and theoretical frameworks in the field of education research. Clinician-teachers should become familiar with these methods in order to appraise research studies critically and apply evidence-based education more effectively in their practice. We stress the importance of formulating a precise question, choosing the correct methodology (even if initially unfamiliar), and harnessing the expertise of experienced researchers in the field. Without well-conducted education research, we cannot move toward the important goal of using evidence-based education in our teaching and institutions.

Think about conceptual and theoretical frameworks when formulating research question(s).

There are many methodological choices – select the most appropriate approach to answer your research question.

The study is often only as good as the outcome measure.

Publish the study in the journal that will be read by your target audience.

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Boet, S., Sharma, S., Goldman, J. et al. Review article: Medical education research: an overview of methods. Can J Anesth/J Can Anesth 59 , 159–170 (2012). https://doi.org/10.1007/s12630-011-9635-y

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Quantitative and qualitative methods in medical education research: AMEE Guide No 90: Part I

Affiliation.

• 1 The University of Nottingham , UK .
• PMID: 24846122
• DOI: 10.3109/0142159X.2014.915298

Medical educators need to understand and conduct medical education research in order to make informed decisions based on the best evidence, rather than rely on their own hunches. The purpose of this Guide is to provide medical educators, especially those who are new to medical education research, with a basic understanding of how quantitative and qualitative methods contribute to the medical education evidence base through their different inquiry approaches and also how to select the most appropriate inquiry approach to answer their research questions.

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

Quantitative research in education : Background information

• Background information
• SAGE researchmethods SAGE Research Methods is a tool created to help researchers, faculty and students with their research projects. Users can explore methods concepts to help them design research projects, understand particular methods or identify a new method, conduct their research, and write up their findings. Since SAGE Research Methods focuses on methodology rather than disciplines, it can be used across the social sciences, health sciences, and other areas of research.

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Speaker 1: Welcome to this overview of quantitative research methods. This tutorial will give you the big picture of quantitative research and introduce key concepts that will help you determine if quantitative methods are appropriate for your project study. First, what is educational research? Educational research is a process of scholarly inquiry designed to investigate the process of instruction and learning, the behaviors, perceptions, and attributes of students and teachers, the impact of institutional processes and policies, and all other areas of the educational process. The research design may be quantitative, qualitative, or a mixed methods design. The focus of this overview is quantitative methods. The general purpose of quantitative research is to explain, predict, investigate relationships, describe current conditions, or to examine possible impacts or influences on designated outcomes. Quantitative research differs from qualitative research in several ways. It works to achieve different goals and uses different methods and design. This table illustrates some of the key differences. Qualitative research generally uses a small sample to explore and describe experiences through the use of thick, rich descriptions of detailed data in an attempt to understand and interpret human perspectives. It is less interested in generalizing to the population as a whole. For example, when studying bullying, a qualitative researcher might learn about the experience of the victims and the experience of the bully by interviewing both bullies and victims and observing them on the playground. Quantitative studies generally use large samples to test numerical data by comparing or finding correlations among sample attributes so that the findings can be generalized to the population. If quantitative researchers were studying bullying, they might measure the effects of a bully on the victim by comparing students who are victims and students who are not victims of bullying using an attitudinal survey. In conducting quantitative research, the researcher first identifies the problem. For Ed.D. research, this problem represents a gap in practice. For Ph.D. research, this problem represents a gap in the literature. In either case, the problem needs to be of importance in the professional field. Next, the researcher establishes the purpose of the study. Why do you want to do the study, and what do you intend to accomplish? This is followed by research questions which help to focus the study. Once the study is focused, the researcher needs to review both seminal works and current peer-reviewed primary sources. Based on the research question and on a review of prior research, a hypothesis is created that predicts the relationship between the study's variables. Next, the researcher chooses a study design and methods to test the hypothesis. These choices should be informed by a review of methodological approaches used to address similar questions in prior research. Finally, appropriate analytical methods are used to analyze the data, allowing the researcher to draw conclusions and inferences about the data, and answer the research question that was originally posed. In quantitative research, research questions are typically descriptive, relational, or causal. Descriptive questions constrain the researcher to describing what currently exists. With a descriptive research question, one can examine perceptions or attitudes as well as more concrete variables such as achievement. For example, one might describe a population of learners by gathering data on their age, gender, socioeconomic status, and attributes towards their learning experiences. Relational questions examine the relationship between two or more variables. The X variable has some linear relationship to the Y variable. Causal inferences cannot be made from this type of research. For example, one could study the relationship between students' study habits and achievements. One might find that students using certain kinds of study strategies demonstrate greater learning, but one could not state conclusively that using certain study strategies will lead to or cause higher achievement. Causal questions, on the other hand, are designed to allow the researcher to draw a causal inference. A causal question seeks to determine if a treatment variable in a program had an effect on one or more outcome variables. In other words, the X variable influences the Y variable. For example, one could design a study that answered the question of whether a particular instructional approach caused students to learn more. The research question serves as a basis for posing a hypothesis, a predicted answer to the research question that incorporates operational definitions of the study's variables and is rooted in the literature. An operational definition matches a concept with a method of measurement, identifying how the concept will be quantified. For example, in a study of instructional strategies, the hypothesis might be that students of teachers who use Strategy X will exhibit greater learning than students of teachers who do not. In this study, one would need to operationalize learning by identifying a test or instrument that would measure learning. This approach allows the researcher to create a testable hypothesis. Relational and causal research relies on the creation of a null hypothesis, a version of the research hypothesis that predicts no relationship between variables or no effect of one variable on another. When writing the hypothesis for a quantitative question, the null hypothesis and the research or alternative hypothesis use parallel sentence structure. In this example, the null hypothesis states that there will be no statistical difference between groups, while the research or alternative hypothesis states that there will be a statistical difference between groups. Note also that both hypothesis statements operationalize the critical thinking skills variable by identifying the measurement instrument to be used. Once the research questions and hypotheses are solidified, the researcher must select a design that will create a situation in which the hypotheses can be tested and the research questions answered. Ideally, the research design will isolate the study's variables and control for intervening variables so that one can be certain of the relationships being tested. In educational research, however, it is extremely difficult to establish sufficient controls in the complex social settings being studied. In our example of investigating the impact of a certain instructional strategy in the classroom on student achievement, each day the teacher uses a specific instructional strategy. After school, some of the students in her class receive tutoring. Other students have parents that are very involved in their child's academic progress and provide learning experiences in the home. These students may do better because they received extra help, not because the teacher's instructional strategy is more effective. Unless the researcher can control for the intervening variable of extra help, it will be impossible to effectively test the study's hypothesis. Quantitative research designs can fall into two broad categories, experimental and quasi-experimental. Classic experimental designs are those that randomly assign subjects to either a control or treatment comparison group. The researcher can then compare the treatment group to the control group to test for an intervention's effect, known as a between-subject design. It is important to note that the control group may receive a standard treatment or may receive a treatment of any kind. Quasi-experimental designs do not randomly assign subjects to groups, but rather take advantage of existing groups. A researcher can still have a control and comparison group, but assignment to the groups is not random. The use of a control group is not required. However, the researcher may choose a design in which a single group is pre- and post-tested, known as a within-subjects design. Or a single group may receive only a post-test. Since quasi-experimental designs lack random assignment, the researcher should be aware of the threats to validity. Educational research often attempts to measure abstract variables such as attitudes, beliefs, and feelings. Surveys can capture data about these hard-to-measure variables, as well as other self-reported information such as demographic factors. A survey is an instrument used to collect verifiable information from a sample population. In quantitative research, surveys typically include questions that ask respondents to choose a rating from a scale, select one or more items from a list, or other responses that result in numerical data. Studies that use surveys or tests need to include strategies that establish the validity of the instrument used. There are many types of validity that need to be addressed. Face validity. Does the test appear at face value to measure what it is supposed to measure? Content validity. Content validity includes both item validity and sampling validity. Item validity ensures that the individual test items deal only with the subject being addressed. Sampling validity ensures that the range of item topics is appropriate to the subject being studied. For example, item validity might be high, but if all the items only deal with one aspect of the subjects, then sampling validity is low. Content validity can be established by having experts in the field review the test. Concurrent validity. Does a new test correlate with an older, established test that measures the same thing? Predictive validity. Does the test correlate with another related measure? For example, GRE tests are used at many colleges because these schools believe that a good grade on this test increases the probability that the student will do well at the college. Linear regression can establish the predictive validity of a test. Construct validity. Does the test measure the construct it is intended to measure? Establishing construct validity can be a difficult task when the constructs being measured are abstract. But it can be established by conducting a number of studies in which you test hypotheses regarding the construct, or by completing a factor analysis to ensure that you have the number of constructs that you say you have. In addition to ensuring the validity of instruments, the quantitative researcher needs to establish their reliability as well. Strategies for establishing reliability include Test retest. Correlates scores from two different administrations of the same test. Alternate forms. Correlates scores from administrations of two different forms of the same test. Split half reliability. Treats each half of one test or survey as a separate administration and correlates the results from each. Internal consistency. Uses Cronbach's coefficient alpha to calculate the average of all possible split halves. Quantitative research almost always relies on a sample that is intended to be representative of a larger population. There are two basic sampling strategies, random and non-random, and a number of specific strategies within each of these approaches. This table provides examples of each of the major strategies. The next section of this tutorial provides an overview of the procedures in conducting quantitative data analysis. There are specific procedures for conducting the data collection, preparing for and analyzing data, presenting the findings, and connecting to the body of existing research. This process ensures that the research is conducted as a systematic investigation that leads to credible results. Data comes in various sizes and shapes, and it is important to know about these so that the proper analysis can be used on the data. In 1946, S.S. Stevens first described the properties of measurement systems that allowed decisions about the type of measurement and about the attributes of objects that are preserved in numbers. These four types of data are referred to as nominal, ordinal, interval, and ratio. First, let's examine nominal data. With nominal data, there is no number value that indicates quantity. Instead, a number has been assigned to represent a certain attribute, like the number 1 to represent male and the number 2 to represent female. In other words, the number is just a label. You could also assign numbers to represent race, religion, or any other categorical information. Nominal data only denotes group membership. With ordinal data, there is again no indication of quantity. Rather, a number is assigned for ranking order. For example, satisfaction surveys often ask respondents to rank order their level of satisfaction with services or programs. The next level of measurement is interval data. With interval data, there are equal distances between two values, but there is no natural zero. A common example is the Fahrenheit temperature scale. Differences between the temperature measurements make sense, but ratios do not. For instance, 20 degrees Fahrenheit is not twice as hot as 10 degrees Fahrenheit. You can add and subtract interval level data, but they cannot be divided or multiplied. Finally, we have ratio data. Ratio is the same as interval, however ratios, means, averages, and other numerical formulas are all possible and make sense. Zero has a logical meaning, which shows the absence of, or having none of. Examples of ratio data are height, weight, speed, or any quantities based on a scale with a natural zero. In summary, nominal data can only be counted. Ordinal data can be counted and ranked. Interval data can also be added and subtracted, and ratio data can also be used in ratios and other calculations. Determining what type of data you have is one of the most important aspects of quantitative analysis. Depending on the research question, hypotheses, and research design, the researcher may choose to use descriptive and or inferential statistics to begin to analyze the data. Descriptive statistics are best illustrated when viewed through the lens of America's pastimes. Sports, weather, economy, stock market, and even our retirement portfolio are presented in a descriptive analysis. Basic terminology for descriptive statistics are terms that we are most familiar in this discipline. Frequency, mean, median, mode, range, variance, and standard deviation. Simply put, you are describing the data. Some of the most common graphic representations of data are bar graphs, pie graphs, histograms, and box and whisker graphs. Attempting to reach conclusions and make causal inferences beyond graphic representations or descriptive analyses is referred to as inferential statistics. In other words, examining the college enrollment of the past decade in a certain geographical region would assist in estimating what the enrollment for the next year might be. Frequently in education, the means of two or more groups are compared. When comparing means to assist in answering a research question, one can use a within-group, between-groups, or mixed-subject design. In a within-group design, the researcher compares measures of the same subjects across time, therefore within-group, or under different treatment conditions. This can also be referred to as a dependent-group design. The most basic example of this type of quasi-experimental design would be if a researcher conducted a pretest of a group of students, subjected them to a treatment, and then conducted a post-test. The group has been measured at different points in time. In a between-group design, subjects are assigned to one of the two or more groups. For example, Control, Treatment 1, Treatment 2. Ideally, the sampling and assignment to groups would be random, which would make this an experimental design. The researcher can then compare the means of the treatment group to the control group. When comparing two groups, the researcher can gain insight into the effects of the treatment. In a mixed-subjects design, the researcher is testing for significant differences between two or more independent groups while subjecting them to repeated measures. Choosing a statistical test to compare groups depends on the number of groups, whether the data are nominal, ordinal, or interval, and whether the data meet the assumptions for parametric tests. Nonparametric tests are typically used with nominal and ordinal data, while parametric tests use interval and ratio-level data. In addition to this, some further assumptions are made for parametric tests that the data are normally distributed in the population, that participant selection is independent, and the selection of one person does not determine the selection of another, and that the variances of the groups being compared are equal. The assumption of independent participant selection cannot be violated, but the others are more flexible. The t-test assesses whether the means of two groups are statistically different from each other. This analysis is appropriate whenever you want to compare the means of two groups, and especially appropriate as the method of analysis for a quasi-experimental design. When choosing a t-test, the assumptions are that the data are parametric. The analysis of variance, or ANOVA, assesses whether the means of more than two groups are statistically different from each other. When choosing an ANOVA, the assumptions are that the data are parametric. The chi-square test can be used when you have non-parametric data and want to compare differences between groups. The Kruskal-Wallis test can be used when there are more than two groups and the data are non-parametric. Correlation analysis is a set of statistical tests to determine whether there are linear relationships between two or more sets of variables from the same list of items or individuals, for example, achievement and performance of students. The tests provide a statistical yes or no as to whether a significant relationship or correlation exists between the variables. A correlation test consists of calculating a correlation coefficient between two variables. Again, there are parametric and non-parametric choices based on the assumptions of the data. Pearson R correlation is widely used in statistics to measure the strength of the relationship between linearly related variables. Spearman-Rank correlation is a non-parametric test that is used to measure the degree of association between two variables. Spearman-Rank correlation test does not assume any assumptions about the distribution. Spearman-Rank correlation test is used when the Pearson test gives misleading results. Often a Kendall-Taw is also included in this list of non-parametric correlation tests to examine the strength of the relationship if there are less than 20 rankings. Linear regression and correlation are similar and often confused. Sometimes your methodologist will encourage you to examine both the calculations. Calculate linear correlation if you measured both variables, x and y. Make sure to use the Pearson parametric correlation coefficient if you are certain you are not violating the test assumptions. Otherwise, choose the Spearman non-parametric correlation coefficient. If either variable has been manipulated using an intervention, do not calculate a correlation. While linear regression does indicate the nature of the relationship between two variables, like correlation, it can also be used to make predictions because one variable is considered explanatory while the other is considered a dependent variable. Establishing validity is a critical part of quantitative research. As with the nature of quantitative research, there is a defined approach or process for establishing validity. This also allows for the findings transferability. For a study to be valid, the evidence must support the interpretations of the data, the data must be accurate, and their use in drawing conclusions must be logical and appropriate. Construct validity concerns whether what you did for the program was what you wanted to do, or whether what you observed was what you wanted to observe. Construct validity concerns whether the operationalization of your variables are related to the theoretical concepts you are trying to measure. Are you actually measuring what you want to measure? Internal validity means that you have evidence that what you did in the study, i.e., the program, caused what you observed, i.e., the outcome, to happen. Conclusion validity is the degree to which conclusions drawn about relationships in the data are reasonable. External validity concerns the process of generalizing, or the degree to which the conclusions in your study would hold for other persons in other places and at other times. Establishing reliability and validity to your study is one of the most critical elements of the research process. Once you have decided to embark upon the process of conducting a quantitative study, use the following steps to get started. First, review research studies that have been conducted on your topic to determine what methods were used. Consider the strengths and weaknesses of the various data collection and analysis methods. Next, review the literature on quantitative research methods. Every aspect of your research has a body of literature associated with it. Just as you would not confine yourself to your course textbooks for your review of research on your topic, you should not limit yourself to your course texts for your review of methodological literature. Read broadly and deeply from the scholarly literature to gain expertise in quantitative research. Additional self-paced tutorials have been developed on different methodologies and techniques associated with quantitative research. Make sure that you complete all of the self-paced tutorials and review them as often as needed. You will then be prepared to complete a literature review of the specific methodologies and techniques that you will use in your study. Thank you for watching.

