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Cross-Sectional Study | Definition, Uses & Examples

Published on May 8, 2020 by Lauren Thomas . Revised on June 22, 2023.

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 analyze 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, other interesting articles, 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 that are connected by a common trait.

Cross-sectional vs longitudinal studies

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 was 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 summarizes 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 organizations 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 analyze 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 analyze behavior over a period of time or establish long-term trends.
  • The timing of the cross-sectional snapshot may be unrepresentative of behavior 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.

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

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

Research bias

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

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.

Longitudinal study Cross-sectional study
observations Observations at a in time
Observes the multiple times Observes (a “cross-section”) in the population
Follows in participants over time Provides of society at a given point

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.

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 .

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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.
Cross-Sectional StudyPurposeExample
To describe the characteristics of a population.Examining the dietary habits of high school students.
To investigate associations between variables.Studying the correlation between smoking and lung disease in adults.
To gather information on a population or a subset.Conducting a survey on the use of public transportation in a city.
To determine the proportion of a population with a specific characteristic, condition, or disease.Assessing the prevalence of obesity in a country.
To examine the effects of certain occupational or environmental exposures.Studying the impact of air pollution on respiratory health in industrial workers.
To generate hypotheses for future research.Investigating relationships between various lifestyle factors and mental health conditions.

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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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

OutcomeExposed

(Body Mass Index ≥ 30)
Unexposed

(Body Mass Index < 30)
Total


(Low Activity Level)
75
250
325
(a + b)


(Moderate to High Activity Level)
25
200
225
(c + d)

100
(a + c)
450
(b + d)
550
(a + b + c + d)
  • 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 | 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.

Cross-sectional vs longitudinal studies

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.

Longitudinal study Cross-sectional study
observations Observations at a in time
Observes the multiple times Observes (a ‘cross-section’) in the population
Follows in participants over time Provides of society at a given point

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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what is cross sectional study design in research methodology

Cross-Sectional Study in Research

what is cross sectional study design in research methodology

Introduction

What is a cross-sectional study in research, what is the difference between cross-sectional and longitudinal research, cross-sectional study examples, types of cross-sectional studies, benefits of cross-sectional studies, challenges of cross-sectional studies.

Cross-sectional studies are a fundamental research method used across various fields to analyze data at a specific point in time. By comparing different subjects without considering the time variable, these studies can provide valuable insights into the prevalence and characteristics of phenomena within a population.

This article explores the concept of cross-sectional research, outlining its key features, applications, and how it differs from longitudinal studies. We will also examine examples of cross-sectional data, discuss the various types of cross-sectional studies, and highlight both the advantages and challenges associated with this research method. Understanding when and how to employ research methods for a cross-sectional study design is crucial for researchers aiming to draw accurate and meaningful conclusions from their data .

what is cross sectional study design in research methodology

A cross-sectional study is a type of observational research design that analyzes data from a population, or a representative subset, at one specific point in time. Unlike longitudinal studies that observe the same subjects over a period of time to detect changes, cross-sectional studies focus on finding relationships and prevalences within a predefined snapshot. This method is particularly useful for understanding the current status of a phenomenon or to identify associations between variables without inferring causal relationships.

In practice, cross-sectional studies collect data across a wide range of subjects at a single moment, aiming to capture a comprehensive picture of a particular research question. Researchers might analyze various factors, including demographic information, behaviors, conditions, or outcomes, to discern patterns or correlations within the population studied.

Though these studies cannot determine cause and effect, they are invaluable for generating hypotheses or propositions, informing policy decisions, and guiding future research. Their descriptive nature and relative ease of execution make cross-sectional studies a common starting point in many research endeavors, providing a foundational understanding of the context and variables of interest.

The primary distinction between cross-sectional and longitudinal research lies in how and when the data is collected. Cross-sectional studies differ in that they capture data at a single point in time, offering a snapshot that helps to identify the prevalence and relationships between variables within a specific moment that further research might be able to explore. In contrast, a longitudinal study involves collecting data from the same subjects repeatedly over an extended period of time, enabling the observation of changes and developments in the variables of interest.

While cross-sectional studies are efficient for gathering data at one point in time and are less costly and time-consuming than longitudinal studies, they fall short in tracking changes over time or establishing cause-and-effect relationships. On the other hand, longitudinal studies excel in observing how variables evolve, providing insights into dynamics and causal pathways. However, longitudinal data collection requires more resources, time, and a rigorous design to manage participant attrition and ensure consistent data collection over the study period.

