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Cross-Sectional Study: Definition, Designs & Examples

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A cross-sectional study design is a type of observational study, or descriptive research, that involves analyzing information about a population at a specific point in time.

This design measures the prevalence of an outcome of interest in a defined population. It provides a snapshot of the characteristics of the population at a single point in time.

It can be used to assess the prevalence of outcomes and exposures, determine relationships among variables, and generate hypotheses about causal connections between factors to be explored in experimental designs.

Typically, these studies are used to measure the prevalence of health outcomes and describe the characteristics of a population.

In this study, researchers examine a group of participants and depict what already exists in the population without manipulating any variables or interfering with the environment.

Cross-sectional studies aim to describe a variable , not measure it. They can be beneficial for describing a population or “taking a snapshot” of a group of individuals at a single moment in time.

In epidemiology and public health research, cross-sectional studies are used to assess exposure (cause) and disease (effect) and compare the rates of diseases and symptoms of an exposed group with an unexposed group.

Cross-sectional studies are also unique because researchers are able to look at numerous characteristics at once.

For example, a cross-sectional study could be used to investigate whether exposure to certain factors, such as overeating, might correlate to particular outcomes, such as obesity.

While this study cannot prove that overeating causes obesity, it can draw attention to a relationship that might be worth investigating.

Cross-sectional studies can be categorized based on the nature of the data collection and the type of data being sought.

Analytical Studies

In analytical cross-sectional studies, researchers investigate an association between two parameters. They collect data for exposures and outcomes at one specific time to measure an association between an exposure and a condition within a defined population.

The purpose of this type of study is to compare health outcome differences between exposed and unexposed individuals.

Descriptive Studies

• Descriptive cross-sectional studies are purely used to characterize and assess the prevalence and distribution of one or many health outcomes in a defined population.
• They can assess how frequently, widely, or severely a specific variable occurs throughout a specific demographic.
• This is the most common type of cross-sectional study.
• Evaluating the COVID-19 positivity rates among vaccinated and unvaccinated adolescents
• Investigating the prevalence of dysfunctional breathing in patients treated for asthma in primary care (Wang & Cheng, 2020)
• Analyzing whether individuals in a community have any history of mental illness and whether they have used therapy to help with their mental health
• Comparing grades of elementary school students whose parents come from different income levels
• Determining the association between gender and HIV status (Setia, 2016)
• Investigating suicide rates among individuals who have at least one parent with chronic depression
• Assessing the prevalence of HIV and risk behaviors in male sex workers (Shinde et al., 2009)
• Examining sleep quality and its demographic and psychological correlates among university students in Ethiopia (Lemma et al., 2012)
• Calculating what proportion of people served by a health clinic in a particular year have high cholesterol
• Analyzing college students’ distress levels with regard to their year level (Leahy et al., 2010)

Simple and Inexpensive

These studies are quick, cheap, and easy to conduct as they do not require any follow-up with subjects and can be done through self-report surveys.

Minimal room for error

Because all of the variables are analyzed at once, and data does not need to be collected multiple times, there will likely be fewer mistakes as a higher level of control is obtained.

Multiple variables and outcomes can be researched and compared at once

Researchers are able to look at numerous characteristics (ie, age, gender, ethnicity, and education level) in one study.

The data can be a starting point for future research

The information obtained from cross-sectional studies enables researchers to conduct further data analyses to explore any causal relationships in more depth.

Limitations

Does not help determine cause and effect.

Cross-sectional studies can be influenced by an antecedent consequent bias which occurs when it cannot be determined whether exposure preceded disease. (Alexander et al.)

Report bias is probable

Cross-sectional studies rely on surveys and questionnaires, which might not result in accurate reporting as there is no way to verify the information presented.

The timing of the snapshot is not always representative

Cross-sectional studies do not provide information from before or after the report was recorded and only offer a single snapshot of a point in time.

It cannot be used to analyze behavior over a period of time

Cross-sectional studies are designed to look at a variable at a particular moment, while longitudinal studies are more beneficial for analyzing relationships over extended periods.

Cross-Sectional vs. Longitudinal

Both cross-sectional and longitudinal studies are observational and do not require any interference or manipulation of the study environment.

However, cross-sectional studies differ from longitudinal studies in that cross-sectional studies look at a characteristic of a population at a specific point in time, while longitudinal studies involve studying a population over an extended period.

Longitudinal studies require more time and resources and can be less valid as participants might quit the study before the data has been fully collected.

Unlike cross-sectional studies, researchers can use longitudinal data to detect changes in a population and, over time, establish patterns among subjects.

Cross-sectional studies can be done much quicker than longitudinal studies and are a good starting point to establish any associations between variables, while longitudinal studies are more timely but are necessary for studying cause and effect.

Alexander, L. K., Lopez, B., Ricchetti-Masterson, K., & Yeatts, K. B. (n.d.). Cross-sectional Studies. Eric Notebook. Retrieved from https://sph.unc.edu/wp-content/uploads/sites/112/2015/07/nciph_ERIC8.pdf

Cherry, K. (2019, October 10). How Does the Cross-Sectional Research Method Work? Verywell Mind. Retrieved from https://www.verywellmind.com/what-is-a-cross-sectional-study-2794978

Cross-sectional vs. longitudinal studies. Institute for Work & Health. (2015, August). Retrieved from https://www.iwh.on.ca/what-researchers-mean-by/cross-sectional-vs-longitudinal-studies

Leahy, C. M., Peterson, R. F., Wilson, I. G., Newbury, J. W., Tonkin, A. L., & Turnbull, D. (2010). Distress levels and self-reported treatment rates for medicine, law, psychology and mechanical engineering tertiary students: cross-sectional study. The Australian and New Zealand journal of psychiatry, 44(7), 608–615.

Lemma, S., Gelaye, B., Berhane, Y. et al. Sleep quality and its psychological correlates among university students in Ethiopia: a cross-sectional study. BMC Psychiatry 12, 237 (2012).

Wang, X., & Cheng, Z. (2020). Cross-Sectional Studies: Strengths, Weaknesses, and Recommendations. Chest, 158(1S), S65–S71.

Setia M. S. (2016). Methodology Series Module 3: Cross-sectional Studies. Indian journal of dermatology, 61 (3), 261–264.

Shinde S, Setia MS, Row-Kavi A, Anand V, Jerajani H. Male sex workers: Are we ignoring a risk group in Mumbai, India? Indian J Dermatol Venereol Leprol. 2009;75:41–6.

Further Information

• Setia, M. S. (2016). Methodology series module 3: Cross-sectional studies. Indian journal of dermatology, 61(3), 261.
• Sedgwick, P. (2014). Cross sectional studies: advantages and disadvantages. Bmj, 348.

1. Are cross-sectional studies qualitative or quantitative?

Cross-sectional studies can be either qualitative or quantitative , depending on the type of data they collect and how they analyze it. Often, the two approaches are combined in mixed-methods research to get a more comprehensive understanding of the research problem.

2. What’s the difference between cross-sectional and cohort studies?

A cohort study is a type of longitudinal study that samples a group of people with a common characteristic. One key difference is that cross-sectional studies measure a specific moment in time, whereas  cohort studies  follow individuals over extended periods.

Another difference between these two types of studies is the subject pool. In cross-sectional studies, researchers select a sample population and gather data to determine the prevalence of a problem.

Cohort studies, on the other hand, begin by selecting a population of individuals who are already at risk for a specific disease.

3. What’s the difference between cross-sectional and case-control studies?

Case-control studies differ from cross-sectional studies in that case-control studies compare groups retrospectively and cannot be used to calculate relative risk.

In these studies, researchers study one group of people who have developed a particular condition and compare them to a sample without the disease.

Case-control studies are used to determine what factors might be associated with the condition and help researchers form hypotheses about a population.

4. Does a cross-sectional study have a control group?

A cross-sectional study does not need to have a control group , as the population studied is not selected based on exposure.

In a cross-sectional study, data are collected from a sample of the target population at a specific point in time, and everyone in the sample is assessed in the same way. There isn’t a manipulation of variables or a control group as there would be in an experimental study design.

5. Is a cross-sectional study prospective or retrospective?

A cross-sectional study is generally considered neither prospective nor retrospective because it provides a “snapshot” of a population at a single point in time.

Cross-sectional studies are not designed to follow individuals forward in time ( prospective ) or look back at historical data ( retrospective ), as they analyze data from a specific point in time.

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Cross-Sectional Study | Definitions, Uses & Examples

Published on 5 May 2022 by Lauren Thomas .

A cross-sectional study is a type of research design in which you collect data from many different individuals at a single point in time. In cross-sectional research, you observe variables without influencing them.

Researchers in economics, psychology, medicine, epidemiology, and the other social sciences all make use of cross-sectional studies in their work. For example, epidemiologists who are interested in the current prevalence of a disease in a certain subset of the population might use a cross-sectional design to gather and analyse the relevant data.

Table of contents

Cross-sectional vs longitudinal studies, when to use a cross-sectional design, how to perform a cross-sectional study, advantages and disadvantages of cross-sectional studies, frequently asked questions about cross-sectional studies.

The opposite of a cross-sectional study is a longitudinal study . While cross-sectional studies collect data from many subjects at a single point in time, longitudinal studies collect data repeatedly from the same subjects over time, often focusing on a smaller group of individuals connected by a common trait.

Both types are useful for answering different kinds of research questions . A cross-sectional study is a cheap and easy way to gather initial data and identify correlations that can then be investigated further in a longitudinal study.

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When you want to examine the prevalence of some outcome at a certain moment in time, a cross-sectional study is the best choice.

Sometimes a cross-sectional study is the best choice for practical reasons – for instance, if you only have the time or money to collect cross-sectional data, or if the only data you can find to answer your research question were gathered at a single point in time.

As cross-sectional studies are cheaper and less time-consuming than many other types of study, they allow you to easily collect data that can be used as a basis for further research.

Descriptive vs analytical studies

Cross-sectional studies can be used for both analytical and descriptive purposes:

• An analytical study tries to answer how or why a certain outcome might occur.
• A descriptive study only summarises said outcome using descriptive statistics.