• Open access
• Published: 02 September 2024

Empathy ability and influencing factors among pediatric residents in China: a mixed-methods study

• Pingping Li 1   na1 ,
• Ling Weng 2   na1 &
• Lu Dong 1  

BMC Medical Education volume  24 , Article number:  955 ( 2024 ) Cite this article

Metrics details

Empathy is one of the fundamental factors enhancing the therapeutic effects of physician–patient relationships, but there has been no relevant research in China on the pediatric resident physicians’ capacity for empathy or the influencing factors.

A mixed-methods study was undertaken. The student version of the Jefferson Scale of Empathy was used to assess 181 postgraduate residents at Shanghai Children’s Medical Center and Shanghai Children’s Hospital. Differences in empathy ability among pediatric resident physicians of different genders and specialties were analyzed using independent sample t-tests and Mann–Whitney U tests. A one-way analysis of variance was used to analyze the differences in empathy ability at different educational levels and years of medical residency training. Seven third-year postgraduate pediatric residents from Shanghai Children’s Medical Center participated in semi-structured interviews exploring the influencing factors. We analyzed the interview transcripts using thematic analysis.

The scale was completed by 154 pediatric residents. No statistically significant differences in empathy were found between educational level, postgraduate year, gender, or specialty. The factors influencing empathy in doctor–patient communication included the person who accompanied the child to see the doctor, how the children cooperated with doctors for medical treatment, the volume of pediatric outpatient and emergency visits, and the physician’s ability to withstand pressure. All interviewed resident physicians regarded learning empathy as important but rarely spent extra time learning it.

Conclusions

The evaluation results of resident physicians on changes in empathy after improving clinical abilities vary according to their understanding of empathy, and the work environment has an important impact on pediatricians’ empathy ability. Their empathy score is relatively low, and this requires exploration and intervention.

Peer Review reports

There has been a long-standing tension in the physician–patient relationship in pediatric clinics in China [ 1 ]. There are complex reasons for this, but research has found that 80% of doctor–patient disputes result from poor communication, often due to a lack of empathy during interactions [ 2 , 3 ]. The current medical literature defines empathy as the ability to understand the patient’s perspective and feelings, as well as sharing and acting on this understanding during interpersonal interactions [ 4 ]. Studies show that empathy is linked with enhanced patient satisfaction and treatment compliance [ 5 ]. High levels of empathy in healthcare professionals are connected to positive clinical prognoses for patients by reducing mental stress, improving self-awareness, and reducing anxiety and depression [ 6 , 7 ].

Residency training is mandatory for doctors to qualify to practice independently [ 8 ]. In China, standardized residency training began nationwide in 2013; seven government ministries jointly issued the policy document, “Guidance on the Establishment of a Standardized Residency Training System” [ 9 ]. All clinicians, including pediatricians, are required to undergo three-year residency training after graduating from medical school. During these three years, residents study in different departments.

The Chinese Medical Doctor Association recommends six core competencies for medical residents based on the content and standards for standardized residency training (2022 version): professionalism, clinical professionalism, managing patients, communication, teaching, and learning. While professionalism necessarily involves knowledge and skill, the unique characteristic of medical professionalism is empathy [ 10 ], a capacity that is also strongly related to communication. Thus, cultivating empathy is important for medical residents.

The student version of the Jefferson Scale of Empathy (JSE-S) was specifically developed as a self-report scale for the assessment of empathy in medical students [ 11 , 12 ]. Some studies have reported a decline in empathy among medical students [ 13 , 14 , 15 ], while some have noted that students in their final year scored higher for empathy than did first-year medical students [ 16 , 17 ] and others have reported little change in empathy scores across the years [ 18 ]. However, there is little comparable research for China.

Some studies have shown that the work environment can affect the development of empathy [ 19 ], and pediatric departments recorded a high incidence of doctor–patient disputes [ 20 ]. According to the 2019 National Medical Injury Liability Dispute Case Big Data Report, pediatrics is a high-risk area for doctor–patient disputes.

Therefore, this study aimed to analyze whether there are differences in the ability to empathize among pediatric resident physicians of different grades and whether the pediatric medical environment affects that ability. A mixed-methods approach was used: We assessed empathy scores using the JSE-S and then conducted a semi-structured survey to discuss the influencing factors.

Study design

Quantitative and qualitative methodologies were used to analyze empathy and influencing factors among pediatric residents, incorporating a survey for the quantitative analysis and interviews for the qualitative assessment.

Quantitative methodology

Data collection: survey.

In July 2023, all residents of the Shanghai Children’s Medical Center, affiliated with Shanghai Jiao Tong University School of Medicine, and the Children’s Hospital affiliated with Shanghai Jiao Tong University School of Medicine, were surveyed using an anonymous online questionnaire. Informed consent was obtained from all participants. The survey was available online for one week, and after three days, the residents were sent reminders via WeChat by staff members from the two hospitals.

The JSE-S was used in this study [ 21 ] The scale consists of 20 items, measured using a seven-point Likert scale ranging from 1 = completely disagree to 7 = completely agree but with items 1, 3, 6, 7, 8, 11, 12, 14, 18, and 19 reverse scored. The total score of the scale comprises the total score for all items, with higher scores indicating higher levels of empathy. The scale is subdivided into three dimensions: perspective-taking, compassionate care, and standing in the patient’s shoes [ 12 , 21 ]. The maximum score on the JSE is 140, and the minimum score is 20. Other data collected as part of the JSE survey included sex and years of medical resident training, specialty, and education.

Data analysis

Independent samples t-tests were performed to assess differences in mean JSE scores between sexes. The Mann–Whitney U test was used to compare the differences in mean JSE scores between specialties. A one-way analysis of variance (ANOVA) was performed to compare the differences between the different years of medical residency training and different levels of education. All analyses were performed using the IBM SPSS Statistics Version 25.0. The data are presented as mean ± standard deviation (SD) unless otherwise stated.

Qualitative methods

Data collection: interviews.

As the third-year postgraduate (PGY3) pediatric residents who entered standardized training for pediatric resident physicians in 2020 had completed their training, in August 2023, PGY3 pediatric residents at the Shanghai Children’s Medical Center were asked to participate in the interviews. Seven consented to participate (Table  1 ).

Two researchers (LPP and WL) conducted individual face-to-face semi-structured interviews. The interviews lasted 50–70 min (60-minute average) and were audio recorded and transcribed verbatim by a professional service. The interview guide (Table  2 ) included three aspects: work environment, residents’ standardized training, and open questions. The open-ended questions explored the most memorable cases of smooth and unsmooth communication with patients.

During the interviews, the research followed the guidelines of the interview outline and interviewees’ actual situations. The order and method of questioning were adjusted according to the context and the value of the questions. The language used by the interviewees was accepted without judgment, and no inducements or interventions were made. To protect the privacy of the respondents, their names have been replaced by numbers.

In accordance with a constructivist approach, the analyses tapped into the sense that the participants made of their experiences of communicating with patients. Inductive thematic analysis [ 22 ] was used to identify themes. The interviews were audio recorded and transcribed verbatim by a professional service (iFLYTEK). WL and LPP read and reread transcripts for immersion and familiarization. Two authors (WL and LPP) iteratively coded the data deemed relevant to the current study using Nvivo14 [ 23 ]. Disagreements were discussed with another author (DL). The next step was to group related codes into potential themes. Subsequently, three authors (LPP, WL, and DL) jointly reviewed the themes to ensure that the codes in each theme were coherent and that the codes in different themes could be clearly distinguished.

Quantitative research results

Study population characteristics.

In total, 154 residents responded to the survey, a response rate of 85.1% (154/181). The participating pediatric residents included 60 (39.0%) residents from postgraduate year 1 (PGY1), 48 (31.1%) from postgraduate year 2 (PGY2), and 46 (29.9%) from PGY3. A total of 111 participants (72.1%) were women, and 43 (27.9%) were men. A total of 112 (72.7%) participants were pediatric residents, and 42 (27.3%) were pediatric surgery residents. There were 63 (40.9%) undergraduate residents, 69 (44.8%) master’s residents, and 22 (14.3%) doctoral degree residents in this study. The mean JSE-S score for the overall study population was 81.41 ± 5.43.

Based on the independent samples t-test and Mann–Whitney test, we found no differences in pediatrics’ sex (t = 0.878, p  = 0.381) or specialty (z=-0.981, p  = 0.327).

The education levels of different residents were not significantly different (f = 1.455, p  = 0.237) (Table  3 ).

Empathy competencies of pediatric residents with different pediatric standardized training years

The empathetic recognition mean JSE-S score was 81.41 ± 5.43. Compared to PGY1 (81.33 ± 4.45) and PGY2 (80.75 ± 4.08), PGY3 had a high JSE-S score (82.2 ± 7.48), but there were no significant differences between different years of medical residency training (f = 0.839, p  = 0.434) (Table  4 ).

In the perspective-taking scale, the mean JSE-S score was 54.66 ± 6.70, and the one-way ANOVA revealed significant differences between PGYs (f = 3.51, p  = 0.032). There were significant differences between PGYs for three items: “Physicians’ understanding of the emotional status of their patients, and that of their families is an important component of the physician–patient relationship” (f = 4.391, p  = 0.014); “Physicians should try to stand in their patients’ shoes when providing care to them” (f = 4.697, p  = 0.010); and “I believe that empathy is an important therapeutic factor in medical treatment” (f = 250.996, p  = 0.000).