Another key difference is in the potential for cohort effects. A cross-sectional analysis might conflate age-related changes with generational effects because different age groups are compared at one particular point in time. Longitudinal research, by observing the same individuals over time, can differentiate between aging effects and cohort effects, offering a clearer view of how specific and multiple variables change throughout an individual's life or over time.

what is cross sectional study design in research methodology

Cross-sectional studies are employed across various disciplines to investigate multiple phenomena at a specific point in time. These studies offer insights into the prevalence, distribution, and potential associations between variables within a defined population.

Below are three examples from different fields illustrating how cross-sectional research is applied to glean valuable findings.

Healthcare: Prevalence of a medical condition

In medical research, cross-sectional studies are frequently used to determine the prevalence of diseases or health outcomes in a population. For instance, a study might collect cross-sectional data from a diverse sample of individuals to assess the current prevalence of diabetes. By analyzing factors such as age, lifestyle, and comorbidities, researchers can identify patterns and risk factors associated with the disease, aiding in public health planning and intervention strategies.

Education: Analyzing student performance

Educational researchers often use a cross-sectional design to evaluate student performance across different grades or age groups at a single point in time. Such a study could compare test scores to analyze trends and disparities in educational achievement. By examining variables like socio-economic status, teaching methods, and school resources, educators and policymakers can identify areas needing improvement or intervention.

Economics: Employment trends analysis

In economics, a cross-sectional survey can provide snapshots of employment trends within a specific region or sector. An example might involve analyzing the employment rates, job types, and economic sectors in a country at a given time. This data can reveal insights into the economic health, workforce distribution, and potential areas for economic development or policy focus, informing stakeholders and guiding decision-making processes.

what is cross sectional study design in research methodology

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Cross-sectional studies can be categorized into different types based on their objectives and methodologies . These variations allow researchers to adapt the cross-sectional approach to suit specific research questions and contexts.

By understanding the different types of cross-sectional studies, researchers can select the most appropriate design to obtain reliable and relevant data. Below are four common types of cross-sectional studies, each with its unique focus and application.

Descriptive cross-sectional studies

Descriptive cross-sectional studies aim to provide a detailed snapshot of a population or phenomenon at a particular point in time. These studies focus on 'what exists' or 'what is prevalent' without delving into relationships between variables or concepts.

For example, a descriptive research study might catalog various health behaviors within a specific demographic group to inform public health initiatives. The primary goal is to describe characteristics, frequencies, or distributions as they exist in the study population.

Analytical cross-sectional studies

Unlike descriptive studies that focus on prevalence and distribution, analytical cross-sectional studies aim to uncover potential associations between variables. These studies often compare different groups within the population to identify factors that may correlate with certain outcomes.

For instance, an analytical cross-sectional study might investigate the relationship between lifestyle choices and blood pressure levels across various age groups. While these studies can suggest associations, they do not establish cause and effect.

Exploratory cross-sectional studies

Exploratory cross-sectional studies are conducted to explore potential relationships or hypotheses when little is known about a subject. These studies are particularly useful in emerging fields or for new phenomena. By examining available data, they can generate hypotheses for further research without committing extensive resources to long-term studies.

An example might be exploring the usage patterns of a new technology within a population to identify trends and areas for in-depth study.

Explanatory cross-sectional studies

Explanatory cross-sectional studies go beyond identifying associations; they aim to explain why certain patterns or relationships are observed. These studies often incorporate theoretical frameworks or models to analyze the data within a broader context, providing deeper insights into the underlying mechanisms or factors.

For example, an explanatory cross-sectional study could investigate why certain educational strategies are associated with better student outcomes, integrating theories of learning and cognition.

what is cross sectional study design in research methodology

Cross-sectional studies are a crucial tool in the repertoire of research methodologies , offering unique advantages that make them particularly suitable for various research contexts. These studies are instrumental in providing a snapshot of a specific point in time, which can be invaluable for understanding the status quo and informing future research directions. Below, we explore three significant benefits of employing cross-sectional studies in research endeavors.

Cost-effectiveness

One of the primary benefits of cross-sectional studies is their cost-effectiveness compared to longitudinal studies . Since they are conducted at a single point in time and do not require follow-ups, the financial resources, time, and logistical efforts needed are considerably lower. This efficiency makes cross-sectional studies an appealing option for researchers with limited budgets or those seeking preliminary data before committing to more extensive research.

Cross-sectional studies are inherently timely, providing quick snapshots that are especially valuable in fast-paced research areas where timely data is crucial. They allow researchers to collect and analyze data relatively quickly, offering insights that are current and relevant. This timeliness is particularly beneficial for informing immediate policy decisions or for studies in fields where trends may change rapidly, such as technology or public health.

Versatility

The versatility of cross-sectional studies is evident in their wide applicability across various fields and purposes. They can be designed to explore numerous variables and their interrelations within different populations and settings. This flexibility enables researchers to tailor studies to specific research questions, making cross-sectional studies a versatile tool for exploratory research, hypothesis generation , or situational analysis across disciplines.