To implement a cross-sectional study, you can rely on data assembled by another source or collect your own. Governments often make cross-sectional datasets freely available online.

Prominent examples include the censuses of several countries like the US or France , which survey a cross-sectional snapshot of the country’s residents on important measures. International organisations like the World Health Organization or the World Bank also provide access to cross-sectional datasets on their websites.

However, these datasets are often aggregated to a regional level, which may prevent the investigation of certain research questions. You will also be restricted to whichever variables the original researchers decided to study.

If you want to choose the variables in your study and analyse your data on an individual level, you can collect your own data using research methods such as surveys . It’s important to carefully design your questions and choose your sample .

Like any research design , cross-sectional studies have various benefits and drawbacks.

• Because you only collect data at a single point in time, cross-sectional studies are relatively cheap and less time-consuming than other types of research.
• Cross-sectional studies allow you to collect data from a large pool of subjects and compare differences between groups.
• Cross-sectional studies capture a specific moment in time. National censuses, for instance, provide a snapshot of conditions in that country at that time.

Disadvantages

• It is difficult to establish cause-and-effect relationships using cross-sectional studies, since they only represent a one-time measurement of both the alleged cause and effect.
• Since cross-sectional studies only study a single moment in time, they cannot be used to analyse behavior over a period of time or establish long-term trends.
• The timing of the cross-sectional snapshot may be unrepresentative of behaviour of the group as a whole. For instance, imagine you are looking at the impact of psychotherapy on an illness like depression. If the depressed individuals in your sample began therapy shortly before the data collection, then it might appear that therapy causes depression even if it is effective in the long term.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data are available for analysis; other times your research question may only require a cross-sectional study to answer it.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyse behaviour over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

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How Do Cross-Sectional Studies Work?

Gathering Data From a Single Point in Time

• Defining Characteristics

Advantages of Cross-Sectional Studies

Challenges of cross-sectional studies, cross-sectional vs. longitudinal studies.

A cross-sectional study looks at data at a single point in time. The participants in this type of study are selected based on particular variables. Cross-sectional studies are typically used in developmental psychology , but they are useful in many other areas as well, including social science and education.

Cross-sectional studies are observational and are known as descriptive research, not causal or relational—meaning you can't use them to determine the cause of something, such as a disease. Researchers record the information that is present in a population, but they do not manipulate variables .

This type of research can be used to describe characteristics that exist in a community, but not to determine cause-and-effect relationships between different variables. This method is often used to make inferences about possible relationships or to gather preliminary data to support further research and experimentation.

Example: Researchers studying developmental psychology might select groups of people who are different ages but investigate them at one point in time. By doing this, any differences among the age groups can be attributed to age differences rather than something that happened over time.

Defining Characteristics of Cross-Sectional Studies

Some of the key characteristics of a cross-sectional study include:

• The study takes place at a single point in time
• It does not involve manipulating variables
• It allows researchers to look at numerous characteristics at once (age, income, gender, etc.)
• It's often used to look at the prevailing characteristics in a given population
• It can provide information about what is happening in a current population

Verywell / Jessica Olah

Think of a cross-sectional study as a snapshot of a particular group of people at a given point in time. Unlike longitudinal studies, which look at a group of people over an extended period, cross-sectional studies are used to describe what is happening at the present moment.This type of research is frequently used to determine the prevailing characteristics in a population at a certain point in time. For example, a cross-sectional study might be used to determine if exposure to specific risk factors might correlate with particular outcomes.

A researcher might collect cross-sectional data on past smoking habits and current diagnoses of lung cancer, for example. While this type of study cannot demonstrate cause and effect, it can provide a quick look at correlations that may exist at a particular point.

For example, researchers may find that people who reported engaging in certain health behaviors were also more likely to be diagnosed with specific ailments. While a cross-sectional study cannot prove for certain that these behaviors caused the condition, such studies can point to a relationship worth investigating further.

Cross-sectional studies are popular because they offer many benefits for researchers.

Inexpensive and Fast

Cross-sectional studies typically allow researchers to collect a great deal of information quickly. Data is often obtained inexpensively using self-report surveys . Researchers are then able to amass large amounts of information from a large pool of participants.

For example, a university might post a short online survey about library usage habits among biology majors, and the responses would be recorded in a database automatically for later analysis. This is a simple, inexpensive way to encourage participation and gather data across a wide swath of individuals who fit certain criteria.

Can Assess Multiple Variables

Researchers can collect data on a few different variables to see how they affect a certain condition. For example, differences in sex, age, educational status, and income might correlate with voting tendencies or give market researchers clues about purchasing habits.

Might Prompt Further Study 

Although researchers can't use cross-sectional studies to determine causal relationships, these studies can provide useful springboards to further research. For example, when looking at a public health issue, such as whether a particular behavior might be linked to a particular illness, researchers might utilize a cross-sectional study to look for clues that can spur further experimental studies.

For example, researchers might be interested in learning how exercise influences cognitive health as people age. They might collect data from different age groups on how much exercise they get and how well they perform on cognitive tests. Conducting such a study can give researchers clues about the types of exercise that might be most beneficial to the elderly and inspire further experimental research on the subject.

No method of research is perfect. Cross-sectional studies also have potential drawbacks.

Difficulties in Determining Causal Effects

Researchers can't always be sure that the conditions a cross-sectional study measures are the result of a particular factor's influence. In many cases, the differences among individuals could be attributed to variation among the study subjects. In this way, cause-and-effect relationships are more difficult to determine in a cross-sectional study than they are in a longitudinal study. This type of research simply doesn't allow for conclusions about causation.

For example, a study conducted some 20 years ago queried thousands of women about their consumption of diet soft drinks. The results of the study, published in the medical journal Stroke , associated diet soft drink intake with stroke risk that was greater than that of those who did not consume such beverages. In other words, those who drank lots of diet soda were more prone to strokes. However, correlation does not equal causation. The increased stroke risk might arise from any number of factors that tend to occur among those who drink diet beverages. For example, people who consume sugar-free drinks might be more likely to be overweight or diabetic than those who drink the regular versions. Therefore, they might be at greater risk of stroke—regardless of what they drink.

Cohort Differences

Groups can be affected by cohort differences that arise from the particular experiences of a group of people. For example, individuals born during the same period might witness the same important historical events, but their geographic regions, religious affiliations, political beliefs, and other factors might affect how they perceive such events.

Report Biases

Surveys and questionnaires about certain aspects of people's lives might not always result in accurate reporting. For example, respondents might not disclose certain behaviors or beliefs out of embarrassment, fear, or other limiting perception. Typically, no mechanism for verifying this information exists.

Cross-sectional research differs from longitudinal studies in several important ways. The key difference is that a cross-sectional study is designed to look at a variable at a particular point in time. A longitudinal study evaluates multiple measures over an extended period to detect trends and changes.

Evaluates variable at single point in time

Participants less likely to drop out

Uses new participant(s) with each study

Measures variable over time

Requires more resources

More expensive

Subject to selective attrition

Follows same participants over time

Longitudinal studies tend to require more resources; these are often more expensive than those used by cross-sectional studies. They are also more likely to be influenced by what is known as selective attrition , which means that some individuals are more likely to drop out of a study than others. Because a longitudinal study occurs over a span of time, researchers can lose track of subjects. Individuals might lose interest, move to another city, change their minds about participating, etc. This can influence the validity of the study.

One of the advantages of cross-sectional studies is that data is collected all at once, so participants are less likely to quit the study before data is fully collected.

A Word From Verywell

Cross-sectional studies can be useful research tools in many areas of health research. By learning about what is going on in a specific population, researchers can improve their understanding of relationships among certain variables and develop additional studies that explore these conditions in greater depth.

Levin KA. Study design III: Cross-sectional studies . Evid Based Dent . 2006;7(1):24-5. doi:10.1038/sj.ebd.6400375 

Morin JF, Olsson C, Atikcan EO, eds.  Research Methods in the Social Sciences: An A-Z of Key Concepts . Oxford University Press; 2021.

Abbasi J. Unpacking a recent study linking diet soda with stroke risks .  JAMA . 2019;321(16):1554-1555. doi:10.1001/jama.2019.2123

Setia MS. Methodology series module 3: Cross-sectional studies . Indian J Dermatol . 2016;61(3):261-4. doi:10.4103/0019-5154.182410

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Overview: Cross-Sectional Studies

The conduct of research requires the selection of the appropriate method to evaluate the research problem or question. Due to some topics’ ethical nature or the need to understand the natural history (i.e., disease or condition), using an observational study design might be the best fit. The primary purposes of observational studies are to describe and examine the distributions of independent (predictor) and dependent (outcome) variables in a population (sample) and analyze the associations between them ( Cummings, 2013 ). Observational studies monitor study participants without providing study interventions. This paper describes the cross-sectional design, examines the strengths and weaknesses, and discusses some methods to report the results. Future articles will focus on other observational methods, the cohort, and case-control designs.

Cross-Sectional Design

Cross-sectional designs help determine the prevalence of a disease, phenomena, or opinion in a population, as represented by a study sample. Prevalence is the proportion of people in a population (sample) who have an attribute or condition at a specific time point ( Mann, 2012 ) regardless of when the attribute or condition first developed ( Wang & Cheng, 2020 ). Additionally, each study participant’s evaluation is completed at one time-point with no follow-ups ( Cummings, 2013 ), providing a ‘snapshot’ of the sample. Cross-sectional designs can be implemented as an interview or survey and may also collect physiological data and biological samples.

Cross-Sectional Design: Descriptive

Cross-sectional studies can be descriptive and analytic ( Alexander, 2015a ). Descriptive cross-sectional studies characterize the prevalence of health outcomes or phenomena under investigation. Prevalence is measured either at a one-time point ( point prevalence ), over a specified period ( period prevalence ) ( Alexander, 2015a ), or as a cross-sectional serial survey ( Cummings, 2013 ). The descriptive design starts by identifying the population of interest, collects the data, and classifies the participant, either as having the outcome or phenomena of interest or not ( Mann, 2012 ). For example, investigators want to determine the point prevalence of obesity among people with HIV. To conduct this study, investigators select several HIV primary care clinics in their region and obtain heights, weights, and measure waist circumference during one specified day at each clinic. For a period prevalence study, the investigators could visit each clinic at four-time points over 12 months to obtain body measurements to capture other patients visiting the clinics. Period prevalence and point prevalence are similar, except that the time-frame is broader since it can be difficult to evaluate or observe the entire population or sample at one time-point**.