The mean JSE-S score on the compassionate care scale was 20.76 ± 5.97. PYG1, PYG2, and PYG3 scored 22.42 ± 4.48, 19.42 ± 6.17, and 20.00 ± 7.00, respectively, indicating significant differences between them (f = 4.053, p  = 0.019). Significant differences were found for years of pediatric residency training for “Physicians should not allow themselves to be influenced by strong personal bonds between their patients (f = 40.158, p = 0.000) and their family members” and “I do not enjoy reading non-medical literature or the arts.” (f = 37.236, p  = 0.000).

The standing in the patient’s shoes dimension of the JSE-S showed no significant differences between the PGYs.

Qualitative research results

The influence of pediatric visiting environment on physicians’ empathy ability.

Because children are unable to express their discomfort or illness well, they should be accompanied by parents or grandparents when attending hospital. Doctors, therefore, have to communicate with the parents or grandparents, and their circumstances, including their education level, familiarity with the child, physical health status, communication and understanding skills, and attitude toward doctors, can affect empathy between doctors and patients.

Compared to adult hospitals , the empathy ability of doctors in children’s hospitals may be slightly reduced because we are dealing with parents , not patients themselves , and many of them are brought for treatment by elderly people. Elderly people do not understand the child’s disease or may have difficulty hearing clearly , which can greatly affect communication , let alone empathy. (P1, M) Some elderly people may regard their children’s condition unnecessarily seriously , resulting in us not being able to understand the symptoms of the child properly. (P2, F) Parents tend to have a good understanding of the child’s condition. If grandparents with a low education or if other relatives bring them over , the process of consultation may not be very smooth. (P3, F) The child might be brought over on the first day of treatment by their parents but subsequently by older relatives. Because the child is still running a fever for two or three days , they will be very anxious. When they communicate this to us , their attitude is often poor. (P4, M) If an elderly person brings a child to see a doctor , I often ask the elderly person to call the parents on the spot so I can listen to them. It is better this way. (P7, M)

Some resident physicians said that the language of the patients’ parents significantly impacted their ability to empathize:

Because I am not from Shanghai and grandparents who accompany their children may speak the local dialect , we are unable to communicate. This is challenging for me and many colleagues because most of us cannot understand the Shanghai dialect. (P2, F)

The child’s upbringing and willingness to cooperate with treatment were also identified as important:

Some parents may spoil their children , some children start acting spoiled as soon as they arrive at the clinic , and some even make a scene , which can interfere with the medical treatment. (P2, F)

The volume of pediatric outpatient and emergency visits and the self-regulation ability of physicians facing strong workloads can also affect communication and empathy between doctors and patients:

Outpatient hours may limit our communication with patients. Generally , you need to finish one within 5–10 min. Otherwise , the patient’s visit may be too long , and you may not be able to see all registered patients before leaving work. For example , last summer , our two doctors saw an average of around 130–150 patients a day , while I saw an average of 80–90 patients per day. That was during the pandemic last year , and there will definitely be more this year. (P7, M) The doctor is very tired and has a large number of patients. If the patients are in a hurry , you need to see them within a short period. If our resident physician’s self-regulation ability is not good , it will affect communication. (P5, M)

Standardized training for resident physicians to cultivate empathy skills

The three resident physicians interviewed believed that in their first year of participating in standardized resident training, they felt more empathy for patients due to their lack of clinical knowledge. By contrast, after three years of clinical practice and improvements in their clinical knowledge, they viewed the patient’s condition more rationally and from a medical perspective.

Because you have learned systematic knowledge about diseases , you know what the likely outcome will be objectively. Consequently , your empathy regarding the intermediate treatment process and patients may decrease , and you have to think about the treatment from a doctor’s professional perspective. (P2, F) When I first entered standardized training for resident physicians , I lacked clinical experience and was not familiar with the treatment process for many diseases. When I encountered critically ill patients , I felt that they were so pitiful. After three years of training , however , these diseases have become more familiar. I know the treatment processes for each disease and feel that empathy has decreased. (P3, F)

The two residents felt that empathy followed a curved path. Residents who have just entered clinical practice have relatively high empathy. However, as their clinical abilities and understanding of diseases increase, coupled with the busy workload of clinical work, their empathy decreases. However, empathy may improve after becoming a physician.

When I went to the outpatient clinic with my supervisor , I felt that my supervisor , who was already a chief physician , had reached a very high level of empathy. I think his empathy ability was much stronger than mine; that is , regardless of the patient’s attitude , he could think from the patient’s perspective. As a resident physician , I still cannot reach the level of empathy that my supervisor possesses. Perhaps I need to acquire some experience in my career to reach the level of empathy that my supervisor possesses , but the process may be a bit complex. (P2, F) As a physician , I think that empathy is a curved process , initially high , but as your clinical abilities improve and work experience increases , empathy may decrease. The attending physician is very busy , and at some point , the value of empathy may be underestimated , but it increases again with age. Perhaps at a certain point or stage , you suddenly feel it is important , and you become very focused on the ability to empathize. (P3, F)

Two interviewees believed that after three years of standardized training for resident physicians, their empathy skills had improved. Three years ago, they only thought about the disease. Today, they are able to think from the perspective of the patient and stand in their shoes.

For example , parents who come to the surgical emergency department are very anxious. As a physician , I can understand their feelings. Some common diseases that you have seen before have a likely trajectory. Although you are also anxious about their diseases , you know how to treat different disease symptoms and have the ability to handle them. I know why parents are anxious , and I can think from their perspective. (P4, F) As you gain an understanding of diseases and as your own abilities and clinical experience improve , your feelings toward the patient change. Because I know how a disease like Mycoplasma pneumonia , for example , develops , when I was in PGY1 , I felt that the child’s cough was very severe , which made the parents very anxious. At the time , I was also quite anxious. Now , however , I know that the course of this disease is long. If parents are very anxious , I will explain this disease to them and comfort them. I have had more contact with patients , and I will consider the problem more from their perspective. (P6, F)

Cultivating residents’ empathy ability during standardized resident training

Self-study: The residents believed it important to learn theories relevant to doctor–patient communication and empathy. The interviews revealed that most of them improved their communication skills in clinical practice, and a few residents spent time studying how to communicate with patients. Only one student bought a book about communication, and one student paid attention to the ability to communicate with patients because they had to take an exam on doctor–patient communication.

When I was admitted for training , there was a medical teacher talking about doctor–patient disputes , which was quite scary at the time. I bought relevant books but did not read them. (P1, M) I have not bought any books related to doctor–patient communication , but I think in clinical practice , it is necessary to participate more in the conversation process with superiors , listen more to their conversations , listen more to how they communicate with patients , and then try to learn how to better communicate with patients on my own. (P2, F) This year’s standardized training and graduation assessment for resident physicians added an assessment of doctor–patient communication. I have paid attention to this knowledge , but I have not delved into it. (P3, F)

Training course: It is necessary to set courses to cultivate residents’ empathy ability, such as theoretical training courses, case-sharing groups, and scenario simulations.

I think it’s necessary to set courses for residents to teach us how to communicate , how to express the appropriate level of empathy to patients , etc. (P1, M) I think theoretical teaching in this area is possible , but it cannot be a single output of this teaching mode. Instead , we could hold some doctor–patient communication and sharing meetings , where residents or specialists could share their cases in clinical work and learn from each other . (P3, F) Maybe establish some scenario simulation courses for training. (P5, M)

Sharing the most memorable cases during resident training

Due to the fact that resident physicians undergo rotational training in different clinical departments over 3 years, clinical departments, patient situations, work environments, and severity of diseases may vary. By conducting interviews with resident physicians during the training period, the factors that affect the empathy ability of resident physicians can be further explored by allowing them to profoundly impact the departments where communication with patients is not smooth or smooth. The results are shown in Table  5 .

Clinical empathy and number of years of standardized training

Some studies have shown that empathy scores are associated with ratings of clinical competence [ 24 ]. From the results of the questionnaire survey, the JSE-S scores of PGY1, PGY2, and PGY3 showed no significant differences. From the interview results, seven respondents compared the changes in their empathy skills between the beginning and completion of the standardized resident physician training. Five pediatric resident physicians believed that their empathy skills had decreased with the improvement in their medical skills, while two resident physicians believed that their empathy skills improved after receiving standardized resident physician training. The results of the interviews seem to confirm the results of the questionnaire survey that different physicians have different understandings of the relationship between the improvement of clinical abilities and empathy. These two perspectives may be due to different perspectives on empathy. A resident physician who believes that empathy decreases may believe that the physician’s empathy toward patients is more about the patient’s illness. As their medical abilities improve, they can treat the patient’s illness and believe that it will eventually be cured, so the need for empathy decreases. Some studies have reported that doctors who sympathize with their patients share their suffering, which could lead to emotional fatigue and a lack of objectivity [ 25 ]. However, one resident physician believed empathy had improved by progressing from learning about diseases from books during their medical student stage to the realities of clinical practice, seeing the impact of diseases on patients, families, and even society.

Clinical empathy and the pediatric work environment

Doctor–patient communication in pediatrics is more complex and difficult than when treating adults, meaning that pediatricians bear higher risks. The probability of medical disputes in pediatrics is much higher than in other departments; pediatricians are often insulted and even physically threatened [ 26 ]. Physician empathy is at the heart of doctor–patient communication and significantly influences patient outcomes [ 27 ]. This study explored the factors that influence empathy between pediatricians and patients. In patient terms, the level of cooperation from the child and the characteristics of the person accompanying the child are factors. As for the doctors, they can be confronted with pressure and the need to communicate effectively in the face of high outpatient volumes, which can affect their expressions of empathy, a finding similar to that of previous studies [ 28 , 29 ].

Further analysis of direct doctor–patient communication and empathy among pediatric resident physicians in different rotating departments showed that communication between doctors and patients was seen to be smoother in the Rheumatology and Immunology, General Surgery, and Special Diagnosis Departments, while difficulties were encountered in Outpatients and Emergency, Hematology and Oncology, Surgical Oncology, and Cardiology. The reasons may be complex, but four principal issues can be identified. First, the duration of communication between doctors and patients and the environment of medical treatment; in the Special Diagnosis Department, for example, patients are able to communicate and interact with doctors for a long time, and the medical environment is very good, whereas Outpatients and Emergency see a rapid turnover and high workload. Second, the level of familiarity between patients and physicians can play a role. In Rheumatology and Immunology Departments, for example, there are often patients with chronic diseases who have been hospitalized for a long time; doctors and patients are very familiar with each other, and some studies have shown empathy is easier to generate when closer interpersonal relationships develop [ 30 ]. Third, different teaching methods may have an impact. Better training on the wards can make residents feel more confident in communicating with patients, whereas Outpatients and Emergency can require residents to face patients alone, generating anxiety or even burnout [ 31 ]. Fourth, disease severity can play a role. In some departments, such as Hematology and Oncology, patients may not have a high hope of recovery but may have high expectations of the treatment. This may not only put a lot of pressure on doctors but also make it difficult to communicate effectively with patients; research has indicated that there is still a gap between the actual and expected disclosure of “bad news” about cancer among healthcare workers, patients, and family members, leading to various disclosure dilemmas [ 32 ].

Clinical empathy across different settings

The mean empathy levels found in this study (81.41 ± 5.43) are lower than those reported [ 33 ] in most similar studies around the world. Similar lower JSE scores have been seen in undergraduate medical students in China; the average JSE score among medical students from Sun Yat-sen University was 84 [ 34 ]. This finding is concerning. The shortage of pediatricians, [ 35 ] low wages, [ 36 ] severe occupational burnout, [ 37 ] and the influence of Asian parental culture [ 38 ] may partly explain our findings. Further investigations are required to determine the factors associated with such low scores so that steps can be taken to address the situation.

Cultivating empathy among pediatric residents

Our research shows that resident physicians believe that empathy is important, even though their self-rated empathy scores are less than ideal. Interventions to further investigate the teaching and learning of empathy were discussed [ 39 ]. Many training courses have proven to be beneficial in enhancing the empathy skills of resident physicians. The teaching innovation “How to act-in-role” has been shown to be effective not only in increasing medical students’ self-reported empathy but also in their competence in consultation skills [ 40 ]. The addition of narrative medicine-based education in standardized training improved empathy and may have improved the professional knowledge of residents [ 41 , 42 ] The use of Balint group activities [ 43 ] with residents has shown significant improvements in empathy across all dimensions. Medical schools should design appropriate training courses and implement interventions at all stages (from the admission process to curricula to residency) and levels (explicit and implicit curricula) depending on the empathy levels of their resident physicians.

Our findings suggest that, based on the different understandings of empathy among resident physicians, the clinical empathy level of pediatric resident physicians is not closely related to an improvement in clinical abilities. Rather, the working environment of pediatricians significantly impacts their empathy ability. Empathy is lower among pediatric residents in China when compared to their European counterparts, and further research into the underlying factors associated with such low scores is necessary to plan interventions to cultivate empathy among pediatric residents.

Limitations

One important weakness of this study is that it was based in one medical school with two specialized children’s hospitals; the limited sample size of the investigation and interviews may mean that the study is not representative of pediatric residents in China. Moreover, the cross-sectional survey precluded us from identifying a causal relationship; thus, a prospective longitudinal study with a larger sample size of pediatric residents is warranted.