Despite their utility in various fields of research, cross-sectional studies face distinct challenges that can affect the validity and applicability of their findings. Understanding these limitations is crucial for researchers to design robust studies and for readers to interpret results appropriately. Here are three key challenges commonly associated with cross-sectional studies.

Causality determination

One of the inherent limitations of cross-sectional studies is their inability to establish causality. Since data is collected at a single point in time, it is challenging to ascertain whether a relationship between two variables is causal or merely correlational. This limitation necessitates cautious interpretation of results, as establishing temporal precedence is essential for causal inference, which cross-sectional designs cannot provide.

Selection bias

Selection bias can occur in cross-sectional studies if the sample is not representative of the population from which it was drawn. This can happen due to non-random sampling methods or non-response, leading to skewed results that do not accurately reflect the broader population. Such bias can compromise the generalizability of the study's findings, making it critical to employ rigorous sampling methods and consider potential biases during analysis.

Cross-sectional confounding

Cross-sectional studies can also be susceptible to confounding, where an external variable influences both the independent and dependent variables , creating a spurious association. Without longitudinal data , it is difficult to control for or identify these confounding factors, which can lead to erroneous conclusions. Researchers must carefully consider potential confounders and employ statistical methods to adjust for these variables where possible.

what is cross sectional study design in research methodology

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what is cross sectional study design in research methodology

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

what is cross sectional study design in research methodology

Repeated Cross-Sectional Data Analysis

what is cross sectional study design in research methodology

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.  
Frequency How common is the outcome (disease, risk factor, etc.)? This is of the common mental disorders among Indigenous people living in regional, remote and metropolitan Australia.
Aetiology What risk factors are associated with these outcomes? This identifies the characteristics of women calling the perinatal anxiety & depression Australia (PANDA) national helpline.
Diagnosis Does the new test perform as well as the ‘gold standard’? This investigates the accuracy of a Client Satisfaction Questionnaire in relation to client satisfaction in mental health service support.

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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How Do Cross-Sectional Studies Work?

Gathering Data From a Single Point in Time

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

what is cross sectional study design in research methodology

Steven Gans, MD is board-certified in psychiatry and is an active supervisor, teacher, and mentor at Massachusetts General Hospital.

what is cross sectional study design in research methodology

  • 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."

Cross-Sectional Research Design

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This chapter addresses the peculiarities, characteristics, and major fallacies of cross-sectional research designs. The major advantage of cross-sectional research lies in cross-case analysis. A cross-sectional study aims at describing generalized relationships between distinct elements and conditions. The specific case and its particularities are not the focus, but all instances and cases. So cross-sectional studies try to establish general models that link a combination of elements with other elements under certain conditions. The results are tested (or rejected) theories about these relationships. Also, researchers find relevant information on how to write a cross-sectional research design paper and learn about typical methodologies used for this research design. The chapter closes with referring to overlapping and adjacent research designs.

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Maxwell, S. E., & Cole, D. A. (2007). Bias in cross-sectional analyses of longitudinal mediation. Psychological Methods, 12 , 23–44.

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An introduction to different types of study design

Posted on 6th April 2021 by Hadi Abbas

""

Study designs are the set of methods and procedures used to collect and analyze data in a study.

Broadly speaking, there are 2 types of study designs: descriptive studies and analytical studies.

Descriptive studies

  • Describes specific characteristics in a population of interest
  • The most common forms are case reports and case series
  • In a case report, we discuss our experience with the patient’s symptoms, signs, diagnosis, and treatment
  • In a case series, several patients with similar experiences are grouped.

Analytical Studies

Analytical studies are of 2 types: observational and experimental.

Observational studies are studies that we conduct without any intervention or experiment. In those studies, we purely observe the outcomes.  On the other hand, in experimental studies, we conduct experiments and interventions.

Observational studies

Observational studies include many subtypes. Below, I will discuss the most common designs.

Cross-sectional study:

  • This design is transverse where we take a specific sample at a specific time without any follow-up
  • It allows us to calculate the frequency of disease ( p revalence ) or the frequency of a risk factor
  • This design is easy to conduct
  • For example – if we want to know the prevalence of migraine in a population, we can conduct a cross-sectional study whereby we take a sample from the population and calculate the number of patients with migraine headaches.