For serial cross-sectional surveys, investigators collect data in the same population over a specified period. It uses a longitudinal time-frame. For example, every three years, investigators repeat the body measurements among HIV patients to draw inferences about the patterns over time about obesity( Cummings, 2013 ). However, new samples are selected each time; therefore, each participant’s changes cannot be evaluated. It is important to note that the results may be affected by “people entering or leaving the population due to births, deaths, and migration” ( Cummings, 2013 , p.88).

Method to Report Results: Descriptive Cross-Sectional Design

Prevalence is generally reported as a percentage (30% or 75 out of 250 HIV patients were obese). Knowing the prevalence of a condition in a population (sample) helps understand the disease burden in terms of services needed, morbidity, mortality, and quality of life ( Noordzij, Dekker, Zoccali, & Jager, 2010 ). For instance, if obesity is high among the participants, clinic visits could provide nutritional counseling and physical activity recommendations and regularly monitor body weight measurements to prevent the complications associated with obesity (i.e., knee osteoarthritis, type 2 diabetes mellitus).

Cross-Sectional Design: Analytic

Analytic cross-sectional studies can provide the groundwork to infer preliminary evidence for a causal relationship ( Mann, 2012 ). This design allows investigators to identify a population or sample and collect prevalence data to evaluate outcome differences between exposed and unexposed participants on a disease, phenomena, or opinion ( Wang & Cheng, 2020 ). This design compares the proportion of participants exposed to the disease or phenomena of interest with the proportion of participants non-exposed with the disease or phenomena of interest ( Alexander, 2015a ). However, determining which variable is the dependent and independent variable or cause and effect is difficult to determine. For example, the association between obesity and hours spent in sedentary behavior among HIV patients (see Table 1 ). Which came first? Did the participant become obese due to sedentary behavior, or was the participant inactive due to obesity? According to Cummings et al., 2013 , determining which variable to label as dependent or independent “depends on the cause-and-effect hypotheses of the investigator” (p. 85) or the biological plausibility rather than on the study design.

Calculation Example

• a = exposed participant and acquires the outcome of interest
• b = unexposed participant and acquires the outcome of interest
• c = exposed participant and does not acquire the outcome of interest
• d = unexposed participant and does not acquire the outcome of interest
• Prevalence of HIV participants who are obese and sedentary = a/(a + b) = 75/325 =. 23 × 100 = 23%
• Prevalence of HIV participants who are obese and not sedentary = c/(c + d) = 25/225 = .11 × 100 = 11.1%
• Prevalence of overall HIV participants who are obese = (a + c)/(a + b + c + d) = 100/550 = .182 × 100 = 18.2%

Interpretation of Prevalence Odds Ratio/Odds Ratio:

• OR = 1 Exposure did not effect the odds of the outcome
• OR > 1 Exposure is associated with the higher odds of outcome versus nonexposed group
• OR < 1 Exposure is associated with lower odds of outcome verus exposed group
• Upper 95 % CI = e ^   [ ln ( OR ) + 1.96 sqrt ( 1 / a + 1 / b + 1 / c + 1 / d ) ] = 1.4713
• Lower 95 % CI = e ^ [ ln ( OR ) − 1.96 sqrt ( 1 / a + 1 / b + 1 / c + 1 / d ) ] = 3.9150

Interpretation of Prevalence Ratio/Risk Ratio:

• RR = 1 Exposure did not prevent or harm the exposed and unexposed groups
• RR > 1 Exposure is harmful to the exposed group compared to the unexposed group
• RR < 1 Exposure is less harmful (protective) to the exposed group compared to the unexposed group
• Upper 95 % CI = e ^ [ ln ( RR ) − 1.96 sqrt ( 1 / a + 1 / c − 1 / a + b − 1 / c + d ) ] = 1.3653
• Lower 95 % CI = e ^ [ ln ( RR ) + 1.96 sqrt ( 1 / a + 1 / c − 1 / a + b − 1 / c + d ) ] = 3.159

References: Alexander, 2015a, Cummings, 2013, Tenny &Hoffman, 2019.

** https://www.medcalc.org/calc/odds_ratio.php (web-based confidence interval calculator of odds ratio)

*** https://www.medcalc.org/calc/relative_risk.php (web-based confidence interval calculator RR

Method to Report Results: Analytic Cross-Sectional Design

In continuing with the obesity and sedentary activity level among HIV participants, the example below (see Table 1 ) describes the methods for calculating and discussing the results for an analytic cross-sectional study. The prevalence odds ratio (POR) (calculated as [ ad/bc] ) and prevalence ratio (PR) (calculated as [a/(a + b)]/ [c/(c + d)]) are commonly used to report estimates of association between independent and dependent variables in cross-sectional studies ( Tamhane, Westfall, Burkholder, & Cutter, 2016 ).

Prevalence Odds Ratio/Odds Ratio

The POR is calculated similarly to the odds ratio (OR) ( Alexander, 2015b ) and referred to as POR when prevalence is used ( Tamhane et al., 2016 ). OR measures the association between exposure and outcome (see Table 1 ) and denotes the chances that an outcome happens with a specific exposure, compared to the chances of an outcome happening in the absence of the exposure (Szumilas, 2010). This information helps both clinicians and investigators determine if certain factors (i.e., clinical characteristics, medical history) are a risk for a particular outcome (i.e., disease, condition). Future studies or health policies can target methods to prevent or treat outcomes (i.e., disease, condition) identified in such studies.

For example, in Table 1 , using the formula and dataset below, the OR was 2.4. The result shows that the obese HIV participants (exposed) were two and a half times (2.5x) more likely to be sedentary than the non-obese participants (unexposed). If the OR for the dataset was equal to 1, then the exposure (obese) did not affect the outcome’s odds. In other words, the chance of being sedentary is the same in the exposed (obese) and the non-exposed (not obese) groups. Similarly, if the OR was less than 1, it implies that the exposed (obese) group, were less likely to be sedentary (outcome) compared to the non-obese group (unexposed) ( Tenny & Hoffman, 2019 ).

Prevalence Ratio/Risk Ratio and Excess Prevalence/Risk Difference

The PR is calculated similarly to the risk ratio (RR)( Alexander, 2015b ). The PR measures the prevalence of an outcome in the exposed group, divided by the unexposed group, and measures the association’s strength between the exposure and outcome (Alexander, 2015). Excess prevalence (EP) or the risk difference (RD) provides the difference in prevalence between the groups and indicates how much additional prevalence is due to the exposure of interest ( Alexander, 2015b ). From Table 1 , the PR/RR for the example equaled 2.07, with an EP of 11.9%. The results might conclude that obesity among the HIV participants was twice (2.07) as common and occurred almost 12% more often among HIV participants who were sedentary.

Similar to the OR interpretation, if the RR was equal to 1, exposure did not prevent or harm the exposed and unexposed groups. In other words, being obese did not affect the activity level (sedentary versus not sedentary). If the RR was less than 1, it implies that the exposure had a protective effect in that obese HIV participants were less likely to be sedentary than the unexposed group (not obese).

Considerations for use: Prevalence Odds Ratio versus Prevalence Ratio

The statistical literature has numerous articles discussing the pros and cons of using either the POR/OR or PR/RR for cross-sectional studies ( Tamhane et al., 2016 ). Consulting a statistician to discuss the best choice for each project is highly recommended. However, according to Alexander and colleagues (2015a) , the POR is preferred when the study topic is a chronic condition (i.e., hypertension, HIV), or the risk of developing the disease takes several months to develop. For studies evaluating acute conditions (i.e., the common cold), the PR is favored ( Alexander, 2015a ).

Furthermore, suppose the prevalence of a disease or phenomena is low, less than ten percent in the exposed and unexposed population (sample). In that case, the resulting POR and PR will be equal ( Alexander, 2015a ). Since cross-sectional studies are suitable for examining chronic diseases or conditions, the POR is generally the ideal measure of association to use ( Alexander, 2015a ).

Confidence Intervals

Confidence intervals (CI) measure the precision of the OR, RR, or the possible “variation in a point estimate (the mean value)” ( Alexander, 2015b , p 4). A narrower CI indicates a higher level of precision versus a wider CI suggesting a lower level of precision ( Cummings, 2013 ). The sample size also impacts the CI’s width, with larger sample sizes providing a more precise estimate. The approximate value of the point estimate is based on factors (i.e., characteristics like body weight, level of activity) such as the mean (average) of a population from a population’s random samples.

From Table 1 , the OR = 2.4 with a confidence interval of (95% CI (1.4713 – 3.9150)) might conclude that the obese HIV participants were two and a half times (2.5x) more likely to be sedentary than the non-obese participants. 2.4 is the point estimate obtained from this example; however, the entire population of obese HIV people was not included. If other samples of HIV participants were assessed, the point estimate would likely differ. Some samples might get the point estimate of less than or some greater than 2.4.

The 95% CI is the interval representing the (population) parameter value 95% of the time if an experiment or study is repeated, in that 95 out of 100 intervals would result in the intervals containing the true risk ratio or odds ratio value. For the sedentary and obesity study, the interpretation might conclude that a 2.4 point estimate could range from a low of 1.4713 to a high of 3.9150.

The main strength of the cross-sectional design is the ability to obtain results faster. Investigators do not need to wait for outcomes to occur. Participants either have the condition or attribute at the time of data collection or not. Furthermore, there are no participant follow-ups; therefore, losing study participants during the study is not an issue.

The design’s inherent nature makes it inexpensive to conduct and can yield multiple independent (predictor) and dependent (outcome) variables ( Cummings, 2013 ). The data collected can lead to additional studies to build upon the knowledge obtained. From the example, the investigators learned that obese HIV participants were more likely to be sedentary; the next study might develop a clinical trial to determine the methods to increase activity level in this population.