Data availability

The questionnaire data that support the findings of this study are available in the Baidu Netdisk repository, https://pan.baidu.com/s/1hRjCKuIVVry79HwTzxB_bA with the primary accession code e9hp.The interview datasets analysed during the current study are not publicly available due to privacy concerns but are available from the corresponding author upon reasonable request.

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Acknowledgements

This work was financed by Postgraduate Medical Education Project in 2022 (BYH20220412); The 2022 Science and Technology Innovation Project (Humanities and Social Sciences) Project of Shanghai Jiao Tong University School of Medicine (WK2217); Fujian Medical University Education Reform Project: Application Research on the Intelligent Teaching Platform for Clinical Teachers under the Background of “New Medical Science” (J22021).

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Pingping Li and Ling Weng contributed equally to this work and should be considered co-first authors.

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Department of Pediatric Clinical Medicine School, Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China

Pingping Li & Lu Dong

Department of Science and Education, Fujian Maternity and Child Health Hospital, Fujian, 350000, China

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L.P.P. conceptualized the idea of this study. L.P.P. and W.L. contributed to design of the project and survey preparation and dissemination. L.P.P. contributed to investigate. D.L. contributed to writing-review and agreed to be accountable for all aspects of the work. All authors reviewed the manuscript.

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Li, P., Weng, L. & Dong, L. Empathy ability and influencing factors among pediatric residents in China: a mixed-methods study. BMC Med Educ 24 , 955 (2024). https://doi.org/10.1186/s12909-024-05858-5

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• Zhigang Hu 1 ,
• Yuexuan Wu 1 &
• Chengliang Wang   ORCID: orcid.org/0000-0003-2208-3508 2  

Humanities and Social Sciences Communications volume  11 , Article number:  1115 ( 2024 ) Cite this article

Metrics details

• Science, technology and society

The rapid expansion of information technology and the intensification of population aging are two prominent features of contemporary societal development. Investigating older adults’ acceptance and use of technology is key to facilitating their integration into an information-driven society. Given this context, the technology acceptance of older adults has emerged as a prioritized research topic, attracting widespread attention in the academic community. However, existing research remains fragmented and lacks a systematic framework. To address this gap, we employed bibliometric methods, utilizing the Web of Science Core Collection to conduct a comprehensive review of literature on older adults’ technology acceptance from 2013 to 2023. Utilizing VOSviewer and CiteSpace for data assessment and visualization, we created knowledge mappings of research on older adults’ technology acceptance. Our study employed multidimensional methods such as co-occurrence analysis, clustering, and burst analysis to: (1) reveal research dynamics, key journals, and domains in this field; (2) identify leading countries, their collaborative networks, and core research institutions and authors; (3) recognize the foundational knowledge system centered on theoretical model deepening, emerging technology applications, and research methods and evaluation, uncovering seminal literature and observing a shift from early theoretical and influential factor analyses to empirical studies focusing on individual factors and emerging technologies; (4) moreover, current research hotspots are primarily in the areas of factors influencing technology adoption, human-robot interaction experiences, mobile health management, and aging-in-place technology, highlighting the evolutionary context and quality distribution of research themes. Finally, we recommend that future research should deeply explore improvements in theoretical models, long-term usage, and user experience evaluation. Overall, this study presents a clear framework of existing research in the field of older adults’ technology acceptance, providing an important reference for future theoretical exploration and innovative applications.

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Introduction.

In contemporary society, the rapid development of information technology has been intricately intertwined with the intensifying trend of population aging. According to the latest United Nations forecast, by 2050, the global population aged 65 and above is expected to reach 1.6 billion, representing about 16% of the total global population (UN 2023 ). Given the significant challenges of global aging, there is increasing evidence that emerging technologies have significant potential to maintain health and independence for older adults in their home and healthcare environments (Barnard et al. 2013 ; Soar 2010 ; Vancea and Solé-Casals 2016 ). This includes, but is not limited to, enhancing residential safety with smart home technologies (Touqeer et al. 2021 ; Wang et al. 2022 ), improving living independence through wearable technologies (Perez et al. 2023 ), and increasing medical accessibility via telehealth services (Kruse et al. 2020 ). Technological innovations are redefining the lifestyles of older adults, encouraging a shift from passive to active participation (González et al. 2012 ; Mostaghel 2016 ). Nevertheless, the effective application and dissemination of technology still depends on user acceptance and usage intentions (Naseri et al. 2023 ; Wang et al. 2023a ; Xia et al. 2024 ; Yu et al. 2023 ). Particularly, older adults face numerous challenges in accepting and using new technologies. These challenges include not only physical and cognitive limitations but also a lack of technological experience, along with the influences of social and economic factors (Valk et al. 2018 ; Wilson et al. 2021 ).

User acceptance of technology is a significant focus within information systems (IS) research (Dai et al. 2024 ), with several models developed to explain and predict user behavior towards technology usage, including the Technology Acceptance Model (TAM) (Davis 1989 ), TAM2, TAM3, and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al. 2003 ). Older adults, as a group with unique needs, exhibit different behavioral patterns during technology acceptance than other user groups, and these uniquenesses include changes in cognitive abilities, as well as motivations, attitudes, and perceptions of the use of new technologies (Chen and Chan 2011 ). The continual expansion of technology introduces considerable challenges for older adults, rendering the understanding of their technology acceptance a research priority. Thus, conducting in-depth research into older adults’ acceptance of technology is critically important for enhancing their integration into the information society and improving their quality of life through technological advancements.

Reviewing relevant literature to identify research gaps helps further solidify the theoretical foundation of the research topic. However, many existing literature reviews primarily focus on the factors influencing older adults’ acceptance or intentions to use technology. For instance, Ma et al. ( 2021 ) conducted a comprehensive analysis of the determinants of older adults’ behavioral intentions to use technology; Liu et al. ( 2022 ) categorized key variables in studies of older adults’ technology acceptance, noting a shift in focus towards social and emotional factors; Yap et al. ( 2022 ) identified seven categories of antecedents affecting older adults’ use of technology from an analysis of 26 articles, including technological, psychological, social, personal, cost, behavioral, and environmental factors; Schroeder et al. ( 2023 ) extracted 119 influencing factors from 59 articles and further categorized these into six themes covering demographics, health status, and emotional awareness. Additionally, some studies focus on the application of specific technologies, such as Ferguson et al. ( 2021 ), who explored barriers and facilitators to older adults using wearable devices for heart monitoring, and He et al. ( 2022 ) and Baer et al. ( 2022 ), who each conducted in-depth investigations into the acceptance of social assistive robots and mobile nutrition and fitness apps, respectively. In summary, current literature reviews on older adults’ technology acceptance exhibit certain limitations. Due to the interdisciplinary nature and complex knowledge structure of this field, traditional literature reviews often rely on qualitative analysis, based on literature analysis and periodic summaries, which lack sufficient objectivity and comprehensiveness. Additionally, systematic research is relatively limited, lacking a macroscopic description of the research trajectory from a holistic perspective. Over the past decade, research on older adults’ technology acceptance has experienced rapid growth, with a significant increase in literature, necessitating the adoption of new methods to review and examine the developmental trends in this field (Chen 2006 ; Van Eck and Waltman 2010 ). Bibliometric analysis, as an effective quantitative research method, analyzes published literature through visualization, offering a viable approach to extracting patterns and insights from a large volume of papers, and has been widely applied in numerous scientific research fields (Achuthan et al. 2023 ; Liu and Duffy 2023 ). Therefore, this study will employ bibliometric methods to systematically analyze research articles related to older adults’ technology acceptance published in the Web of Science Core Collection from 2013 to 2023, aiming to understand the core issues and evolutionary trends in the field, and to provide valuable references for future related research. Specifically, this study aims to explore and answer the following questions:

RQ1: What are the research dynamics in the field of older adults’ technology acceptance over the past decade? What are the main academic journals and fields that publish studies related to older adults’ technology acceptance?

RQ2: How is the productivity in older adults’ technology acceptance research distributed among countries, institutions, and authors?

RQ3: What are the knowledge base and seminal literature in older adults’ technology acceptance research? How has the research theme progressed?

RQ4: What are the current hot topics and their evolutionary trajectories in older adults’ technology acceptance research? How is the quality of research distributed?

Methodology and materials

Research method.

In recent years, bibliometrics has become one of the crucial methods for analyzing literature reviews and is widely used in disciplinary and industrial intelligence analysis (Jing et al. 2023 ; Lin and Yu 2024a ; Wang et al. 2024a ; Xu et al. 2021 ). Bibliometric software facilitates the visualization analysis of extensive literature data, intuitively displaying the network relationships and evolutionary processes between knowledge units, and revealing the underlying knowledge structure and potential information (Chen et al. 2024 ; López-Robles et al. 2018 ; Wang et al. 2024c ). This method provides new insights into the current status and trends of specific research areas, along with quantitative evidence, thereby enhancing the objectivity and scientific validity of the research conclusions (Chen et al. 2023 ; Geng et al. 2024 ). VOSviewer and CiteSpace are two widely used bibliometric software tools in academia (Pan et al. 2018 ), recognized for their robust functionalities based on the JAVA platform. Although each has its unique features, combining these two software tools effectively constructs mapping relationships between literature knowledge units and clearly displays the macrostructure of the knowledge domains. Particularly, VOSviewer, with its excellent graphical representation capabilities, serves as an ideal tool for handling large datasets and precisely identifying the focal points and hotspots of research topics. Therefore, this study utilizes VOSviewer (version 1.6.19) and CiteSpace (version 6.1.R6), combined with in-depth literature analysis, to comprehensively examine and interpret the research theme of older adults’ technology acceptance through an integrated application of quantitative and qualitative methods.

Data source

Web of Science is a comprehensively recognized database in academia, featuring literature that has undergone rigorous peer review and editorial scrutiny (Lin and Yu 2024b ; Mongeon and Paul-Hus 2016 ; Pranckutė 2021 ). This study utilizes the Web of Science Core Collection as its data source, specifically including three major citation indices: Science Citation Index Expanded (SCIE), Social Sciences Citation Index (SSCI), and Arts & Humanities Citation Index (A&HCI). These indices encompass high-quality research literature in the fields of science, social sciences, and arts and humanities, ensuring the comprehensiveness and reliability of the data. We combined “older adults” with “technology acceptance” through thematic search, with the specific search strategy being: TS = (elder OR elderly OR aging OR ageing OR senile OR senior OR old people OR “older adult*”) AND TS = (“technology acceptance” OR “user acceptance” OR “consumer acceptance”). The time span of literature search is from 2013 to 2023, with the types limited to “Article” and “Review” and the language to “English”. Additionally, the search was completed by October 27, 2023, to avoid data discrepancies caused by database updates. The initial search yielded 764 journal articles. Given that searches often retrieve articles that are superficially relevant but actually non-compliant, manual screening post-search was essential to ensure the relevance of the literature (Chen et al. 2024 ). Through manual screening, articles significantly deviating from the research theme were eliminated and rigorously reviewed. Ultimately, this study obtained 500 valid sample articles from the Web of Science Core Collection. The complete PRISMA screening process is illustrated in Fig. 1 .

Presentation of the data culling process in detail.

Data standardization

Raw data exported from databases often contain multiple expressions of the same terminology (Nguyen and Hallinger 2020 ). To ensure the accuracy and consistency of data, it is necessary to standardize the raw data (Strotmann and Zhao 2012 ). This study follows the data standardization process proposed by Taskin and Al ( 2019 ), mainly executing the following operations:

(1) Standardization of author and institution names is conducted to address different name expressions for the same author. For instance, “Chan, Alan Hoi Shou” and “Chan, Alan H. S.” are considered the same author, and distinct authors with the same name are differentiated by adding identifiers. Diverse forms of institutional names are unified to address variations caused by name changes or abbreviations, such as standardizing “FRANKFURT UNIV APPL SCI” and “Frankfurt University of Applied Sciences,” as well as “Chinese University of Hong Kong” and “University of Hong Kong” to consistent names.

(2) Different expressions of journal names are unified. For example, “International Journal of Human-Computer Interaction” and “Int J Hum Comput Interact” are standardized to a single name. This ensures consistency in journal names and prevents misclassification of literature due to differing journal names. Additionally, it involves checking if the journals have undergone name changes in the past decade to prevent any impact on the analysis due to such changes.

(3) Keywords data are cleansed by removing words that do not directly pertain to specific research content (e.g., people, review), merging synonyms (e.g., “UX” and “User Experience,” “aging-in-place” and “aging in place”), and standardizing plural forms of keywords (e.g., “assistive technologies” and “assistive technology,” “social robots” and “social robot”). This reduces redundant information in knowledge mapping.

Bibliometric results and analysis

Distribution power (rq1), literature descriptive statistical analysis.