Cohort study:

  • We conduct this study by comparing two samples from the population: one sample with a risk factor while the other lacks this risk factor
  • It shows us the risk of developing the disease in individuals with the risk factor compared to those without the risk factor ( RR = relative risk )
  • Prospective : we follow the individuals in the future to know who will develop the disease
  • Retrospective : we look to the past to know who developed the disease (e.g. using medical records)
  • This design is the strongest among the observational studies
  • For example – to find out the relative risk of developing chronic obstructive pulmonary disease (COPD) among smokers, we take a sample including smokers and non-smokers. Then, we calculate the number of individuals with COPD among both.

Case-Control Study:

  • We conduct this study by comparing 2 groups: one group with the disease (cases) and another group without the disease (controls)
  • This design is always retrospective
  •  We aim to find out the odds of having a risk factor or an exposure if an individual has a specific disease (Odds ratio)
  •  Relatively easy to conduct
  • For example – we want to study the odds of being a smoker among hypertensive patients compared to normotensive ones. To do so, we choose a group of patients diagnosed with hypertension and another group that serves as the control (normal blood pressure). Then we study their smoking history to find out if there is a correlation.

Experimental Studies

  • Also known as interventional studies
  • Can involve animals and humans
  • Pre-clinical trials involve animals
  • Clinical trials are experimental studies involving humans
  • In clinical trials, we study the effect of an intervention compared to another intervention or placebo. As an example, I have listed the four phases of a drug trial:

I:  We aim to assess the safety of the drug ( is it safe ? )

II: We aim to assess the efficacy of the drug ( does it work ? )

III: We want to know if this drug is better than the old treatment ( is it better ? )

IV: We follow-up to detect long-term side effects ( can it stay in the market ? )

  • In randomized controlled trials, one group of participants receives the control, while the other receives the tested drug/intervention. Those studies are the best way to evaluate the efficacy of a treatment.

Finally, the figure below will help you with your understanding of different types of study designs.

A visual diagram describing the following. Two types of epidemiological studies are descriptive and analytical. Types of descriptive studies are case reports, case series, descriptive surveys. Types of analytical studies are observational or experimental. Observational studies can be cross-sectional, case-control or cohort studies. Types of experimental studies can be lab trials or field trials.

References (pdf)

You may also be interested in the following blogs for further reading:

An introduction to randomized controlled trials

Case-control and cohort studies: a brief overview

Cohort studies: prospective and retrospective designs

Prevalence vs Incidence: what is the difference?

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you are amazing one!! if I get you I’m working with you! I’m student from Ethiopian higher education. health sciences student

' src=

Very informative and easy understandable

' src=

You are my kind of doctor. Do not lose sight of your objective.

' src=

Wow very erll explained and easy to understand

' src=

I’m Khamisu Habibu community health officer student from Abubakar Tafawa Balewa university teaching hospital Bauchi, Nigeria, I really appreciate your write up and you have make it clear for the learner. thank you

' src=

well understood,thank you so much

' src=

Well understood…thanks

' src=

Simply explained. Thank You.

' src=

Thanks a lot for this nice informative article which help me to understand different study designs that I felt difficult before

' src=

That’s lovely to hear, Mona, thank you for letting the author know how useful this was. If there are any other particular topics you think would be useful to you, and are not already on the website, please do let us know.

' src=

it is very informative and useful.

thank you statistician

Fabulous to hear, thank you John.

' src=

Thanks for this information

Thanks so much for this information….I have clearly known the types of study design Thanks

That’s so good to hear, Mirembe, thank you for letting the author know.

' src=

Very helpful article!! U have simplified everything for easy understanding

' src=

I’m a health science major currently taking statistics for health care workers…this is a challenging class…thanks for the simified feedback.

That’s good to hear this has helped you. Hopefully you will find some of the other blogs useful too. If you see any topics that are missing from the website, please do let us know!

' src=

Hello. I liked your presentation, the fact that you ranked them clearly is very helpful to understand for people like me who is a novelist researcher. However, I was expecting to read much more about the Experimental studies. So please direct me if you already have or will one day. Thank you

Dear Ay. My sincere apologies for not responding to your comment sooner. You may find it useful to filter the blogs by the topic of ‘Study design and research methods’ – here is a link to that filter: https://s4be.cochrane.org/blog/topic/study-design/ This will cover more detail about experimental studies. Or have a look on our library page for further resources there – you’ll find that on the ‘Resources’ drop down from the home page.

However, if there are specific things you feel you would like to learn about experimental studies, that are missing from the website, it would be great if you could let me know too. Thank you, and best of luck. Emma

' src=

Great job Mr Hadi. I advise you to prepare and study for the Australian Medical Board Exams as soon as you finish your undergrad study in Lebanon. Good luck and hope we can meet sometime in the future. Regards ;)

' src=

You have give a good explaination of what am looking for. However, references am not sure of where to get them from.