A significant limitation of using this design is the inability to measure the incidence of a disease or attribute ( Wang & Cheng, 2020 ). Incidence measures the proportion of participants that develop a disease or attribute over time ( Cummings, 2013 ). In other words, investigators need a follow-up phase to determine the incidence . In continuing with the example, if investigators continued to follow the HIV participants who were obese but not sedentary, would additional time (follow-up) result in increased sedentary behavior associated with conditions secondary to aging or worsening of immune status? Unfortunately, the cross-sectional design can not answer this question.

Additionally, the prevalence of a disease or attribute is influenced by the disease’s incidence and survival or disease duration ( Alexander, 2015a ). For example, participants who live longer with a disease will have a higher likelihood of being counted ( Prevalence = # of participants with the condition at the time point/ Total # of participants in the sample ) versus those who are short-term survivors. Moreover, if treatments for a disease or attribute are improved, or the survival time-frame decreases, the disease or attribute’s prevalence will reduce ( Alexander, 2015a ). New information presented to the lay public could also influence the prevalence of a disease or attribute through lifestyle changes (i.e., increasing physical activity, improving diet) or changing jobs if the profession is associated with an identified risk or disease. Therefore, this design does not allow investigators to ascertain the events’ sequence, which came first, obesity or sedentary behavior.

For investigators studying rare diseases or conditions, the cross-sectional design is not the best fit. Cross-sectional studies often draw samples from a large and heterogeneous study population ( Wang & Cheng, 2020 ). Participants with the rare condition of interest might not be identified in the study sample.

Reporting Recommendations

A reporting guideline for cross-sectional studies is available for investigators and consumers of research to use. A reporting guideline’s primary goal is to ensure that published clinical research studies provide transparency in reporting a study’s conduct (what was done) and results. The guideline is a tool investigators can use to develop their manuscripts and offers a checklist of inclusion items for a published paper (Equator.network). The recommended items will help ensure that a reader can understand the manuscript, follow the study’s planning and how the research was conducted, the findings, and the conclusions ( von Elm et al., 2014 ).

For cross-sectional studies, the guideline is titled Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) ( von Elm et al., 2014 ). The STROBE g uideline is a 22-item checklist. The checklist provides essential information for a study to be replicated, useful for healthcare professionals to make clinical decisions, and give enough information for inclusion in a systematic review ( https://www.equator-network.org/reporting-guidelines/strobe/ ).

The cross-sectional design is an appropriate method to determine the prevalence of a disease, attribute, or phenomena in a study sample. The design provides a ‘snapshot” of the sample, and investigators can describe their study sample and review associations between the collected variables (independent and dependent). The observational nature makes it relatively quick to complete a study and provides data to support future studies that might lead to methods to treat or prevent diseases or conditions.

Acknowledgments

This manuscript is supported in part by grant # UL1TR001866 from the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH) Clinical and Translational Science Award (CTSA) program.

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Cross Sectional Study: Overview, Examples & Benefits

By Jim Frost Leave a Comment

What is a Cross Sectional Study?

A cross-sectional study is an experimental design that analyzes data from a representative sample at a specific point in time. Researchers usually evaluate multiple attributes at once when using this design. Unlike longitudinal studies , these studies don’t track changes over time.

Typically, researchers use this design to simply observe the prevalence of a condition or outcome at a specific moment and do not manipulate any variables or treatment conditions. This non-interference makes cross sectional studies a type of observational study . Think of it as taking a ‘photograph’ of a population at a particular time. Researchers collect data once, offering insights into various factors at that point.

Scientists use two broad types of cross-sectional studies:

• Descriptive : Summarizes the prevalence of a condition with descriptive statistics .
• Analytical : Evaluates relationships between variables to understand how outcomes occur.

While analysts can explore correlations between the variables in cross-sectional studies, these studies are not good at identifying causal relationships. Instead, they are relatively inexpensive and quick projects that can lay the groundwork for more in-depth longitudinal studies.

Imagine a study assessing the dietary habits of 5,000 people from different cities at one point in time. This study could reveal prevalent nutritional patterns and health indicators across these populations.

For example, researchers frequently use cross-sectional studies in the following fields:

• Public Health : Assessing health status or disease prevalence in a community.
• Sociology : Understanding current social conditions.
• Market Research : Gauging consumer preferences.
• Education : Evaluating recent educational outcomes.

Learn more about Experimental Designs: Definition and Types .

Duration of Cross-Sectional Studies

Cross-sectional studies are typically shorter in duration compared to longitudinal studies. They are conducted all at once, although the planning and analysis phases can span several months or a year.

Implementing a Cross-Sectional Study: Your Choices

When conducting a cross-sectional study, you face a critical decision: collect new data or use pre-existing datasets.

Option 1: Utilizing Existing Data

Many organizations, including governments and research institutes, frequently release data from cross-sectional studies. A classic example is the U.S. National Health and Nutrition Examination Survey (NHANES), which provides a snapshot of the nation’s health and nutritional status.

Such data is typically robust and can offer immediate insights. However, it’s less customizable than data you collect yourself. The data might be generalized to ensure privacy, limiting detailed analysis. Additionally, the original study’s variables restrict you, and you can’t modify data collection to meet your study’s needs.

If you choose pre-existing data, carefully evaluate the dataset’s source and the specifics of the data provided.

Option 2: Collecting Data Yourself

Opting to collect your own data gives you control over the variables and the nature of the information gathered. Here are some standard data collection methods for a cross-sectional study.

• Surveys and Questionnaires: Ideal for gathering a wide range of information quickly.
• Observational Methods: Useful for capturing data in natural settings.
• Interviews: Provide in-depth insights but can be time-consuming.

For all these methods, selecting a representative sample of your target population is crucial because it ensures the findings apply to the broader population and enhances the study’s overall validity and reliability .

Self-collected data can be tailored to your specific research question, offering depth and relevance. However, this approach requires carefully planning your sampling method to ensure representativeness and avoid biases.

In summary, whether you choose to use existing datasets or collect your own data, each approach has its own set of advantages and challenges. The key is aligning your choice with your research objectives and available resources.

Advantages of a Cross-Sectional Study

A cross-sectional study can be efficient regarding time and resources, making it ideal for initial explorations that might not be possible with longitudinal studies.

These studies can also gather a vast amount of data on various variables at once from a large sample, allowing you to compare subgroups, all of which are more costly in longitudinal studies.

For example, imagine a cross-sectional study surveying 10,000 people nationwide about their dietary habits. This study can simultaneously collect data on numerous variables like age, income, education, and health status.

With such a comprehensive dataset, researchers can assess dietary patterns across different subgroups, such as comparing eating habits between urban and rural residents or analyzing how dietary choices vary with income levels. This ability to gather and compare many variables at once is a crucial strength of this research design, providing a rich, multifaceted snapshot of the population.

Disadvantages of a Cross-Sectional Study

A cross-sectional study can identify correlations but not causal relationships . They record one-time measurements and can’t determine a sequence of events. In essence, they simultaneously measure possible causes and effects, making them hard to distinguish.

For example, a this type of research might find a correlation between high stress levels and poor sleep quality, but it can’t confirm if stress causes poor sleep or vice versa.

These studies provide only a snapshot and cannot track behaviors or trends over time. Consequently, they can’t establish how variables evolve or interact longitudinally.

For example, a survey conducted during an economic downturn might reflect unusually high levels of financial stress in the population, which may not be indicative of general long-term trends.

In conclusion, cross-sectional studies offer valuable insights into the status of a population at a specific point. They are handy for exploratory research and identifying potential areas for more in-depth study. Understanding their strengths and limitations is crucial for researchers to utilize this method effectively.

Wang, X., & Cheng, Z. (2020). Cross-Sectional Studies: Strengths, Weaknesses, and Recommendations . Chest, 158(1), S65-S71. DOI:10.1016/j.chest.2020.03.012.

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Quantitative study designs: Cross-Sectional Studies

Quantitative study designs.

• Introduction
• Cohort Studies
• Randomised Controlled Trial
• Case Control
• Cross-Sectional Studies
• Study Designs Home

Cross-Sectional Study

The Australian Census run by the Australian Bureau of Statistics, is an example of a whole of population cross-sectional study.

Data on a number of aspects of the Australian population is gathered through completion of a survey within every Australian household on the same night. This provides a snapshot of the Australian population at that instance.

Cross-sectional studies look at a population at a single point in time, like taking a slice or cross-section of a group, and variables are recorded for each participant.

This may be a single snapshot for one point in time or may look at a situation at one point in time and then follow it up with another or multiple snapshots at later points; this is then termed a repeated cross-sectional data analysis. 

The stages of a Cross-Sectional study

Repeated Cross-Sectional Data Analysis

Which clinical questions does a Cross-Sectional study best answer?

Please note the Introduction , where there is a table under "Which study type will answer my clinical question?" .  You may find that there are only one or two question types that your study answers – that’s ok. 

Cross-sectional study designs are useful when:

• Answering questions about the incidence or prevalence of a condition, belief or situation.
• Establishing what the norm is for a specific demographic at a specific time. For example: what is the most common or normal age for students completing secondary education in Victoria?
• Justifying further research on a topic. Cross-sectional studies can infer a relationship or correlation but are not always sufficient to determine a direct cause. As a result, these studies often pave the way for other investigations.  

What are the advantages and disadvantages to consider when using a Cross-Sectional study design?

What does a strong Cross-Sectional study look like?

• Appropriate recruitment of participants. The sample of participants must be an accurate representation of the population being measured.
• Sample size. As is the case for most study types a larger sample size gives greater power and is more ideal for a strong study design. Within a cross-sectional study a sample size of at least 60 participants is recommended, although this will depend on suitability to the research question and the variables being measured.
• A suitable number of variables. Cross-sectional studies ideally measure at least three variables in order to develop a well-rounded understanding of the potential relationships of the two key conditions being measured.

What are the pitfalls to look for?

Cross-sectional studies are at risk of participation bias, or low response rates from participants. If a large number of surveys are sent out and only a quarter are completed and returned then this becomes an issue as those who responded may not be a true representation of the overall population.