Table 1 presents a detailed descriptive statistical overview of the literature in the field of older adults’ technology acceptance. After deduplication using the CiteSpace software, this study confirmed a valid sample size of 500 articles. Authored by 1839 researchers, the documents encompass 792 research institutions across 54 countries and are published in 217 different academic journals. As of the search cutoff date, these articles have accumulated 13,829 citations, with an annual average of 1156 citations, and an average of 27.66 citations per article. The h-index, a composite metric of quantity and quality of scientific output (Kamrani et al. 2021 ), reached 60 in this study.

Trends in publications and disciplinary distribution

The number of publications and citations are significant indicators of the research field’s development, reflecting its continuity, attention, and impact (Ale Ebrahim et al. 2014 ). The ranking of annual publications and citations in the field of older adults’ technology acceptance studies is presented chronologically in Fig. 2A . The figure shows a clear upward trend in the amount of literature in this field. Between 2013 and 2017, the number of publications increased slowly and decreased in 2018. However, in 2019, the number of publications increased rapidly to 52 and reached a peak of 108 in 2022, which is 6.75 times higher than in 2013. In 2022, the frequency of document citations reached its highest point with 3466 citations, reflecting the widespread recognition and citation of research in this field. Moreover, the curve of the annual number of publications fits a quadratic function, with a goodness-of-fit R 2 of 0.9661, indicating that the number of future publications is expected to increase even more rapidly.

A Trends in trends in annual publications and citations (2013–2023). B Overlay analysis of the distribution of discipline fields.

Figure 2B shows that research on older adults’ technology acceptance involves the integration of multidisciplinary knowledge. According to Web of Science Categories, these 500 articles are distributed across 85 different disciplines. We have tabulated the top ten disciplines by publication volume (Table 2 ), which include Medical Informatics (75 articles, 15.00%), Health Care Sciences & Services (71 articles, 14.20%), Gerontology (61 articles, 12.20%), Public Environmental & Occupational Health (57 articles, 11.40%), and Geriatrics & Gerontology (52 articles, 10.40%), among others. The high output in these disciplines reflects the concentrated global academic interest in this comprehensive research topic. Additionally, interdisciplinary research approaches provide diverse perspectives and a solid theoretical foundation for studies on older adults’ technology acceptance, also paving the way for new research directions.

Knowledge flow analysis

A dual-map overlay is a CiteSpace map superimposed on top of a base map, which shows the interrelationships between journals in different domains, representing the publication and citation activities in each domain (Chen and Leydesdorff 2014 ). The overlay map reveals the link between the citing domain (on the left side) and the cited domain (on the right side), reflecting the knowledge flow of the discipline at the journal level (Leydesdorff and Rafols 2012 ). We utilize the in-built Z-score algorithm of the software to cluster the graph, as shown in Fig. 3 .

The left side shows the citing journal, and the right side shows the cited journal.

Figure 3 shows the distribution of citing journals clusters for older adults’ technology acceptance on the left side, while the right side refers to the main cited journals clusters. Two knowledge flow citation trajectories were obtained; they are presented by the color of the cited regions, and the thickness of these trajectories is proportional to the Z-score scaled frequency of citations (Chen et al. 2014 ). Within the cited regions, the most popular fields with the most records covered are “HEALTH, NURSING, MEDICINE” and “PSYCHOLOGY, EDUCATION, SOCIAL”, and the elliptical aspect ratio of these two fields stands out. Fields have prominent elliptical aspect ratios, highlighting their significant influence on older adults’ technology acceptance research. Additionally, the major citation trajectories originate in these two areas and progress to the frontier research area of “PSYCHOLOGY, EDUCATION, HEALTH”. It is worth noting that the citation trajectory from “PSYCHOLOGY, EDUCATION, SOCIAL” has a significant Z-value (z = 6.81), emphasizing the significance and impact of this development path. In the future, “MATHEMATICS, SYSTEMS, MATHEMATICAL”, “MOLECULAR, BIOLOGY, IMMUNOLOGY”, and “NEUROLOGY, SPORTS, OPHTHALMOLOGY” may become emerging fields. The fields of “MEDICINE, MEDICAL, CLINICAL” may be emerging areas of cutting-edge research.

Main research journals analysis

Table 3 provides statistics for the top ten journals by publication volume in the field of older adults’ technology acceptance. Together, these journals have published 137 articles, accounting for 27.40% of the total publications, indicating that there is no highly concentrated core group of journals in this field, with publications being relatively dispersed. Notably, Computers in Human Behavior , Journal of Medical Internet Research , and International Journal of Human-Computer Interaction each lead with 15 publications. In terms of citation metrics, International Journal of Medical Informatics and Computers in Human Behavior stand out significantly, with the former accumulating a total of 1,904 citations, averaging 211.56 citations per article, and the latter totaling 1,449 citations, with an average of 96.60 citations per article. These figures emphasize the academic authority and widespread impact of these journals within the research field.

Research power (RQ2)

Countries and collaborations analysis.

The analysis revealed the global research pattern for country distribution and collaboration (Chen et al. 2019 ). Figure 4A shows the network of national collaborations on older adults’ technology acceptance research. The size of the bubbles represents the amount of publications in each country, while the thickness of the connecting lines expresses the closeness of the collaboration among countries. Generally, this research subject has received extensive international attention, with China and the USA publishing far more than any other countries. China has established notable research collaborations with the USA, UK and Malaysia in this field, while other countries have collaborations, but the closeness is relatively low and scattered. Figure 4B shows the annual publication volume dynamics of the top ten countries in terms of total publications. Since 2017, China has consistently increased its annual publications, while the USA has remained relatively stable. In 2019, the volume of publications in each country increased significantly, this was largely due to the global outbreak of the COVID-19 pandemic, which has led to increased reliance on information technology among the elderly for medical consultations, online socialization, and health management (Sinha et al. 2021 ). This phenomenon has led to research advances in technology acceptance among older adults in various countries. Table 4 shows that the top ten countries account for 93.20% of the total cumulative number of publications, with each country having published more than 20 papers. Among these ten countries, all of them except China are developed countries, indicating that the research field of older adults’ technology acceptance has received general attention from developed countries. Currently, China and the USA were the leading countries in terms of publications with 111 and 104 respectively, accounting for 22.20% and 20.80%. The UK, Germany, Italy, and the Netherlands also made significant contributions. The USA and China ranked first and second in terms of the number of citations, while the Netherlands had the highest average citations, indicating the high impact and quality of its research. The UK has shown outstanding performance in international cooperation, while the USA highlights its significant academic influence in this field with the highest h-index value.

A National collaboration network. B Annual volume of publications in the top 10 countries.

Institutions and authors analysis

Analyzing the number of publications and citations can reveal an institution’s or author’s research strength and influence in a particular research area (Kwiek 2021 ). Tables 5 and 6 show the statistics of the institutions and authors whose publication counts are in the top ten, respectively. As shown in Table 5 , higher education institutions hold the main position in this research field. Among the top ten institutions, City University of Hong Kong and The University of Hong Kong from China lead with 14 and 9 publications, respectively. City University of Hong Kong has the highest h-index, highlighting its significant influence in the field. It is worth noting that Tilburg University in the Netherlands is not among the top five in terms of publications, but the high average citation count (130.14) of its literature demonstrates the high quality of its research.

After analyzing the authors’ output using Price’s Law (Redner 1998 ), the highest number of publications among the authors counted ( n  = 10) defines a publication threshold of 3 for core authors in this research area. As a result of quantitative screening, a total of 63 core authors were identified. Table 6 shows that Chen from Zhejiang University, China, Ziefle from RWTH Aachen University, Germany, and Rogers from Macquarie University, Australia, were the top three authors in terms of the number of publications, with 10, 9, and 8 articles, respectively. In terms of average citation rate, Peek and Wouters, both scholars from the Netherlands, have significantly higher rates than other scholars, with 183.2 and 152.67 respectively. This suggests that their research is of high quality and widely recognized. Additionally, Chen and Rogers have high h-indices in this field.

Knowledge base and theme progress (RQ3)

Research knowledge base.

Co-citation relationships occur when two documents are cited together (Zhang and Zhu 2022 ). Co-citation mapping uses references as nodes to represent the knowledge base of a subject area (Min et al. 2021). Figure 5A illustrates co-occurrence mapping in older adults’ technology acceptance research, where larger nodes signify higher co-citation frequencies. Co-citation cluster analysis can be used to explore knowledge structure and research boundaries (Hota et al. 2020 ; Shiau et al. 2023 ). The co-citation clustering mapping of older adults’ technology acceptance research literature (Fig. 5B ) shows that the Q value of the clustering result is 0.8129 (>0.3), and the average value of the weight S is 0.9391 (>0.7), indicating that the clusters are uniformly distributed with a significant and credible structure. This further proves that the boundaries of the research field are clear and there is significant differentiation in the field. The figure features 18 cluster labels, each associated with thematic color blocks corresponding to different time slices. Highlighted emerging research themes include #2 Smart Home Technology, #7 Social Live, and #10 Customer Service. Furthermore, the clustering labels extracted are primarily classified into three categories: theoretical model deepening, emerging technology applications, research methods and evaluation, as detailed in Table 7 .

A Co-citation analysis of references. B Clustering network analysis of references.

Seminal literature analysis

The top ten nodes in terms of co-citation frequency were selected for further analysis. Table 8 displays the corresponding node information. Studies were categorized into four main groups based on content analysis. (1) Research focusing on specific technology usage by older adults includes studies by Peek et al. ( 2014 ), Ma et al. ( 2016 ), Hoque and Sorwar ( 2017 ), and Li et al. ( 2019 ), who investigated the factors influencing the use of e-technology, smartphones, mHealth, and smart wearables, respectively. (2) Concerning the development of theoretical models of technology acceptance, Chen and Chan ( 2014 ) introduced the Senior Technology Acceptance Model (STAM), and Macedo ( 2017 ) analyzed the predictive power of UTAUT2 in explaining older adults’ intentional behaviors and information technology usage. (3) In exploring older adults’ information technology adoption and behavior, Lee and Coughlin ( 2015 ) emphasized that the adoption of technology by older adults is a multifactorial process that includes performance, price, value, usability, affordability, accessibility, technical support, social support, emotion, independence, experience, and confidence. Yusif et al. ( 2016 ) conducted a literature review examining the key barriers affecting older adults’ adoption of assistive technology, including factors such as privacy, trust, functionality/added value, cost, and stigma. (4) From the perspective of research into older adults’ technology acceptance, Mitzner et al. ( 2019 ) assessed the long-term usage of computer systems designed for the elderly, whereas Guner and Acarturk ( 2020 ) compared information technology usage and acceptance between older and younger adults. The breadth and prevalence of this literature make it a vital reference for researchers in the field, also providing new perspectives and inspiration for future research directions.

Research thematic progress

Burst citation is a node of literature that guides the sudden change in dosage, which usually represents a prominent development or major change in a particular field, with innovative and forward-looking qualities. By analyzing the emergent literature, it is often easy to understand the dynamics of the subject area, mapping the emerging thematic change (Chen et al. 2022 ). Figure 6 shows the burst citation mapping in the field of older adults’ technology acceptance research, with burst citations represented by red nodes (Fig. 6A ). For the ten papers with the highest burst intensity (Fig. 6B ), this study will conduct further analysis in conjunction with literature review.

A Burst detection of co-citation. B The top 10 references with the strongest citation bursts.

As shown in Fig. 6 , Mitzner et al. ( 2010 ) broke the stereotype that older adults are fearful of technology, found that they actually have positive attitudes toward technology, and emphasized the centrality of ease of use and usefulness in the process of technology acceptance. This finding provides an important foundation for subsequent research. During the same period, Wagner et al. ( 2010 ) conducted theory-deepening and applied research on technology acceptance among older adults. The research focused on older adults’ interactions with computers from the perspective of Social Cognitive Theory (SCT). This expanded the understanding of technology acceptance, particularly regarding the relationship between behavior, environment, and other SCT elements. In addition, Pan and Jordan-Marsh ( 2010 ) extended the TAM to examine the interactions among predictors of perceived usefulness, perceived ease of use, subjective norm, and convenience conditions when older adults use the Internet, taking into account the moderating roles of gender and age. Heerink et al. ( 2010 ) adapted and extended the UTAUT, constructed a technology acceptance model specifically designed for older users’ acceptance of assistive social agents, and validated it using controlled experiments and longitudinal data, explaining intention to use by combining functional assessment and social interaction variables.

Then the research theme shifted to an in-depth analysis of the factors influencing technology acceptance among older adults. Two papers with high burst strengths emerged during this period: Peek et al. ( 2014 ) (Strength = 12.04), Chen and Chan ( 2014 ) (Strength = 9.81). Through a systematic literature review and empirical study, Peek STM and Chen K, among others, identified multidimensional factors that influence older adults’ technology acceptance. Peek et al. ( 2014 ) analyzed literature on the acceptance of in-home care technology among older adults and identified six factors that influence their acceptance: concerns about technology, expected benefits, technology needs, technology alternatives, social influences, and older adult characteristics, with a focus on differences between pre- and post-implementation factors. Chen and Chan ( 2014 ) constructed the STAM by administering a questionnaire to 1012 older adults and adding eight important factors, including technology anxiety, self-efficacy, cognitive ability, and physical function, based on the TAM. This enriches the theoretical foundation of the field. In addition, Braun ( 2013 ) highlighted the role of perceived usefulness, trust in social networks, and frequency of Internet use in older adults’ use of social networks, while ease of use and social pressure were not significant influences. These findings contribute to the study of older adults’ technology acceptance within specific technology application domains.