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what is cross sectional study design in research methodology

A well-designed cohort study can provide powerful results. This blog introduces prospective and retrospective cohort studies, discussing the advantages, disadvantages and use of these type of study designs.

Educational resources and simple solutions for your research journey

What is a Cross-Sectional Study? Definition, Advantages, Disadvantages, and Examples

What is a Cross-Sectional Study? Definition, Advantages, Disadvantages, and Examples

Table of Contents

What is a cross-sectional study ?

So, “what is a cross-sectional study?” Here is a simple cross-sectional study definition: A cross-sectional study is an observational study design that examines data on various variables gathered at a single time point within a sample population or predefined subgroup, offering a depiction of the population’s characteristics.

It is a time-saving, cost-effective, and straightforward approach for gathering preliminary data, wherein a researcher collects data at a single point in time (there is no prospective or retrospective follow-up) and observes variables without influencing them. The prevalence of an outcome at a given point in time can be determined in this manner.

What is the purpose of a cross-sectional study?

The purpose of a cross-sectional study is basically to take a “slice” or a “snapshot” of a population. In the fields of epidemiology and public health research, cross-sectional studies are used to evaluate associations, e.g., between exposure and disease, and to compare disease and symptom rates between an exposed group and an unexposed group.

Another purpose of a cross-sectional study is to simultaneously describe multiple characteristics. For instance, it can be employed to explore whether factors like excessive screen time, social media use, and resulting social pressures are linked to specific outcomes such as anxiety.

While such studies cannot establish a causal link and do not quantify a variable, they can highlight a relationship that might be worth further investigation. One of the advantages of a cross-sectional study is that it plays a key role in developing hypotheses and in laying the foundation for a more comprehensive research project.

Characteristics of a cross-sectional study

Now, let’s delve into the key characteristics of a cross-sectional study:

  • Cross-sectional studies examine a fixed set of variables within a specific timeframe. Researchers use the same measuring tools and data points throughout their investigation.
  • Although different cross-sectional studies may focus on the same variable of interest, they do so by observing distinct groups of subjects; each study captures a fresh set of participants.
  • Cross-sectional analyses focus on a single point in time, marked by a clear starting and stopping point.
  • In a cross-sectional study, researchers can zero in on a single independent variable, while also accommodating one or more dependent variables in their examination.

These studies can map the prevailing variables that coexist at a specific point in time. For instance, cross-sectional data can reveal the buying preferences of a population at a given time and how they correlate with economic trends.

Cross-sectional study examples

From the following cross-sectional study examples, we see that these studies gather data from participants sharing similarity across most variables, except for the variable(s) under scrutiny.

Some fictional cross-sectional study examples across various fields are as follows:

Agriculture : Examining pesticide use and knowledge of smallholder farmers in a specific region.

Nutrition : Fruit and nut consumption in a region according to gender and educational level.

Psychology : Psychological impact of the COVID-19 pandemic on healthcare workers in a region.

Economics : Economic burden of unemployment during armed conflict in a particular region.

Psychology : Psychological status of male prisoners at a particular facility.

Healthcare and medicine : (i) Population-based surveys, e.g., the prevalence of twin births in a village, or (ii) prevalence in clinical studies, e.g., antibiotic resistance in Clostridium difficile isolates in a tertiary care hospital.

Cross-sectional studies equip scholars and policymakers with actionable data that can be acquired quickly, facilitating informed decision-making and the development of products or services.

T ypes of cross-sectional studies

The main types of cross-sectional studies are d

escriptive, analytical, and repeated/serial.

Descriptive cross-sectional studies: These characterize the prevalence of one or more outcomes in a particular population, e.g., examining the prevalence of Alzheimer’s disease in a target population.

Analytical cross-sectional studies: Data are obtained for both exposure and outcome at a specific point in time to compare the outcome differences between exposed and unexposed subjects. Such studies answer how or why a certain outcome might occur, e.g., looking at vascular disease, traumatic brain injury, and family history to explain why some adults are much more likely to get Alzheimer’s disease than others.

Repeated (or serial) cross-sectional studies: Data are obtained from the same target population at different time points. At each time point, researchers select a different sample (different subjects) from the same target population. Repeated cross-sectional studies can therefore examine changes in a population over time. An example of serial cross-sectional study could be one that investigates the prevalence and risk factors of Alzheimer’s disease in adults aged 50-80 years in a specific decade.

Advantages and disadvantages of cross-sectional studies

Let’s look at the pros and cons of cross-sectional studies.

Advantages of a cross-sectional study

  • Relatively quick and inexpensive to conduct
  • No potential ethical issues
  • Multiple outcomes and exposures can be studied
  • Helpful for generating hypotheses
  • Many findings can be used to create an in-depth research study
  • Data are obtained from a large pool of subjects, and differences between groups can be compared.