Critical appraisal tools 

To assist with critically appraising cross-sectional studies there are some tools / checklists you can use.

• Axis Appraisal Tool for Cross Sectional Studies
• Critical Appraisal Tool for Cross- Sectional Studies (CAT-CSS)
• Critical appraisal tool for cross-sectional studies using biomarker data (BIOCROSS)
• CEBM Critical Appraisal of a Cross-Sectional Study (Survey)
• JBI Critical Appraisal checklist for analytical cross-sectional studies
• Specialist Unit for Review Evidence (SURE) 2018. Questions to assist with the critical appraisal of cross sectional studies
• STROBE Checklist for cross-sectional studies

Real World Examples

The Australian National Survey of Mental Health and Wellbeing (NSMHWB)

https://www.abs.gov.au/statistics/health/mental-health/national-survey-mental-health-and-wellbeing-summary-results/2007

A widely known example of cross-sectional study design, the Australian National Survey of Mental Health and Wellbeing (NSMHWB). This study was a national epidemiological survey of mental disorders investigating the questions: How many people meet DSM-IV and ICD-10 diagnostic criteria for the major mental disorders? How disabled are they by their mental disorders? And, how many have seen a health professional for their mental disorder?

References and Further Reading

Australian Government Department of Health. (2003). The Australian National Survey of Mental Health and Wellbeing (NSMHWB). 2019, from https://www.abs.gov.au/statistics/health/mental-health/national-survey-mental-health-and-wellbeing-summary-results/2007

Bowers, D. a., Bewick, B., House, A., & Owens, D. (2013). Understanding clinical papers (Third edition. ed.): Wiley Blackwell.

Gravetter, F. J. a., & Forzano, L.-A. B. (2012). Research methods for the behavioral sciences (Fourth edition. ed.): Wadsworth Cengage Learning.

Greenhalgh, T. a. (2014). How to read a paper : the basics of evidence-based medicine (Fifth edition. ed.): John Wiley & Sons Inc.

Hoffmann, T. a., Bennett, S. P., & Mar, C. D. (2017). Evidence-Based Practice Across the Health Professions (Third edition. ed.): Elsevier.

Howitt, D., & Cramer, D. (2008). Introduction to research methods in psychology (Second edition. ed.): Prentice Hall.

Kelly, P. J., Kyngdon, F., Ingram, I., Deane, F. P., Baker, A. L., & Osborne, B. A. (2018). The Client Satisfaction Questionnaire‐8: Psychometric properties in a cross‐sectional survey of people attending residential substance abuse treatment. Drug and Alcohol Review, 37(1), 79-86. doi: 10.1111/dar.12522

Lawrence, D., Hancock, K. J., & Kisely, S. (2013). The gap in life expectancy from preventable physical illness in psychiatric patients in Western Australia: retrospective analysis of population based registers. BMJ: British Medical Journal, 346(7909), 13-13.

Nasir, B. F., Toombs, M. R., Kondalsamy-Chennakesavan, S., Kisely, S., Gill, N. S., Black, E., Ranmuthugala, G., Ostini, R., Nicholson, G. C., Hayman, N., & Beccaria, G.. (2018). Common mental disorders among Indigenous people living in regional, remote and metropolitan Australia: A cross-sectional study. BMJ Open , 8 (6). https://doi.org/10.1136/bmjopen-2017-020196

Robson, C., & McCartan, K. (2016). Real world research (Fourth Edition. ed.): Wiley.

Sedgwick, P. (2014). Cross sectional studies: advantages and disadvantages. BMJ : British Medical Journal, 348, g2276. doi: 10.1136/bmj.g2276

Setia, M. S. (2016). Methodology Series Module 3: Cross-sectional Studies. Indian journal of dermatology, 61(3), 261-264. doi: 10.4103/0019-5154.182410

Shafiei, T., Biggs, L. J., Small, R., McLachlan, H. L., & Forster, D. A. (2018). Characteristics of women calling the panda perinatal anxiety & depression australia national helpline: A cross-sectional study. Archives of Women's Mental Health. doi: 10.1007/s00737-018-0868-4

Van Heyningen, T., Honikman, S., Myer, L., Onah, M. N., Field, S., & Tomlinson, M. (2017). Prevalence and predictors of anxiety disorders amongst low-income pregnant women in urban South Africa: a cross-sectional study. Archives of Women's Mental Health(6), 765. doi: 10.1007/s00737-017-0768-z

Vogt, W. P. (2005). Dictionary of statistics & methodology : a nontechnical guide for the social sciences (Third edition. ed.): Sage Publications.

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Cross-Sectional Study: What it is + Free Examples

A cross-sectional study is used to collect data from a population simultaneously. It is a snapshot of the population at a particular moment rather than a study that tracks changes over time. This design is often used in fields such as public health, sociology, and psychology to gather information about the characteristics, attitudes, and behaviors of a group of individuals .

This blog will discuss what cross-sectional studies are. We’ll review examples and explain the types of cross-sectional studies you might perform. We’ll also take a closer look at the benefits of this valuable research for the work you do.

What is a Cross-Sectional Study?

A cross-sectional study is a type of observational research that analyzes data of variables collected at one given point in time across a sample population or a pre-defined subset.

This study type is also known as cross-sectional analysis, transverse study, or prevalence study. Although this research does not involve conducting experiments, researchers often use it to understand outcomes in the physical and social sciences and many business industries.

Characteristics of Cross-Sectional Studies

When researchers conduct cross-sectional studies, they look at a specific group of people at a single point in time. Here are some simple characteristics of cross-sectional studies that might help you understand them better:

• Researchers can conduct cross-sectional studies with the same set of variables over a set period.
• Similar research may look at the same variable of interest, but each study observes a new set of subjects.
• The cross-sectional analysis assesses topics during a single instance with a defined start and stopping point, unlike longitudinal studies, where variables can change during extensive research.
• Cross-sectional studies allow the researcher to look at one independent variable and one or more dependent variables as the focus of the cross-sectional study.

Want a fitting metaphor? Think of a snapshot of a group of people at one event, say a family reunion. The people in that extended family are used to determine what is happening in real-time at the moment.

All people have at least one variable in common – being related – and multiple variables they do not share. You could make all kinds of observations and analyses from that starting point. Hence, this research type “takes the pulse” of population data at any given time.

You can also use this type of research to map prevailing variables that exist at a particular given point—for example, cross-sectional data on past drinking habits and a current diagnosis of liver failure.

Cross-Sectional Study Examples

The data collected in cross-sectional studies involves subjects or participants who are similar in all variables – except the one that is under review. This variable remains constant throughout the study. This is unlike a longitudinal study , where variables can change throughout the research. Consider these examples for more clarity:

• Retail: In retail, this research can be conducted on men and women in a specific age range to reveal similarities and differences in spending trends related to gender.
• Education : Cross-sectional studies in school are beneficial in understanding how students who scored within a particular grade range in the same preliminary courses perform with a new curriculum .
• Healthcare: Scientists in healthcare may use cross-sectional studies to understand how children ages 2-12 across the United States are prone to calcium deficiency.
• Business: In business, researchers can study how people of different socio-economic statuses from one  geographic segment  respond to one change in an offering.
• Psychology: The cross-sectional study definition in psychology is research that involves different groups of people who do not share the same variable of interest (like the variable you’re focusing on) but who do share other relevant variables. These could include age range, gender identity, socio-economic status, and so on.

This research allows scholars and strategists to quickly collect cross-sectional data that helps in decision-making and offering products or services.

Types of Cross-Sectional Studies

When you conduct a cross-sectional research study, you will engage in one or both types of research: descriptive or analytical. Read their descriptions to see how they might apply to your work.

• Descriptive Research:  A cross-sectional study may be entirely descriptive research . A cross-sectional descriptive survey assesses how frequently, widely, or severely the variable of interest occurs throughout a specific demographic . Please think of the retail example we mentioned above. In that example, researchers make focused observations to identify spending trends. They might use those findings to develop products and services and market existing offerings. They aren’t necessarily looking at why these gendered trends occur in the first place.
• Analytical Research: A cross-sectional survey investigates the association between two related or unrelated parameters. This research isn’t entirely foolproof, though, because outside variables and outcomes are simultaneous, and their studies are, too. For example, to validate whether coal miners could develop bronchitis, look only at the variables in a mine. What it doesn’t account for is that a predisposition to bronchitis could be hereditary, or this health condition could be present in the coal workers before their employment in the mine. Other medical research has shown that coal mining is detrimental to the lungs, but you don’t want those assumptions to bias your current study.

Researchers usually use descriptive and analytical research methods in real-life cross-sectional studies.

Benefits of a Cross-Sectional Study

Are you curious whether this research is the right approach for your next study? A Cross-Sectional Survey is an efficient and revealing way to collect data. Check out some of the critical advantages of conducting online research using cross-sectional studies and see if it’s a good fit for your needs.

• Relatively quick to conduct.
• Researchers can collect all variables at a point in time.
• Multiple outcomes can be researched at once.
• Prevalence for all factors can be measured.
• Suitable for descriptive analysis .
• Researchers can use it as a springboard for further research.

If you are looking for an approach that studies subjects and variables over time, you might prefer a longitudinal study. Additionally, you could follow your research with a longitudinal study. It is easy to confuse the two research methods, so we’ve broken it down here:

We recently published a blog that talks about Causal Research ; why don’t you check it out for more ideas?

Cross-Sectional vs. Longitudinal Studies

Although they are both quantitative research methods, there are a few differences when comparing and contrasting cross-sectional and  longitudinal studies .

Researchers prefer cross-sectional studies to find common points between variables. Still, they use longitudinal studies, due to their nature, to dissect the research from the cross-sectional studies for further research.

Examples of Cross-Sectional Data

Now that you have a better understanding of what cross-sectional research is and how to perform your studies, let’s look at two examples in more detail:

Example 1: Gender and Phone Sales

Phone companies rely on advanced and innovative features to drive sales.  Research  by a phone manufacturer throughout the target demographic market validates the expected adoption rate and potential phone sales. In cross-sectional studies, researchers enroll men and women across regions and age ranges for research. 