Recent research has focused on empirical studies of personal factors and emerging technologies. Ma et al. ( 2016 ) identified key personal factors affecting smartphone acceptance among older adults through structured questionnaires and face-to-face interviews with 120 participants. The study found that cost, self-satisfaction, and convenience were important factors influencing perceived usefulness and ease of use. This study offers empirical evidence to comprehend the main factors that drive smartphone acceptance among Chinese older adults. Additionally, Yusif et al. ( 2016 ) presented an overview of the obstacles that hinder older adults’ acceptance of assistive technologies, focusing on privacy, trust, and functionality.

In summary, research on older adults’ technology acceptance has shifted from early theoretical deepening and analysis of influencing factors to empirical studies in the areas of personal factors and emerging technologies, which have greatly enriched the theoretical basis of older adults’ technology acceptance and provided practical guidance for the design of emerging technology products.

Research hotspots, evolutionary trends, and quality distribution (RQ4)

Core keywords analysis.

Keywords concise the main idea and core of the literature, and are a refined summary of the research content (Huang et al. 2021 ). In CiteSpace, nodes with a centrality value greater than 0.1 are considered to be critical nodes. Analyzing keywords with high frequency and centrality helps to visualize the hot topics in the research field (Park et al. 2018 ). The merged keywords were imported into CiteSpace, and the top 10 keywords were counted and sorted by frequency and centrality respectively, as shown in Table 9 . The results show that the keyword “TAM” has the highest frequency (92), followed by “UTAUT” (24), which reflects that the in-depth study of the existing technology acceptance model and its theoretical expansion occupy a central position in research related to older adults’ technology acceptance. Furthermore, the terms ‘assistive technology’ and ‘virtual reality’ are both high-frequency and high-centrality terms (frequency = 17, centrality = 0.10), indicating that the research on assistive technology and virtual reality for older adults is the focus of current academic attention.

Research hotspots analysis

Using VOSviewer for keyword co-occurrence analysis organizes keywords into groups or clusters based on their intrinsic connections and frequencies, clearly highlighting the research field’s hot topics. The connectivity among keywords reveals correlations between different topics. To ensure accuracy, the analysis only considered the authors’ keywords. Subsequently, the keywords were filtered by setting the keyword frequency to 5 to obtain the keyword clustering map of the research on older adults’ technology acceptance research keyword clustering mapping (Fig. 7 ), combined with the keyword co-occurrence clustering network (Fig. 7A ) and the corresponding density situation (Fig. 7B ) to make a detailed analysis of the following four groups of clustered themes.

A Co-occurrence clustering network. B Keyword density.

Cluster #1—Research on the factors influencing technology adoption among older adults is a prominent topic, covering age, gender, self-efficacy, attitude, and and intention to use (Berkowsky et al. 2017 ; Wang et al. 2017 ). It also examined older adults’ attitudes towards and acceptance of digital health technologies (Ahmad and Mozelius, 2022 ). Moreover, the COVID-19 pandemic, significantly impacting older adults’ technology attitudes and usage, has underscored the study’s importance and urgency. Therefore, it is crucial to conduct in-depth studies on how older adults accept, adopt, and effectively use new technologies, to address their needs and help them overcome the digital divide within digital inclusion. This will improve their quality of life and healthcare experiences.

Cluster #2—Research focuses on how older adults interact with assistive technologies, especially assistive robots and health monitoring devices, emphasizing trust, usability, and user experience as crucial factors (Halim et al. 2022 ). Moreover, health monitoring technologies effectively track and manage health issues common in older adults, like dementia and mild cognitive impairment (Lussier et al. 2018 ; Piau et al. 2019 ). Interactive exercise games and virtual reality have been deployed to encourage more physical and cognitive engagement among older adults (Campo-Prieto et al. 2021 ). Personalized and innovative technology significantly enhances older adults’ participation, improving their health and well-being.

Cluster #3—Optimizing health management for older adults using mobile technology. With the development of mobile health (mHealth) and health information technology, mobile applications, smartphones, and smart wearable devices have become effective tools to help older users better manage chronic conditions, conduct real-time health monitoring, and even receive telehealth services (Dupuis and Tsotsos 2018 ; Olmedo-Aguirre et al. 2022 ; Kim et al. 2014 ). Additionally, these technologies can mitigate the problem of healthcare resource inequality, especially in developing countries. Older adults’ acceptance and use of these technologies are significantly influenced by their behavioral intentions, motivational factors, and self-management skills. These internal motivational factors, along with external factors, jointly affect older adults’ performance in health management and quality of life.

Cluster #4—Research on technology-assisted home care for older adults is gaining popularity. Environmentally assisted living enhances older adults’ independence and comfort at home, offering essential support and security. This has a crucial impact on promoting healthy aging (Friesen et al. 2016 ; Wahlroos et al. 2023 ). The smart home is a core application in this field, providing a range of solutions that facilitate independent living for the elderly in a highly integrated and user-friendly manner. This fulfills different dimensions of living and health needs (Majumder et al. 2017 ). Moreover, eHealth offers accurate and personalized health management and healthcare services for older adults (Delmastro et al. 2018 ), ensuring their needs are met at home. Research in this field often employs qualitative methods and structural equation modeling to fully understand older adults’ needs and experiences at home and analyze factors influencing technology adoption.

Evolutionary trends analysis

To gain a deeper understanding of the evolutionary trends in research hotspots within the field of older adults’ technology acceptance, we conducted a statistical analysis of the average appearance times of keywords, using CiteSpace to generate the time-zone evolution mapping (Fig. 8 ) and burst keywords. The time-zone mapping visually displays the evolution of keywords over time, intuitively reflecting the frequency and initial appearance of keywords in research, commonly used to identify trends in research topics (Jing et al. 2024a ; Kumar et al. 2021 ). Table 10 lists the top 15 keywords by burst strength, with the red sections indicating high-frequency citations and their burst strength in specific years. These burst keywords reveal the focus and trends of research themes over different periods (Kleinberg 2002 ). Combining insights from the time-zone mapping and burst keywords provides more objective and accurate research insights (Wang et al. 2023b ).

Reflecting the frequency and time of first appearance of keywords in the study.

An integrated analysis of Fig. 8 and Table 10 shows that early research on older adults’ technology acceptance primarily focused on factors such as perceived usefulness, ease of use, and attitudes towards information technology, including their use of computers and the internet (Pan and Jordan-Marsh 2010 ), as well as differences in technology use between older adults and other age groups (Guner and Acarturk 2020 ). Subsequently, the research focus expanded to improving the quality of life for older adults, exploring how technology can optimize health management and enhance the possibility of independent living, emphasizing the significant role of technology in improving the quality of life for the elderly. With ongoing technological advancements, recent research has shifted towards areas such as “virtual reality,” “telehealth,” and “human-robot interaction,” with a focus on the user experience of older adults (Halim et al. 2022 ). The appearance of keywords such as “physical activity” and “exercise” highlights the value of technology in promoting physical activity and health among older adults. This phase of research tends to make cutting-edge technology genuinely serve the practical needs of older adults, achieving its widespread application in daily life. Additionally, research has focused on expanding and quantifying theoretical models of older adults’ technology acceptance, involving keywords such as “perceived risk”, “validation” and “UTAUT”.

In summary, from 2013 to 2023, the field of older adults’ technology acceptance has evolved from initial explorations of influencing factors, to comprehensive enhancements in quality of life and health management, and further to the application and deepening of theoretical models and cutting-edge technologies. This research not only reflects the diversity and complexity of the field but also demonstrates a comprehensive and in-depth understanding of older adults’ interactions with technology across various life scenarios and needs.

Research quality distribution

To reveal the distribution of research quality in the field of older adults’ technology acceptance, a strategic diagram analysis is employed to calculate and illustrate the internal development and interrelationships among various research themes (Xie et al. 2020 ). The strategic diagram uses Centrality as the X-axis and Density as the Y-axis to divide into four quadrants, where the X-axis represents the strength of the connection between thematic clusters and other themes, with higher values indicating a central position in the research field; the Y-axis indicates the level of development within the thematic clusters, with higher values denoting a more mature and widely recognized field (Li and Zhou 2020 ).

Through cluster analysis and manual verification, this study categorized 61 core keywords (Frequency ≥5) into 11 thematic clusters. Subsequently, based on the keywords covered by each thematic cluster, the research themes and their directions for each cluster were summarized (Table 11 ), and the centrality and density coordinates for each cluster were precisely calculated (Table 12 ). Finally, a strategic diagram of the older adults’ technology acceptance research field was constructed (Fig. 9 ). Based on the distribution of thematic clusters across the quadrants in the strategic diagram, the structure and developmental trends of the field were interpreted.

Classification and visualization of theme clusters based on density and centrality.

As illustrated in Fig. 9 , (1) the theme clusters of #3 Usage Experience and #4 Assisted Living Technology are in the first quadrant, characterized by high centrality and density. Their internal cohesion and close links with other themes indicate their mature development, systematic research content or directions have been formed, and they have a significant influence on other themes. These themes play a central role in the field of older adults’ technology acceptance and have promising prospects. (2) The theme clusters of #6 Smart Devices, #9 Theoretical Models, and #10 Mobile Health Applications are in the second quadrant, with higher density but lower centrality. These themes have strong internal connections but weaker external links, indicating that these three themes have received widespread attention from researchers and have been the subject of related research, but more as self-contained systems and exhibit independence. Therefore, future research should further explore in-depth cooperation and cross-application with other themes. (3) The theme clusters of #7 Human-Robot Interaction, #8 Characteristics of the Elderly, and #11 Research Methods are in the third quadrant, with lower centrality and density. These themes are loosely connected internally and have weak links with others, indicating their developmental immaturity. Compared to other topics, they belong to the lower attention edge and niche themes, and there is a need for further investigation. (4) The theme clusters of #1 Digital Healthcare Technology, #2 Psychological Factors, and #5 Socio-Cultural Factors are located in the fourth quadrant, with high centrality but low density. Although closely associated with other research themes, the internal cohesion within these clusters is relatively weak. This suggests that while these themes are closely linked to other research areas, their own development remains underdeveloped, indicating a core immaturity. Nevertheless, these themes are crucial within the research domain of elderly technology acceptance and possess significant potential for future exploration.

Discussion on distribution power (RQ1)

Over the past decade, academic interest and influence in the area of older adults’ technology acceptance have significantly increased. This trend is evidenced by a quantitative analysis of publication and citation volumes, particularly noticeable in 2019 and 2022, where there was a substantial rise in both metrics. The rise is closely linked to the widespread adoption of emerging technologies such as smart homes, wearable devices, and telemedicine among older adults. While these technologies have enhanced their quality of life, they also pose numerous challenges, sparking extensive research into their acceptance, usage behaviors, and influencing factors among the older adults (Pirzada et al. 2022 ; Garcia Reyes et al. 2023 ). Furthermore, the COVID-19 pandemic led to a surge in technology demand among older adults, especially in areas like medical consultation, online socialization, and health management, further highlighting the importance and challenges of technology. Health risks and social isolation have compelled older adults to rely on technology for daily activities, accelerating its adoption and application within this demographic. This phenomenon has made technology acceptance a critical issue, driving societal and academic focus on the study of technology acceptance among older adults.

The flow of knowledge at the level of high-output disciplines and journals, along with the primary publishing outlets, indicates the highly interdisciplinary nature of research into older adults’ technology acceptance. This reflects the complexity and breadth of issues related to older adults’ technology acceptance, necessitating the integration of multidisciplinary knowledge and approaches. Currently, research is primarily focused on medical health and human-computer interaction, demonstrating academic interest in improving health and quality of life for older adults and addressing the urgent needs related to their interactions with technology. In the field of medical health, research aims to provide advanced and innovative healthcare technologies and services to meet the challenges of an aging population while improving the quality of life for older adults (Abdi et al. 2020 ; Wilson et al. 2021 ). In the field of human-computer interaction, research is focused on developing smarter and more user-friendly interaction models to meet the needs of older adults in the digital age, enabling them to actively participate in social activities and enjoy a higher quality of life (Sayago, 2019 ). These studies are crucial for addressing the challenges faced by aging societies, providing increased support and opportunities for the health, welfare, and social participation of older adults.

Discussion on research power (RQ2)

This study analyzes leading countries and collaboration networks, core institutions and authors, revealing the global research landscape and distribution of research strength in the field of older adults’ technology acceptance, and presents quantitative data on global research trends. From the analysis of country distribution and collaborations, China and the USA hold dominant positions in this field, with developed countries like the UK, Germany, Italy, and the Netherlands also excelling in international cooperation and research influence. The significant investment in technological research and the focus on the technological needs of older adults by many developed countries reflect their rapidly aging societies, policy support, and resource allocation.