Disadvantages of a cross-sectional study

  • Cannot measure incidence
  • Deriving causal inferences is challenging as it is a one-time measurement of the apparent cause and effect
  • Associations identified might be difficult to interpret
  • Cannot determine temporal relations between outcomes and risk factors
  • Not suitable for studying rare diseases or sporadic events
  • Susceptible to biases
  • Cannot be used to analyze trends over a period of time.

Limitations of cross-sectional studies

It is important to know the limitations of cross-sectional studies. Here are some important limitations:

  • A cross-sectional study is a one-time measurement of exposure and outcome. Therefore, it does not determine cause-and-effect relationships.
  • Such studies are prone to certain biases: report bias (because surveys and questionnaires might not result in accurate reporting) and sampling bias (owing to the need to select a sample of subjects from a large and heterogeneous study population).
  • Researchers need to be extremely careful about interpreting the associations and direction of associations from cross-sectional studies.
  • Cross-sectional surveys may not be sufficient to understand disease trends. In clinical studies, the prevalence of an outcome depends on disease incidence and length of survival following the outcome.
  • One of the disadvantages of a cross-sectional study is that it does not provide information from before or after the data were obtained.
  • Cross-sectional studies cannot be used to analyze behavior or trends over time.

Cross-sectional vs. longitudinal studies

It is critical to understand the key features of cross-sectional vs. longitudinal studies before you choose the study design to answer your research question. While both cross-sectional and longitudinal studies are observational, not requiring manipulation of the study environment, they differ in a number of ways (Table).

Table: Cross-sectional vs. longitudinal studies

Data of a characteristic of a population are collected at one point in time. Data from a population are collected at multiple time points over an extended period.
There are different individuals at each time point. The same individuals are followed over time.
It is less time- and resource-intensive. It requires more time and resources. Results can be affected by participants quitting the study before the data have been fully collected.
It cannot determine causality but is a good starting point to establish associations between variables. It is used for studying cause and effect.
It provides a “slice” of the population at a particular moment.

 

It tracks changes over time. Variables can evolve over a prolonged period. Therefore, researchers can use longitudinal data to detect changes in a population and establish patterns.
Example: A cross-sectional study collecting data from a group of children of various ages to see if there is any association between the amount of time they spend on screens (e.g., watching TV, using smartphones or computers) and their grades in school.

Data analysis shows a negative correlation between high screen time and lower grades in middle-school-aged children, but this correlation does not hold as strongly for high-school-aged children.

 

Example: Based on these findings from the cross-sectional study, you decide to design a longitudinal study to explore this relationship in more detail, focusing on middle-school-aged children. Without initially conducting the cross-sectional study, you would not have decided to focus on this age group, and you might not have chosen to investigate it further.

 

Frequently asked questions

A cross-sectional study is a type of observational research design that involves collecting data from a group of participants at a single point in time to assess various characteristics or variables of interest.

The primary goal of a cross-sectional study is to describe the prevalence of a specific condition or characteristic within a defined population at a particular moment in time.

What are some advantages of cross-sectional studies?

Cross-sectional studies are relatively quick and cost-effective. They are useful for generating hypotheses and identifying potential research directions.

What are the limitations of a cross-sectional study?

Cross-sectional studies do not allow researchers to track changes over time, making them unsuitable for studying temporal relationships. They cannot establish cause-and-effect relationships.

Can cross-sectional studies be used to study rare conditions or events?

Cross-sectional studies are not the best choice for studying rare events because of the need for a sufficiently large sample size to obtain meaningful results.

What are some suitable cross-sectional study examples?

Some potential cross-sectional study examples could be determining (i) the prevalence of obesity in teenagers from high-income families; (ii) the prevalence of accelerated skin aging, and the association between skin wrinkles and sunscreen application in women; or (iii) the prevalence and risk factors and geographic of reduced visual acuity in secondary students in a specific decade.

Setia, M. S. Methodology Series Module 3: Cross-sectional studies. Indian J Dermatol . (2016) 61(3): 261–264. doi: 10.4103/0019-5154.182410

Wang, X., & Cheng, Z. Cross-sectional studies: strengths, weaknesses, and recommendations. Chest (2020) 158(1) Suppl , S65–S71. https://doi.org/10.1016/j.chest.2020.03.012

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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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What (Exactly) Is A Cross-Sectional Study?

A plain-language explanation & definition (with examples).