If the results show that Asian women would not buy the phone because it is bulky, the mobile phone company can tweak the design to make it less bulky. They can also develop and market a smaller phone to appeal to a more inclusive group of women.

Example 2: Men and Cancer

Another example of a cross-sectional study would be a medical study examining the prevalence of cancer amongst a defined population. The researcher can evaluate people of different ages, ethnicities, geographical locations, and social backgrounds.

If a significant number of men from a particular age group are more prone to have the disease, the researcher can conduct further studies to understand the reasons. A longitudinal study is best used, in this case, to study the same participants over time.

Create and Analyse a Cross-Sectional Study Survey

It’s your turn! Whether you’re building a marketing strategy or performing a cutting-edge medical study, you can get started by creating an intuitive survey from QuestionPro. Please choose from one of our 350+ survey templates, or build your own and leverage our reporting tools to discover deep insights to apply to your best work.

You can use single-ease questions . A single-ease research question is a straightforward query that elicits a concise and uncomplicated response.

Also, you can find advanced data analysis tools such as trend analysis and dashboards to visualize your information and do your own cross-sectional studies simply and efficiently.

A cross-sectional study provides valuable insights into a population’s characteristics, attitudes, and behaviors at a single point in time. As with any research design , cross-sectional studies should be used with other research methods to provide a complete study. Overall, cross-sectional studies can be a valuable tool for researchers looking to understand a population quickly.

With QuestionPro, you can conduct cross-sectional studies with ease. QuestionPro provides various tools for analyzing your collected data, cross-tabulation, and more. Whether you’re a researcher, marketer, or business professional, QuestionPro can help you gather the data you need to make informed decisions.

LEARN MORE         FREE TRIAL

Frequently Asked Questions (FAQ)

A cross-sectional study is a type of research that collects data from a group of people at a single point in time to analyze characteristics and relationships.

They are valuable for understanding the current status of a condition or behavior within a population, making them great for initial assessments.

Cross-sectional studies capture data at a one-time point, while longitudinal studies track the same individuals over an extended period to observe changes.

It’s cost-effective, quick to conduct, and provides a broad view of a population’s characteristics or behaviors at a specific time.

The primary goal of a cross-sectional study is to examine and analyze the relationships or associations between different variables within a population at a specific point in time.

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Methodology Series Module 3: Cross-sectional Studies

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15 Cross-Sectional Study Examples

Dave Cornell (PhD)

Dr. Cornell has worked in education for more than 20 years. His work has involved designing teacher certification for Trinity College in London and in-service training for state governments in the United States. He has trained kindergarten teachers in 8 countries and helped businessmen and women open baby centers and kindergartens in 3 countries.

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Chris Drew (PhD)

This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.

A cross-sectional study is a research methodology that involves collecting data on a sample of individuals at one specific point in time.

The researcher(s) will collect data on various factors, all at the one time, and observe how those variables are related to other factors.

In this type of study, researchers do not manipulate any variables, but rather observe their interconnected influence on specific variables within the sample of individuals being studied.

The usual purpose of cross-sectional research is descriptive; to paint a picture of an existing relation between variables within a given population or subgroup.

Cross-sectional studies are often implemented in developmental psychology to examine factors that impact children, medical research to identify determinants of certain health outcomes, or in economics research to understand how predictor variables relate to outcome variables.

Cross-Sectional Studies: Definition and  Overview

In a cross-sectional study, the sample of individuals studied is extremely important. Sample groups can be one of two types: heterogeneous or homogeneous .

• A heterogeneous sample is a diverse sample that includes individuals from various demographics, such as different ages, races, and genders.
• A homogeneous sample includes individuals that are all similar on at least one factor. For example, the sample may consist of only a specific age group or gender.

The more homogeneous the sample, the less generalizability the study’s results have to the wider population.

For example, a sample of college students may allow comparisons within males and females in that sample, but it will be difficult to say the results apply to older populations or non-college students.

Cross-Sectional vs. Longitudinal Research

Cross-sectional research collects data on one sample at one point in time, whereas longitudinal research collects multiple datapoints over a longer period of time.

1. Cross-Sectional Research

A cross-sectional study allows researchers to make comparisons among different groups within the sample, but is not particularly useful for analyzing changes over time.

2. Longitudinal Research

Longitudinal research collects data on the same sample over a longer period of time.

Sometimes that period of time will consist of just a few years, while in other studies it could consist of decades, depending on the study’s purpose.  Collecting data over a several years or decades allows researchers to examine how variables change over time.

Differences between Cross-Sectional and Longitudinal Research

An important difference between the two types of research has to do with the concept of causality .

Ideally, researchers want to know what causes behavior or a health outcome.

Many types of research designs do not allow the assessment of causality. Researchers can identify factors that are related to , or connected with , or even statistically correlated with , other variables, but each of those terms is weaker than the notion of causality.

Because longitudinal research occurs over time, researchers have more confidence on inferring causality among predictor variables and outcome variables.

Note however, that because longitudinal research does not involve the researchers manipulating the level of a variable, inferences regarding causality are still cautionary.

Cross-sectional research involves only static data collected at a single point in time. Therefore, inferences regarding causality cannot be made.

Cross-Sectional Study Examples

• Online Learning and Student Engagement: Education researchers wanted to examine if online learning makes student engagement difficult. Therefore, the researchers administered a survey to 100 students during the month of December that asks questions about how motivated they feel during online classes.
• Health Differences between Rural and Non-Rural Populations: Health researchers accessed data from the CDC to examine the health differences and health-related habits between individuals living in rural areas compared to those living in non-rural areas.
• Depression in the Elderly: Several hundred elderly individuals were administered a depression inventory and asked several questions regarding social and family support, income level, and marital status. The results found that being in a nuclear family system and being single or divorced were significant predictors of depression.
• Motivation and Academic Performance: Students in a primary school were administered a questionnaire designed to assess their level of motivation to study. The scores on this measure were then correlated with students’ grades.
• In Marketing Research: The marketing department of a large corporation examined consumer preferences and demographic variables. The week after an expensive ad campaign they collected sales data in different cities and compared purchases of different age groups, gender, and education levels.
• Verbal Fluency and Parents’ Education Level: Researchers were interested in determining if there is a relation between the education level of parents and their children’s verbal fluency. So, they examined school records of several districts and correlated parents’ education with children’s score on the verbal section of an achievement test.
• Stress and Psychological Well-Being: A questionnaire was placed online in a particular FB group. It asked members of the group to respond to a survey that measures stress and one that measures psychological well-being. Demographic data was also collected regarding age and gender. The researchers then correlated level of stress with level of well-being to determine if there was a connection.
• Sleep and Grades: Teachers at a secondary school were concerned about their students not getting enough sleep. So, they sent questionnaires home with students that asked parents to estimate the number of hours their child slept each night. The teachers then correlated that data with the students’ grades.
• EQ and Burnout in Nursing: Researchers administered a large questionnaire to assess EQ, spirituality, various personality characteristics, and burnout among experienced nurses. Thorough statistical analyses identified that, among several other findings, EQ effects work investment which then affects burnout, but spirituality can help mitigate the effects.
• Physical Activity and Obesity in Adolescents: Researchers in a city surveyed adolescents about their daily physical activities and recorded their Body Mass Index (BMI). They investigated whether higher levels of physical activity correlate with a lower BMI, suggesting a lower risk of obesity.
• Socioeconomic Status and Mental Health: Psychologists collected data on individuals’ socioeconomic status, including their income, education, and occupation. They also gathered data on their mental health status using validated scales. The aim was to explore the relationship between socioeconomic status and mental health.
• Exercise and Bone Density: Medical researchers collected data on the regularity and intensity of exercise in adults and correlated it with their bone density levels. The study aimed to identify the role of exercise in maintaining good bone health.
• Dietary Habits and Cardiovascular Health: In a study, data was collected from adults about their daily dietary habits. The information was then correlated with measures of cardiovascular health, such as blood pressure and cholesterol levels, to examine the impact of diet on heart health.
• Climate Change Awareness and Recycling Behavior: An environmental organization conducted a study to determine the correlation between people’s awareness of climate change issues and their recycling behavior. They distributed surveys in various communities and analyzed the responses.
• Work Environment and Job Satisfaction: HR researchers distributed questionnaires to employees in several organizations, investigating factors such as workload, work-life balance, and leadership quality. The data collected was then correlated with self-reported job satisfaction levels to understand the impact of the work environment on employee happiness.

Strengths and Weaknesses of Cross- Sectional Research

For a full discussion of the strengths and weaknesses of cross-sectional research, see my article here .

A cross-sectional study is a valuable research methodology that allows scientists to determine the relationship between different variables and how they are connected with a specific outcome variable.

The procedure involves collecting data from one group of individuals at the same time. This can be accomplished by distributing surveys or by accessing large data sets that are maintained by government institutions or private companies.

The advantages of cross-sectional research include the ease and efficiency of collecting lots of data, the opportunity to examine how numerous factors are related, and the ability to identify factors that should be studied further.

One of the biggest disadvantages of cross-sectional research is not being able to infer a causal relationship between the factors studied and the outcome variable of primary interest.

Other disadvantages include low response rates, participants unable or unwilling to answer questions accurately and honestly, or the characteristic of the sample limiting the generalizability of the results.

Anderson, T. J., Saman, D. M., Lipsky, M. S., & Lutfiyya, M. N. (2015). A cross-sectional study on health differences between rural and non-rural US counties using the County Health Rankings. BMC Health Services Research , 15 , 1-8.

Bland, M. (2015). An introduction to medical statistics . Oxford University Press.

Kaur, D., Sambasivan, M., & Kumar, N. (2013). Effect of spiritual intelligence, emotional intelligence, psychological ownership and burnout on caring behaviour of nurses: A cross‐sectional study. Journal of Clinical Nursing , 22 (21-22), 3192-3202.

Levin, K. A. (2006). Study design III: Cross-sectional studies. Evidence-based Dentistry , 7 (1), 24-25.

Manfreda, K. L., Bosnjak, M., Berzelak, J., Haas, I., & Vehovar, V. (2008). Web surveys versus other survey modes: A meta-analysis comparing response rates. International Journal of Market Research , 50 (1), 79-104.