China is the only developing country that has become a major contributor in this field, indicating its growing research capabilities and high priority given to aging societies and technological innovation. Additionally, China has close collaborations with countries such as USA, the UK, and Malaysia, driven not only by technological research needs but also by shared challenges and complementarities in aging issues among these nations. For instance, the UK has extensive experience in social welfare and aging research, providing valuable theoretical guidance and practical experience. International collaborations, aimed at addressing the challenges of aging, integrate the strengths of various countries, advancing in-depth and widespread development in the research of technology acceptance among older adults.

At the institutional and author level, City University of Hong Kong leads in publication volume, with research teams led by Chan and Chen demonstrating significant academic activity and contributions. Their research primarily focuses on older adults’ acceptance and usage behaviors of various technologies, including smartphones, smart wearables, and social robots (Chen et al. 2015 ; Li et al. 2019 ; Ma et al. 2016 ). These studies, targeting specific needs and product characteristics of older adults, have developed new models of technology acceptance based on existing frameworks, enhancing the integration of these technologies into their daily lives and laying a foundation for further advancements in the field. Although Tilburg University has a smaller publication output, it holds significant influence in the field of older adults’ technology acceptance. Particularly, the high citation rate of Peek’s studies highlights their excellence in research. Peek extensively explored older adults’ acceptance and usage of home care technologies, revealing the complexity and dynamics of their technology use behaviors. His research spans from identifying systemic influencing factors (Peek et al. 2014 ; Peek et al. 2016 ), emphasizing familial impacts (Luijkx et al. 2015 ), to constructing comprehensive models (Peek et al. 2017 ), and examining the dynamics of long-term usage (Peek et al. 2019 ), fully reflecting the evolving technology landscape and the changing needs of older adults. Additionally, the ongoing contributions of researchers like Ziefle, Rogers, and Wouters in the field of older adults’ technology acceptance demonstrate their research influence and leadership. These researchers have significantly enriched the knowledge base in this area with their diverse perspectives. For instance, Ziefle has uncovered the complex attitudes of older adults towards technology usage, especially the trade-offs between privacy and security, and how different types of activities affect their privacy needs (Maidhof et al. 2023 ; Mujirishvili et al. 2023 ; Schomakers and Ziefle 2023 ; Wilkowska et al. 2022 ), reflecting a deep exploration and ongoing innovation in the field of older adults’ technology acceptance.

Discussion on knowledge base and thematic progress (RQ3)

Through co-citation analysis and systematic review of seminal literature, this study reveals the knowledge foundation and thematic progress in the field of older adults’ technology acceptance. Co-citation networks and cluster analyses illustrate the structural themes of the research, delineating the differentiation and boundaries within this field. Additionally, burst detection analysis offers a valuable perspective for understanding the thematic evolution in the field of technology acceptance among older adults. The development and innovation of theoretical models are foundational to this research. Researchers enhance the explanatory power of constructed models by deepening and expanding existing technology acceptance theories to address theoretical limitations. For instance, Heerink et al. ( 2010 ) modified and expanded the UTAUT model by integrating functional assessment and social interaction variables to create the almere model. This model significantly enhances the ability to explain the intentions of older users in utilizing assistive social agents and improves the explanation of actual usage behaviors. Additionally, Chen and Chan ( 2014 ) extended the TAM to include age-related health and capability features of older adults, creating the STAM, which substantially improves predictions of older adults’ technology usage behaviors. Personal attributes, health and capability features, and facilitating conditions have a direct impact on technology acceptance. These factors more effectively predict older adults’ technology usage behaviors than traditional attitudinal factors.

With the advancement of technology and the application of emerging technologies, new research topics have emerged, increasingly focusing on older adults’ acceptance and use of these technologies. Prior to this, the study by Mitzner et al. ( 2010 ) challenged the stereotype of older adults’ conservative attitudes towards technology, highlighting the central roles of usability and usefulness in the technology acceptance process. This discovery laid an important foundation for subsequent research. Research fields such as “smart home technology,” “social life,” and “customer service” are emerging, indicating a shift in focus towards the practical and social applications of technology in older adults’ lives. Research not only focuses on the technology itself but also on how these technologies integrate into older adults’ daily lives and how they can improve the quality of life through technology. For instance, studies such as those by Ma et al. ( 2016 ), Hoque and Sorwar ( 2017 ), and Li et al. ( 2019 ) have explored factors influencing older adults’ use of smartphones, mHealth, and smart wearable devices.

Furthermore, the diversification of research methodologies and innovation in evaluation techniques, such as the use of mixed methods, structural equation modeling (SEM), and neural network (NN) approaches, have enhanced the rigor and reliability of the findings, enabling more precise identification of the factors and mechanisms influencing technology acceptance. Talukder et al. ( 2020 ) employed an effective multimethodological strategy by integrating SEM and NN to leverage the complementary strengths of both approaches, thus overcoming their individual limitations and more accurately analyzing and predicting older adults’ acceptance of wearable health technologies (WHT). SEM is utilized to assess the determinants’ impact on the adoption of WHT, while neural network models validate SEM outcomes and predict the significance of key determinants. This combined approach not only boosts the models’ reliability and explanatory power but also provides a nuanced understanding of the motivations and barriers behind older adults’ acceptance of WHT, offering deep research insights.

Overall, co-citation analysis of the literature in the field of older adults’ technology acceptance has uncovered deeper theoretical modeling and empirical studies on emerging technologies, while emphasizing the importance of research methodological and evaluation innovations in understanding complex social science issues. These findings are crucial for guiding the design and marketing strategies of future technology products, especially in the rapidly growing market of older adults.

Discussion on research hotspots and evolutionary trends (RQ4)

By analyzing core keywords, we can gain deep insights into the hot topics, evolutionary trends, and quality distribution of research in the field of older adults’ technology acceptance. The frequent occurrence of the keywords “TAM” and “UTAUT” indicates that the applicability and theoretical extension of existing technology acceptance models among older adults remain a focal point in academia. This phenomenon underscores the enduring influence of the studies by Davis ( 1989 ) and Venkatesh et al. ( 2003 ), whose models provide a robust theoretical framework for explaining and predicting older adults’ acceptance and usage of emerging technologies. With the widespread application of artificial intelligence (AI) and big data technologies, these theoretical models have incorporated new variables such as perceived risk, trust, and privacy issues (Amin et al. 2024 ; Chen et al. 2024 ; Jing et al. 2024b ; Seibert et al. 2021 ; Wang et al. 2024b ), advancing the theoretical depth and empirical research in this field.

Keyword co-occurrence cluster analysis has revealed multiple research hotspots in the field, including factors influencing technology adoption, interactive experiences between older adults and assistive technologies, the application of mobile health technology in health management, and technology-assisted home care. These studies primarily focus on enhancing the quality of life and health management of older adults through emerging technologies, particularly in the areas of ambient assisted living, smart health monitoring, and intelligent medical care. In these domains, the role of AI technology is increasingly significant (Qian et al. 2021 ; Ho 2020 ). With the evolution of next-generation information technologies, AI is increasingly integrated into elder care systems, offering intelligent, efficient, and personalized service solutions by analyzing the lifestyles and health conditions of older adults. This integration aims to enhance older adults’ quality of life in aspects such as health monitoring and alerts, rehabilitation assistance, daily health management, and emotional support (Lee et al. 2023 ). A survey indicates that 83% of older adults prefer AI-driven solutions when selecting smart products, demonstrating the increasing acceptance of AI in elder care (Zhao and Li 2024 ). Integrating AI into elder care presents both opportunities and challenges, particularly in terms of user acceptance, trust, and long-term usage effects, which warrant further exploration (Mhlanga 2023 ). These studies will help better understand the profound impact of AI technology on the lifestyles of older adults and provide critical references for optimizing AI-driven elder care services.

The Time-zone evolution mapping and burst keyword analysis further reveal the evolutionary trends of research hotspots. Early studies focused on basic technology acceptance models and user perceptions, later expanding to include quality of life and health management. In recent years, research has increasingly focused on cutting-edge technologies such as virtual reality, telehealth, and human-robot interaction, with a concurrent emphasis on the user experience of older adults. This evolutionary process demonstrates a deepening shift from theoretical models to practical applications, underscoring the significant role of technology in enhancing the quality of life for older adults. Furthermore, the strategic coordinate mapping analysis clearly demonstrates the development and mutual influence of different research themes. High centrality and density in the themes of Usage Experience and Assisted Living Technology indicate their mature research status and significant impact on other themes. The themes of Smart Devices, Theoretical Models, and Mobile Health Applications demonstrate self-contained research trends. The themes of Human-Robot Interaction, Characteristics of the Elderly, and Research Methods are not yet mature, but they hold potential for development. Themes of Digital Healthcare Technology, Psychological Factors, and Socio-Cultural Factors are closely related to other themes, displaying core immaturity but significant potential.

In summary, the research hotspots in the field of older adults’ technology acceptance are diverse and dynamic, demonstrating the academic community’s profound understanding of how older adults interact with technology across various life contexts and needs. Under the influence of AI and big data, research should continue to focus on the application of emerging technologies among older adults, exploring in depth how they adapt to and effectively use these technologies. This not only enhances the quality of life and healthcare experiences for older adults but also drives ongoing innovation and development in this field.

Research agenda

Based on the above research findings, to further understand and promote technology acceptance and usage among older adults, we recommend future studies focus on refining theoretical models, exploring long-term usage, and assessing user experience in the following detailed aspects:

Refinement and validation of specific technology acceptance models for older adults: Future research should focus on developing and validating technology acceptance models based on individual characteristics, particularly considering variations in technology acceptance among older adults across different educational levels and cultural backgrounds. This includes factors such as age, gender, educational background, and cultural differences. Additionally, research should examine how well specific technologies, such as wearable devices and mobile health applications, meet the needs of older adults. Building on existing theoretical models, this research should integrate insights from multiple disciplines such as psychology, sociology, design, and engineering through interdisciplinary collaboration to create more accurate and comprehensive models, which should then be validated in relevant contexts.

Deepening the exploration of the relationship between long-term technology use and quality of life among older adults: The acceptance and use of technology by users is a complex and dynamic process (Seuwou et al. 2016 ). Existing research predominantly focuses on older adults’ initial acceptance or short-term use of new technologies; however, the impact of long-term use on their quality of life and health is more significant. Future research should focus on the evolution of older adults’ experiences and needs during long-term technology usage, and the enduring effects of technology on their social interactions, mental health, and life satisfaction. Through longitudinal studies and qualitative analysis, this research reveals the specific needs and challenges of older adults in long-term technology use, providing a basis for developing technologies and strategies that better meet their requirements. This understanding aids in comprehensively assessing the impact of technology on older adults’ quality of life and guiding the optimization and improvement of technological products.

Evaluating the Importance of User Experience in Research on Older Adults’ Technology Acceptance: Understanding the mechanisms of information technology acceptance and use is central to human-computer interaction research. Although technology acceptance models and user experience models differ in objectives, they share many potential intersections. Technology acceptance research focuses on structured prediction and assessment, while user experience research concentrates on interpreting design impacts and new frameworks. Integrating user experience to assess older adults’ acceptance of technology products and systems is crucial (Codfrey et al. 2022 ; Wang et al. 2019 ), particularly for older users, where specific product designs should emphasize practicality and usability (Fisk et al. 2020 ). Researchers need to explore innovative age-appropriate design methods to enhance older adults’ usage experience. This includes studying older users’ actual usage preferences and behaviors, optimizing user interfaces, and interaction designs. Integrating feedback from older adults to tailor products to their needs can further promote their acceptance and continued use of technology products.

Conclusions

This study conducted a systematic review of the literature on older adults’ technology acceptance over the past decade through bibliometric analysis, focusing on the distribution power, research power, knowledge base and theme progress, research hotspots, evolutionary trends, and quality distribution. Using a combination of quantitative and qualitative methods, this study has reached the following conclusions:

Technology acceptance among older adults has become a hot topic in the international academic community, involving the integration of knowledge across multiple disciplines, including Medical Informatics, Health Care Sciences Services, and Ergonomics. In terms of journals, “PSYCHOLOGY, EDUCATION, HEALTH” represents a leading field, with key publications including Computers in Human Behavior , Journal of Medical Internet Research , and International Journal of Human-Computer Interaction . These journals possess significant academic authority and extensive influence in the field.

Research on technology acceptance among older adults is particularly active in developed countries, with China and USA publishing significantly more than other nations. The Netherlands leads in high average citation rates, indicating the depth and impact of its research. Meanwhile, the UK stands out in terms of international collaboration. At the institutional level, City University of Hong Kong and The University of Hong Kong in China are in leading positions. Tilburg University in the Netherlands demonstrates exceptional research quality through its high average citation count. At the author level, Chen from China has the highest number of publications, while Peek from the Netherlands has the highest average citation count.

Co-citation analysis of references indicates that the knowledge base in this field is divided into three main categories: theoretical model deepening, emerging technology applications, and research methods and evaluation. Seminal literature focuses on four areas: specific technology use by older adults, expansion of theoretical models of technology acceptance, information technology adoption behavior, and research perspectives. Research themes have evolved from initial theoretical deepening and analysis of influencing factors to empirical studies on individual factors and emerging technologies.