By: Derek Jansen (MBA) | June 2020

If you’ve just started out on your dissertation, thesis or research project and it’s your first time carrying out formal research, you’ve probably encountered the terms “cross-sectional study” and “cross-sectional research” and are wondering what exactly they mean. In this post, we’ll explain exactly :

  • What a cross-sectional study is (and what the alternative approach is)
  • What the main advantages of a cross-sectional study are
  • What the main disadvantages of a cross-sectional study are
  • Whether you should use a cross-sectional or longitudinal study for your research

What is a cross-sectional study or cross-sectional research?

What (exactly) is a cross-sectional study?

A cross-sectional study (also referred to as cross-sectional research) is simply a study in which data are collected at one point in time . In other words, data are collected on a snapshot basis, as opposed to collecting data at multiple points in time (for example, once a week, once a month, etc) and assessing how it changes over time.

Example: Cross-Sectional vs Longitudinal 

Here’s an example of what this looks like in practice:

Cross-sectional study: a study which assesses a group of people’s attitudes and feelings towards a newly elected president, directly after the election happened.

Longitudinal study: a study which assesses how people’s attitudes towards the president changed over a period of 3 years after the president is elected, assessing sentiment every 6 months.

As you can probably see, while both these studies are analysing the same topic (people’s sentiment towards the president), they each have a different focus. The cross-sectional study is interested in what people are feeling and thinking “ right now ”, whereas the longitudinal study is interested in not just what people are feeling and thinking, but how those thoughts and feelings change over time .

What are the advantages of a cross-sectional study?

There are many advantages to taking a cross-sectional approach, which makes it the more popular option for dissertations and theses. Some main advantages are:

  • Speed – given the nature of a cross-sectional study, you can complete your research relatively quickly, as information only needs to be gathered once.
  • Cost – because information only needs to be collected once, the cost is lower than a longitudinal approach.
  • Control – because the data are only collected at one point in time, you have a lot more control over the measurement process (i.e. you don’t need to worry about measurement instruments changing over a period of years).
  • Flexibility – using a cross-sectional approach, you can measure multiple factors at once. Your study can be descriptive (assessing the prevalence of something), analytical (assessing the relationship between two or more things) or both.

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what is cross sectional study design in research methodology

What are the disadvantages of a cross-sectional study?

While the cross-sectional approach to research has many advantages, it (naturally) has its limitations and disadvantages too. Some of the main disadvantages are:

  • Static – cross-sectional studies cannot establish any sequence of events, as they only assess data with a snapshot view.
  • Causality – because cross-sectional studies look at data at a single point in time (no sequence of events), it’s sometimes difficult to understand which way causality flows – for example, does A cause B, or does B cause A? Without knowing whether A or B came first, it’s not always easy to tell which causes which.
  • Sensitivity to timing – the exact time at which data are collected can have a large impact on the results, and therefore the findings of the study may not be representative.

One of the disadvantages of the cross-sectional approach is that it provides a static view, meaning that it's very sensitive to timing.

Should I use a cross-sectional study or longitudinal study design?

It depends… Your decision to use a cross-sectional or longitudinal approach needs to be informed by your overall research aims, objectives and research questions . As with most research design choices, the research aims will heavily influence your approach.

For example, if your research objective is to get a snapshot view of something, then a cross-sectional approach should work well for you. However, if your research aim is to understand how something has changed over time, a longitudinal approach might be more appropriate.

If you’re trying to make this decision for a dissertation or thesis, you also need to consider the practical limitations such as time and access to data. Chances are, you won’t have the luxury of conducting your research over a period of a few years, so you might be “forced” into a cross-sectional approach due to time restrictions.

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what is cross sectional study design in research methodology

  • > The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences
  • > Cross-Sectional Studies

what is cross sectional study design in research methodology

Book contents

  • The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences
  • Cambridge Handbooks in Psychology
  • Copyright page
  • Contributors
  • Part I From Idea to Reality: The Basics of Research
  • Part II The Building Blocks of a Study
  • Part III Data Collection
  • 13 Cross-Sectional Studies
  • 14 Quasi-Experimental Research
  • 15 Non-equivalent Control Group Pretest–Posttest Design in Social and Behavioral Research
  • 16 Experimental Methods
  • 17 Longitudinal Research: A World to Explore
  • 18 Online Research Methods
  • 19 Archival Data
  • 20 Qualitative Research Design
  • Part IV Statistical Approaches
  • Part V Tips for a Successful Research Career

13 - Cross-Sectional Studies

from Part III - Data Collection

Published online by Cambridge University Press:  25 May 2023

Cross-sectional studies are a type of observational studies in which the researcher commonly assesses the exposure, outcome, and other variables (such as confounding variables) at the same time. They are also referred to as “ prevalence studies. ” These studies are useful in a range of disciplines across the social and behavioral sciences. The common statistical estimates from these studies are correlation values, prevalence estimates, prevalence odds ratios, and prevalence ratios. These studies can be completed relatively quickly, are relatively inexpensive to conduct, and may be used to generate new hypotheses. However, the major limitation of these studies are biases due to sampling, length-time bias, same source bias, and the inability to have a clear temporal association between exposure and outcome in many scenarios. The researcher should be careful while interpreting the measure of association from these studies, as it may not be appropriate to make causal inferences from these associations.