Sindiani, A. M., Obeidat, N., Alshdaifat, E., Elsalem, L., Alwani, M. M., Rawashdeh, H., … & Tawalbeh, L. I. (2020). Distance education during the COVID-19 outbreak: A cross-sectional study among medical students in North of Jordan. Annals of Medicine and Surgery , 59 , 186-194.

Szklo, M., & Nieto, F. J. (2014). Epidemiology: beyond the basics . Jones & Bartlett Publishers.

Taqui, A. M., Itrat, A., Qidwai, W., & Qadri, Z. (2007). Depression in the elderly: Does family system play a role? A cross-sectional study. BMC Psychiatry , 7 (1), 1-12.

Ullman, J. B., & Bentler, P. M. (2012). Structural equation modeling. Handbook of Psychology, Second Edition , 2 .

• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 23 Achieved Status Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 25 Defense Mechanisms Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 15 Theory of Planned Behavior Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 18 Adaptive Behavior Examples

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 23 Achieved Status Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Ableism Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 25 Defense Mechanisms Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Theory of Planned Behavior Examples

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Cross-Sectional Studies: Strengths, Weaknesses, and Recommendations

Affiliations.

• 1 Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH. Electronic address: [email protected].
• 2 Department of Respiratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China.
• PMID: 32658654
• DOI: 10.1016/j.chest.2020.03.012

Cross-sectional studies are observational studies that analyze data from a population at a single point in time. They are often used to measure the prevalence of health outcomes, understand determinants of health, and describe features of a population. Unlike other types of observational studies, cross-sectional studies do not follow individuals up over time. They are usually inexpensive and easy to conduct. They are useful for establishing preliminary evidence in planning a future advanced study. This article reviews the essential characteristics, describes strengths and weaknesses, discusses methodological issues, and gives our recommendations on design and statistical analysis for cross-sectional studies in pulmonary and critical care medicine. A list of considerations for reviewers is also provided.

Keywords: bias; confounding; cross-sectional studies; prevalence; sampling.

Copyright © 2020 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

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Frequently asked questions

What is the difference between a longitudinal study and a cross-sectional study.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

Frequently asked questions: Methodology

Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extramarital affairs)

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

• Reproducing research entails reanalyzing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.

A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.

The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as “people watching” with a purpose.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation).

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with “yes” or “no” (questions that start with “why” or “how” are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when: 

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyze your data quickly and efficiently.
• Your research question depends on strong parity between participants, with environmental conditions held constant.

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions.
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualize your initial thoughts and hypotheses.
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts.

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order. 
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalization: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Triangulation can help:

• Reduce research bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analyzing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. 

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps: 

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or 
• Send it onward to the selected peer reviewer(s) 
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made. 
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.

These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

In multistage sampling , you can use probability or non-probability sampling methods .

For a probability sample, you have to conduct probability sampling at every stage.

You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

To find the slope of the line, you’ll need to perform a regression analysis .

Correlation coefficients always range between -1 and 1.

The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.

These are the assumptions your data must meet if you want to use Pearson’s r :

• Both variables are on an interval or ratio level of measurement
• Data from both variables follow normal distributions
• Your data have no outliers
• Your data is from a random or representative sample
• You expect a linear relationship between the two variables

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

Random error  is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .

You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.

Systematic error is generally a bigger problem in research.

With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.

Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.

Random and systematic error are two types of measurement error.

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).

Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).

On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment.
• Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

There are 4 main types of extraneous variables :

• Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
• Experimenter effects : unintentional actions by researchers that influence study outcomes.
• Situational variables : environmental variables that alter participants’ behaviors.
• Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes

Disadvantages:

• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.
• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.

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

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

• It’s caused by the independent variable .
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

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

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

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

Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalization .

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
• You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

• The type of soda – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .

Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.

Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.

The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).

The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g. the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g. water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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19 Advantages and Disadvantages of Cross Sectional Studies

A cross-sectional study involves the review of information from a population demographic at a specific point in time. The participants who get involved with this research are selected based on particular variables that researchers want to study. It is often used in developmental psychology, but this method is also useful in several other areas. Social sciences and educational processes benefit from this work.

Researchers who are following cross-sectional study techniques would study select groups of people in different age demographics. Their work would look at one investigatory point at a time. By taking this approach, any differences that exist between the demographics would be attributed to characteristics instead of something that happens.

These studies are observational in nature. They are sometimes described as descriptive research, but not causal or relational. That means researchers are unable to determine the cause of something, such as an illness, when using this method.

Several advantages and disadvantages are worth considering when looking at cross-sectional studies.

List of the Advantages of Cross-Sectional Studies

1. This study takes place during a specific moment in time. A cross-sectional study has defined characteristics that limit the size and scope of the work. Researchers look at specific relationships that happened during a particular moment in time. That means there are fewer risks to manage if tangents begin to develop in the data. The goal is to look for a meaningful result within an expected boundary.

2. No variable manipulation occurs with a cross-sectional study. Researchers directly observe the variables under study when using the cross-sectional technique. There is no reason to manipulate the environment because this is not an experimental technique. The data gathering process goes quickly because everything occurs within the scope of the research method. This advantage reduces the risk of having bias creep into the information being gathered.

3. It is an affordable way to conduct research. A cross-sectional study is much more affordable to complete when compared to the other options that are available to researchers today. No follow up work is necessary when taking this approach because once the information gets collected from the entire participant group, it can be analyzed immediately. This advantage as possible because only a single time reference is under consideration.

This approach allows for usable data to become available without the risk of a significant initial investment. Most of the data points collected using this method come from self-report surveys. Researchers can then collect a significant amount of information from a large pool of participants without a major time investment.

4. This study method provides excellent controls over the measurement process. Cross-sectional studies are only as good as the measurement processes that researchers used to collect data. Because there aren’t any long-term considerations involved with this specific approach, researchers have more control over the information acquisition process. Everything obtained during this work is quickly and easily measured and applied to the targeted demographics because the controls involved are straightforward to implement.

5. Researchers can look at several useful characteristics at once. Many researchers prefer the cross-sectional studies method because it allows them to look at numerous characteristics simultaneously. Instead of focusing on income, gender, age, or other separating factors, this method looks at each participant as an entire individual. That makes it possible for the work to include several useful characteristics that can each benefit from changing variables instead of using only one to determine an outcome.

This advantage is the reason why researchers often use cross-sectional studies to look at the prevailing characteristics in a given population. It is a process that lets different variables become the foundation of new correlations.

6. It provides relevant information in real-time updates. Cross-sectional studies provide us with a snapshot of a specific group of people at a particular point in time. Unlike other methods of research that look at demographics over an extended period, we use this information to look at what is happening in the present. That means the data researchers collect from this process is immediately relevant, giving us an opportunity to create real-time updates within specific population groups.

This process is how we can determine if there are specific risk factors that correlate to particular outcomes wit in that group. A cross-sectional study might look at a person’s past smoking and chewing habits to determine if there is a correlation with a recent lung cancer diagnosis. Although it won’t provide a cause-and-effect explanation, it does offer a fast look at potential correlations.

7. Cross-sectional studies miss fewer data points. The processes involved with cross-sectional studies reduce the risk of missing critical data points. Researchers have the ability to maximize their examination of the available information at any time because there are no time variables included in this work. That means a lower error rate typically occurs when using this method compared to the other approaches that are available to the scientific community.

8. It allows anyone to look at the data to determine a possible conclusion. The information that cross-sectional studies obtain is always suitable for secondary analysis. This advantage means that researchers can collect information for one set of purposes, and then use it to explore different variables that might exist in that specific demographic at the same time. That means an investment in this work can provide ongoing usefulness because it always applies to the people involved during that specific time. It is one of the easiest ways to maximize your research investment value.

9. Cross-sectional studies offer information that’s well-suited for descriptive analysis. If researchers want to develop a general hypothesis, then cross-sectional studies are the best way to generate specific situations that face a particular demographic. Each description of the critical data points creates the possibility of forwarding movement toward a future solution that may not have been considered previously.

Although this benefit doesn’t apply to causal relationships with this research method, the information collected from cross-sectional studies is a useful forward push toward additional research.

10. The focus of a cross-sectional study is to prove or disprove an assumption. A cross-sectional study is a research tool that is useful across various industries. The reason why it is such a generalized process that anyone can initiate is that the purpose of the work is to prove or disapprove and assumption or theory. Although health-related work tends to be the most popular industry that takes advantage of this approach, retail, education, social science, religion, and government industries can also benefit from this process.

The research that occurs allows each industry to learn more about the various demographics for the purpose of analyzing a target market. It creates data that’s useful when trying to determine what products or services to sell, or when it is necessary to look for specific patient outcomes.

List of the Disadvantages of Cross-Sectional Studies

1. It requires the entire population to be studied to create useful data. A correctly structured cross-sectional study must be representative of an entire demographic for it to provide useful information. If this representation is not possible, then the data collected from the participating individuals will have a built-in error rate that must come under consideration.

That’s why a complete generalization is not possible when using this approach. Environmental conditions, a person’s education, and several other factors can all change an individual’s perspective.

2. A researcher’s personal bias can influence the data from cross-sectional studies. Everyone has particular biases that influence their personality and general perspective on life. Many of these circumstances come from the conditioning that happens over the course of time. Even people who work hard to avoid showing bias in any situation can come under the influence of this disadvantage of cross-sectional studies.

Some demographics might include prison populations, the homeless, or people who are unable to leave their homes. If a researcher feels uncomfortable contacting individuals in these groups, then the final data points will not have as much relevance as they could.

3. The questions asked during cross-sectional studies may lead to specific results. If researchers want to achieve a specific result when performing a cross-sectional study, then they can ask questions in such a way that it leads participants to the desired answer. When there are surveys or questionnaires about specific aspects of a person’s life, then the answers received may not always result in an accurate report. Shared experiences can result in different perspectives.

We have seen this disadvantage play out numerous times throughout several generations. The people who were alive during the Vietnam war, the attack on Pearl Harbor, or the terrorist event in New York City on 9/11 have shared experiences that make them different from other age groups. The people who survived these events are another subgroup that can impact the quality of information gathered.