Keyword analysis indicates that TAM and UTAUT are the most frequently occurring terms, while “assistive technology” and “virtual reality” are focal points with high frequency and centrality. Keyword clustering analysis reveals that research hotspots are concentrated on the influencing factors of technology adoption, human-robot interaction experiences, mobile health management, and technology for aging in place. Time-zone evolution mapping and burst keyword analysis have revealed the research evolution from preliminary exploration of influencing factors, to enhancements in quality of life and health management, and onto advanced technology applications and deepening of theoretical models. Furthermore, analysis of research quality distribution indicates that Usage Experience and Assisted Living Technology have become core topics, while Smart Devices, Theoretical Models, and Mobile Health Applications point towards future research directions.

Through this study, we have systematically reviewed the dynamics, core issues, and evolutionary trends in the field of older adults’ technology acceptance, constructing a comprehensive Knowledge Mapping of the domain and presenting a clear framework of existing research. This not only lays the foundation for subsequent theoretical discussions and innovative applications in the field but also provides an important reference for relevant scholars.

Limitations

To our knowledge, this is the first bibliometric analysis concerning technology acceptance among older adults, and we adhered strictly to bibliometric standards throughout our research. However, this study relies on the Web of Science Core Collection, and while its authority and breadth are widely recognized, this choice may have missed relevant literature published in other significant databases such as PubMed, Scopus, and Google Scholar, potentially overlooking some critical academic contributions. Moreover, given that our analysis was confined to literature in English, it may not reflect studies published in other languages, somewhat limiting the global representativeness of our data sample.

It is noteworthy that with the rapid development of AI technology, its increasingly widespread application in elderly care services is significantly transforming traditional care models. AI is profoundly altering the lifestyles of the elderly, from health monitoring and smart diagnostics to intelligent home systems and personalized care, significantly enhancing their quality of life and health care standards. The potential for AI technology within the elderly population is immense, and research in this area is rapidly expanding. However, due to the restrictive nature of the search terms used in this study, it did not fully cover research in this critical area, particularly in addressing key issues such as trust, privacy, and ethics.

Consequently, future research should not only expand data sources, incorporating multilingual and multidatabase literature, but also particularly focus on exploring older adults’ acceptance of AI technology and its applications, in order to construct a more comprehensive academic landscape of older adults’ technology acceptance, thereby enriching and extending the knowledge system and academic trends in this field.

Data availability

The datasets analyzed during the current study are available in the Dataverse repository: https://doi.org/10.7910/DVN/6K0GJH .

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This research was supported by the Social Science Foundation of Shaanxi Province in China (Grant No. 2023J014).

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Xianru Shang, Zijian Liu, Chen Gong, Zhigang Hu & Yuexuan Wu

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Shang, X., Liu, Z., Gong, C. et al. Knowledge mapping and evolution of research on older adults’ technology acceptance: a bibliometric study from 2013 to 2023. Humanit Soc Sci Commun 11 , 1115 (2024). https://doi.org/10.1057/s41599-024-03658-2

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The University of Chicago The Law School

Innovation clinic—significant achievements for 2023-24.

The Innovation Clinic continued its track record of success during the 2023-2024 school year, facing unprecedented demand for our pro bono services as our reputation for providing high caliber transactional and regulatory representation spread. The overwhelming number of assistance requests we received from the University of Chicago, City of Chicago, and even national startup and venture capital communities enabled our students to cherry-pick the most interesting, pedagogically valuable assignments offered to them. Our focus on serving startups, rather than all small- to medium-sized businesses, and our specialization in the needs and considerations that these companies have, which differ substantially from the needs of more traditional small businesses, has proven to be a strong differentiator for the program both in terms of business development and prospective and current student interest, as has our further focus on tackling idiosyncratic, complex regulatory challenges for first-of-their kind startups. We are also beginning to enjoy more long-term relationships with clients who repeatedly engage us for multiple projects over the course of a year or more as their legal needs develop.

This year’s twelve students completed over twenty projects and represented clients in a very broad range of industries: mental health and wellbeing, content creation, medical education, biotech and drug discovery, chemistry, food and beverage, art, personal finance, renewable energy, fintech, consumer products and services, artificial intelligence (“AI”), and others. The matters that the students handled gave them an unparalleled view into the emerging companies and venture capital space, at a level of complexity and agency that most junior lawyers will not experience until several years into their careers.

Representative Engagements

While the Innovation Clinic’s engagements are highly confidential and cannot be described in detail, a high-level description of a representative sample of projects undertaken by the Innovation Clinic this year includes:

Transactional/Commercial Work

• A previous client developing a symptom-tracking wellness app for chronic disease sufferers engaged the Innovation Clinic again, this time to restructure its cap table by moving one founder’s interest in the company to a foreign holding company and subjecting the holding company to appropriate protections in favor of the startup.
• Another client with whom the Innovation Clinic had already worked several times engaged us for several new projects, including (1) restructuring their cap table and issuing equity to an additional, new founder, (2) drafting several different forms of license agreements that the company could use when generating content for the platform, covering situations in which the company would license existing content from other providers, jointly develop new content together with contractors or specialists that would then be jointly owned by all creators, or commission contractors to make content solely owned by the company, (3) drafting simple agreements for future equity (“Safes”) for the company to use in its seed stage fundraising round, and (4) drafting terms of service and a privacy policy for the platform.
• Yet another repeat client, an internet platform that supports independent artists by creating short films featuring the artists to promote their work and facilitates sales of the artists’ art through its platform, retained us this year to draft a form of independent contractor agreement that could be used when the company hires artists to be featured in content that the company’s Fortune 500 brand partners commission from the company, and to create capsule art collections that could be sold by these Fortune 500 brand partners in conjunction with the content promotion.
• We worked with a platform using AI to accelerate the Investigational New Drug (IND) approval and application process to draft a form of license agreement for use with its customers and an NDA for prospective investors.
• A novel personal finance platform for young, high-earning individuals engaged the Innovation Clinic to form an entity for the platform, including helping the founders to negotiate a deal among them with respect to roles and equity, terms that the equity would be subject to, and other post-incorporation matters, as well as to draft terms of service and a privacy policy for the platform.
• Students also formed an entity for a biotech therapeutics company founded by University of Chicago faculty members and an AI-powered legal billing management platform founded by University of Chicago students.
• A founder the Innovation Clinic had represented in connection with one venture engaged us on behalf of his other venture team to draft an equity incentive plan for the company as well as other required implementing documentation. His venture with which we previously worked also engaged us this year to draft Safes to be used with over twenty investors in a seed financing round.

More information regarding other types of transactional projects that we typically take on can be found here .

Regulatory Research and Advice

• A team of Innovation Clinic students invested a substantial portion of our regulatory time this year performing highly detailed and complicated research into public utilities laws of several states to advise a groundbreaking renewable energy technology company as to how its product might be regulated in these states and its clearest path to market. This project involved a review of not only the relevant state statutes but also an analysis of the interplay between state and federal statutes as it relates to public utilities law, the administrative codes of the relevant state executive branch agencies, and binding and non-binding administrative orders, decisions and guidance from such agencies in other contexts that could shed light on how such states would regulate this never-before-seen product that their laws clearly never contemplated could exist. The highly varied approach to utilities regulation in all states examined led to a nuanced set of analysis and recommendations for the client.
• In another significant research project, a separate team of Innovation Clinic students undertook a comprehensive review of all settlement orders and court decisions related to actions brought by the Consumer Financial Protection Bureau for violations of the prohibition on unfair, deceptive, or abusive acts and practices under the Consumer Financial Protection Act, as well as selected relevant settlement orders, court decisions, and other formal and informal guidance documents related to actions brought by the Federal Trade Commission for violations of the prohibition on unfair or deceptive acts or practices under Section 5 of the Federal Trade Commission Act, to assemble a playbook for a fintech company regarding compliance. This playbook, which distilled very complicated, voluminous legal decisions and concepts into a series of bullet points with clear, easy-to-follow rules and best practices, designed to be distributed to non-lawyers in many different facets of this business, covered all aspects of operations that could subject a company like this one to liability under the laws examined, including with respect to asset purchase transactions, marketing and consumer onboarding, usage of certain terms of art in advertising, disclosure requirements, fee structures, communications with customers, legal documentation requirements, customer service and support, debt collection practices, arrangements with third parties who act on the company’s behalf, and more.

Miscellaneous

• Last year’s students built upon the Innovation Clinic’s progress in shaping the rules promulgated by the Financial Crimes Enforcement Network (“FinCEN”) pursuant to the Corporate Transparency Act to create a client alert summarizing the final rule, its impact on startups, and what startups need to know in order to comply. When FinCEN issued additional guidance with respect to that final rule and changed portions of the final rule including timelines for compliance, this year’s students updated the alert, then distributed it to current and former clients to notify them of the need to comply. The final bulletin is available here .
• In furtherance of that work, additional Innovation Clinic students this year analyzed the impact of the final rule not just on the Innovation Clinic’s clients but also its impact on the Innovation Clinic, and how the Innovation Clinic should change its practices to ensure compliance and minimize risk to the Innovation Clinic. This also involved putting together a comprehensive filing guide for companies that are ready to file their certificates of incorporation to show them procedurally how to do so and explain the choices they must make during the filing process, so that the Innovation Clinic would not be involved in directing or controlling the filings and thus would not be considered a “company applicant” on any client’s Corporate Transparency Act filings with FinCEN.
• The Innovation Clinic also began producing thought leadership pieces regarding AI, leveraging our distinct and uniquely University of Chicago expertise in structuring early-stage companies and analyzing complex regulatory issues with a law and economics lens to add our voice to those speaking on this important topic. One student wrote about whether non-profits are really the most desirable form of entity for mitigating risks associated with AI development, and another team of students prepared an analysis of the EU’s AI Act, comparing it to the Executive Order on AI from President Biden, and recommended a path forward for an AI regulatory environment in the United States. Both pieces can be found here , with more to come!

Innovation Trek

Thanks to another generous gift from Douglas Clark, ’89, and managing partner of Wilson, Sonsini, Goodrich & Rosati, we were able to operationalize the second Innovation Trek over Spring Break 2024. The Innovation Trek provides University of Chicago Law School students with a rare opportunity to explore the innovation and venture capital ecosystem in its epicenter, Silicon Valley. The program enables participating students to learn from business and legal experts in a variety of different industries and roles within the ecosystem to see how the law and economics principles that students learn about in the classroom play out in the real world, and facilitates meaningful connections between alumni, students, and other speakers who are leaders in their fields. This year, we took twenty-three students (as opposed to twelve during the first Trek) and expanded the offering to include not just Innovation Clinic students but also interested students from our JD/MBA Program and Doctoroff Business Leadership Program. We also enjoyed four jam-packed days in Silicon Valley, expanding the trip from the two and a half days that we spent in the Bay Area during our 2022 Trek.

The substantive sessions of the Trek were varied and impactful, and enabled in no small part thanks to substantial contributions from numerous alumni of the Law School. Students were fortunate to visit Coinbase’s Mountain View headquarters to learn from legal leaders at the company on all things Coinbase, crypto, and in-house, Plug & Play Tech Center’s Sunnyvale location to learn more about its investment thesis and accelerator programming, and Google’s Moonshot Factory, X, where we heard from lawyers at a number of different Alphabet companies about their lives as in-house counsel and the varied roles that in-house lawyers can have. We were also hosted by Wilson, Sonsini, Goodrich & Rosati and Fenwick & West LLP where we held sessions featuring lawyers from those firms, alumni from within and outside of those firms, and non-lawyer industry experts on topics such as artificial intelligence, climate tech and renewables, intellectual property, biotech, investing in Silicon Valley, and growth stage companies, and general advice on career trajectories and strategies. We further held a young alumni roundtable, where our students got to speak with alumni who graduated in the past five years for intimate, candid discussions about life as junior associates. In total, our students heard from more than forty speakers, including over twenty University of Chicago alumni from various divisions.

The Trek didn’t stop with education, though. Throughout the week students also had the opportunity to network with speakers to learn more from them outside the confines of panel presentations and to grow their networks. We had a networking dinner with Kirkland & Ellis, a closing dinner with all Trek participants, and for the first time hosted an event for admitted students, Trek participants, and alumni to come together to share experiences and recruit the next generation of Law School students. Several speakers and students stayed in touch following the Trek, and this resulted not just in meaningful relationships but also in employment for some students who attended.

More information on the purposes of the Trek is available here , the full itinerary is available here , and one student participant’s story describing her reflections on and descriptions of her experience on the Trek is available here .

The Innovation Clinic is grateful to all of its clients for continuing to provide its students with challenging, high-quality legal work, and to the many alumni who engage with us for providing an irreplaceable client pipeline and for sharing their time and energy with our students. Our clients are breaking the mold and bringing innovations to market that will improve the lives of people around the world in numerous ways. We are glad to aid in their success in any way that we can. We look forward to another productive year in 2024-2025!