4. Large sample sizes are often necessary to generate usable information. A significant sample size is often necessary for a cross-sectional study to provide useful information. This disadvantage occurs because the entire population demographic must go through the research at once to prevent errors in the data. When a smaller sample size is the focus of the work, then the risk of errors entering into the information increases dramatically. There are more opportunities for coincidence or a chance to influence the results with a smaller research sample.

Although cross-sectional studies are often very affordable, the inclusion of an entire demographic pushes the cost of this work higher than it would be for other approaches.

5. Cross-sectional studies don’t offer any control over purpose or choice. When the data from a cross-sectional study is found useful for secondary data analysis, the bias of the researchers can influence the information without any future realization. The secondary approach has no control over how this work gets completed initially. That’s why an overview of the methods used and the purpose for collecting information in the first place are often included as part of the results of this work.

If these additional facts are not part of the final experience, and the usefulness of the information for future needs becomes questionable.

6. No information about causal relationships is possible with this approach. This research method does not provide information about causal relationships. The goal of this approach is to offer correlated data that is useful when drawing conclusions about a specific demographic. It can only let researchers see that a causal relationship exists without letting them know the reason behind its existence.

That’s why individualization is a disadvantage to this type of study. Researchers are wanting to see a generalized overview of a specific population sample instead of understanding why some people make particular choices.

7. Demographic definitions must be available to create a. successful result. The information collected during a cross-sectional study is not reliable unless there are specific definitions in place for a population sample that is large enough for generalization. If researchers want to look at a rare outcome or a unique event, then inappropriate conclusions could get gleaned from the collected data. Trying to force a specific question or result could encourage responses that are unnecessary within the study population.

The only way to avoid this disadvantage of a cross-sectional study is to create definitions that work specifically with the intended results.

8. Cross-sectional studies have no way to measure incidence. The goal of a cross-sectional study is to review the data that researchers collect as they study-specific variables. It does not take a look at the reason why the specific information points occur within the population demographics. This disadvantage limits the availability of an outcome for researchers in many situations because there is no determination available as to why the variables are present initially.

It only measures the existence and relationships that are present in that environment, not what triggers the variables.

9. It can be challenging to duplicate the results. Even though a large population sample is necessary to create an accurate dataset from a cross-sectional study, it is challenging to duplicate results from multiple efforts. This disadvantage occurs because work happens in real-time situations. What happens right now can create a very different result then what could happen in the future.

That’s why many institutions face some challenges when they attempt to put together a sampling pool. The variables that should be studied are available in complex ways that may be difficult to manage. This issue is so detrimental that the timing of a specific snapshot is never a 100% guarantee that it’s representative of the entire population group.

A cross-sectional study is a useful research tool in most areas of health and wellness. When we can learn more about what is happening within a specific population demographic, then researchers can better understand the relationships that could exist between particular variables. The information that comes out of this process allows us to develop further studies that can explore the results in greater depth.

Several other research study options are available when there is a need to collect information from a specific demographic. It is essential to compare the critical points from each approach to determine what the best possible solution will be for each situation.

These cross-sectional advantages and disadvantages show us that a massive undertaking of simultaneous data collection can provide unique results that can benefit an entire population group. Although there are some challenges to manage when taking this approach, it is one that most researchers find to be beneficial.

Calculating Earthworks Cut & Fill With A Spreadsheet | Cross Section Method

by Kate Pettitt | Mar 25, 2021 | Earthworks , Tutorial

Previously, in regard to earthworks estimation, we have written two articles on the Cross Section and Grid Methods. Read these in our Industry Articles library.

This article demonstrates how to calculate earthworks cut and filling using the Cross Section Method and a spreadsheet. Starting with pen and paper we will show you how to generate and input the cross section data to calculate the cut and fill volumes in the spreadsheet. (There is a separate blog which explains how to use the Grid Method sheet)

Download the spreadsheet to use with the tutorial.

CROSS SECTION METHOD – SUMMARY

The cross-section method is when you divide your site into equal parallel sections. Imagine slicing through the earth with a knife at regular intervals.

For each cross section the cut area and the fill area are calculated. Then for each cross section pair, the cut and fill volume of the section between them is approximated  by finding the average of two adjacent sections and multiplying by the distance between them.

An example of a cross sections and sums done to calculate the volume is below:

The formula to calculate the volume between two sections: 

Average Cut Area = (4295 + 2565)/2 = 3430m sq

Average Fill Area = (5376 + 5906)/2 = 5641m sq

Distance Between Sections = 50m 

Cut Volume = 3430 x 50 = 171,500m cu

Fill Volume =  5641 x 50 = 282,050m cu

For the whole site, the total volume is determined by adding all the pairs of cut volumes and the fill volumes together. 

USING A SPREADSHEET

As there are so many calculations to complete, another method is using a spreadsheet, such as the one we have provided.

The spreadsheet : 

a)  Creates a graph showing the profile of the Existing and Proposed for each section.   This shows the existing levels in blue and proposed in orange. 

b)  Calculates the cut and fill area for each section.   Shown next to the profile graph.

c)  Calculates the overall total Cut, Fill and Net Volume.  Shown in the Results table.

4. Collect your data.   You now need to take off the existing (E) and proposed (P) levels and write them next to each significant change in elevation, to produce a cross section.  An example, showing one way to do this, is in the photo.  It is key to use your fine pen or pencil here to ensure you can fit in the two levels and so they remain legible.

In this example, the levels have been defined using contour lines.

i) For each contour make a mark on your section and write the level.  

I have used two colours to make the mark; black for existing and written the level above the section line and pink for the proposed and the level is written below the line.  This makes it clear for when you are entering the levels into the spreadsheet. 

ii) The start of the section is 0 distance and then you measure from 0 along the line to get the distance for the change in elevation.  The distances for the changes for the existing may well be different to the proposed. 

You can use a ruler to measure the distance in cm/mm/inches and then multiple by x to scale it.  Alternatively, you can use a scale ruler with a compatible level which means you don’t need to complete that sum – really handy! 

TOP TIPS : 1. Use a fine line pen or mechanical pencil so that your figures for each level change remain legible in the small space that you have available to you.

2. Use a roller ruler to draw parallel lines at set distances

3. Use a scale ruler, with a compatible scale

INPUTTING DATA INTO THE SPREADSHEET

1. Open the spreadsheet and select the “Section Method Example” tab so that you have the sheet on screen.

You will notice that this sheet is protected and cannot be amended. Before starting your own sheet, we recommend you view this example to understand what your finished cross section results should look like. 

2. Open the second tab called, “Section Method”.   This one you can edit and enter your data into. (Please refer to the licence tab for modifying and redistributing terms.)  

5. Read your Results and make your estimations.   Once all data has been entered, you will have the Cut, Fill Volumes calculated in each section and the Total volumes net volume for the entire grids. 

You can now use this data to estimate the cost of your project.

1. Check every section for data entry mistakes

2. The graphs will highlight an major anomalies

USING KUBLA CUBED TO ESTIMATE EARTHWORKS CUT & FILL

Although the Grid Method spreadsheet does speed up the calculation process, the possibility of human error when inputting the data remains. The alternative to using the spreadsheet is to allow TIN prism software to complete the calculations for you.   

Kubla Cubed calculates cut and fill volumes using TIN (Triangular Irregular Networks), which will calculate to a higher degree of accuracy than the 2D grid or cross section methods, and in less time.

Click below to find out more and download a free version of Kubla Cubed (Lite)   

FURTHER RESOURCES

• Check out more articles in our Library .
• Download the free Kubla Cubed Lite to try out using software to calculate cut and fill volumes. 
• Buy a Kubla Cubed Professional licence.
• Visit the  wikipedia page which compares the different software for earthworks estimates.

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AP®︎/College Calculus AB

Course: ap®︎/college calculus ab   >   unit 8, volume with cross sections: intro.

• Volumes with cross sections: squares and rectangles (intro)
• Volume with cross sections: squares and rectangles (no graph)
• Volume with cross sections perpendicular to y-axis
• Volumes with cross sections: squares and rectangles

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Modeling and Designing of Rogowski Coil for High-Frequency Sinusoidal Signal Measurement with Finite Element and Experimental Method

• Research Article-Electrical Engineering
• Published: 14 August 2024

Cite this article

• Priti Bawankule 1 &
• Kandasamy Chandrasekaran   ORCID: orcid.org/0000-0001-5904-1753 1  

Rogowski-coil (RC) current sensors are preferred for their ability to measure a broad range of currents, maintain linearity, and avoid magnetic saturation. This paper describes the design process and testing of RC for high frequency signal. The focus of the paper is on designing of the RC, which is modelled in 3D using Ansys Maxwell and experimental setup for testing purpose. Finite element method (FEM) is adapted to measure parameters of RC and the same is verified with experiment. A rectangular cross section model of RC is developed with nonmagnetic core. The electrical parameters of the coil are theoretically calculated using a lumped model, also frequency response of RC for different terminal resistance is analysed. The performance of the RC is experimentally tested for currents of 25 mA and 60 mA peak at a frequency of 100 kHz. The electrical parameters and output of the RC are examined and analysed through theoretical, FEM, and experimental method. The effect of terminal resistance on output of RC is analysed. The measured RC output voltage is compared and validated with FEM simulation result for peak current 25 mA and 60 mA at a 100 kHz frequency. Further, the performance of RC is analysed using the FEM method for 25 mA and 60 mA(peak) sinusoidal current at different frequency such as 100 kHz, 500 kHz and 1 MHz with terminal resistance of 1Ω and 10Ω and its results are reported. The sensitivity of prototype RC model is also analysed and its result is closely matched well with FEM simulation results.

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Bawankule, P., Chandrasekaran, K. Modeling and Designing of Rogowski Coil for High-Frequency Sinusoidal Signal Measurement with Finite Element and Experimental Method. Arab J Sci Eng (2024). https://doi.org/10.1007/s13369-024-09410-x

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Received : 10 January 2024

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DOI : https://doi.org/10.1007/s13369-024-09410-x

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