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Further reading.

The following are sources that describe various aspects of cross-sectional studies.

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  • Cross-Sectional Studies
  • By Maninder Singh Setia
  • Edited by Austin Lee Nichols , Central European University, Vienna , John Edlund , Rochester Institute of Technology, New York
  • Book: The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences
  • Online publication: 25 May 2023
  • Chapter DOI: https://doi.org/10.1017/9781009010054.014

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  • What is a cross-sectional study?

Last updated

6 February 2023

Reviewed by

Miroslav Damyanov

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

Read on to learn about cross-sectional studies. We’ll explore examples, types, advantages, and limitations of cross-sectional studies, plus when you might use them.

Analyze cross-sectional studies

Dovetail streamlines cross-sectional studies to help you uncover and share actionable insights

A cross-sectional study is also known as a prevalence or transverse study. It’s a tool that allows researchers to collect data across a pre-defined subset or sample population at a single point in time. The information is typically about many individuals with multiple variables, such as gender and age. Although researchers get to analyze these variables, they do not manipulate them.

This study type is commonly used in clinical research, business-related studies, and population studies.

Once the researcher has selected the ideal study period and participant group, the study usually takes place as a survey or physical experiment.

  • Characteristics of cross-sectional studies

Primary characteristics of cross-sectional studies include the following:

Consistent variables : Researchers carry out a cross-sectional study over a specific period with the same set of variables (income, gender, age, etc.).

Observational nature : Researchers record findings about a specific population but do not alter variables—they just observe.

Well-defined extremes : The analysis includes defined start and stop points which allow all variables to stay the same.

Singular instances : Only one topic or instance can be analyzed with a cross-sectional study. This allows for more accurate data collection .

  • Examples of cross-sectional studies

Variables remain the same during a cross-sectional study. This makes it a useful research tool in various sectors and circumstances across multiple industries.

Here are some examples to give you better clarity:

Healthcare : Scientists might leverage cross-sectional research to assess how children aged 3–10 are prone to calcium deficiency.

Retail : Researchers use cross-sectional studies to identify similarities and differences in spending habits between men and women within a specific age group.

Education : These studies help reveal how students with a specific grade range perform when schools introduce a new curriculum.

Business: Researchers might leverage cross-sectional studies to understand how a geographic segment responds to offers and discounts.

  • Types of cross-sectional studies

We can categorize cross-sectional studies into two distinct types: descriptive and analytical research. However, the researcher may use one or both types to gather and analyze data.

Here is a description of the two to help you understand how they may apply to your work.

Descriptive research

A descriptive cross-sectional survey or study assesses how commonly or frequently the primary variable occurs within a select demographic. This enables you to identify any problem areas within the group.

Descriptive research makes trend identification easy, facilitating the development of products and services that fit a particular population.

Analytical research

An analytical cross-sectional study investigates the relationship between two related or unrelated parameters. Outside variables may affect the study while the investigation is ongoing, however.

Note that the original results and data are studied together simultaneously in an analytical cross-sectional study.

  • Cross-sectional versus longitudinal studies

Although longitudinal and cross-sectional studies are both observational, they are relatively different types of research design.

Below are the main differences between cross-sectional and  longitudinal studies :

Sample group

A cross-sectional study will include several variables and sample groups, meaning it will collect data for all the different sample groups at once. However, in longitudinal studies, the same groups with similar variables can be observed repeatedly.

Cross-sectional studies are usually cheaper to conduct than longitudinal studies, so they are ideal if you have a limited budget.

Participants in longitudinal studies have to commit for an extended period, which significantly increases costs. Cross-sectional studies, on the other hand, are shorter and require less effort.

Data is collected only once in cross-sectional research. In contrast, longitudinal research takes considerable time because data is collected across numerous periods (potentially decades).

Researchers don’t necessarily seek causation in longitudinal research. This means the data will lack context regarding previous participant behavior.

Longitudinal research, on the other hand, clearly shows how data evolves. This means you can infer cause-and-effect relationships.

  • How to perform a cross-sectional study

You will need to follow these steps to conduct a cross-sectional study:

Formulate research questions and hypotheses . You will also need to identify your target population at this stage.

Design the research . You will need to leverage observation rather than experiments when collecting data. However, you can always use non-experimental techniques such as questionnaires or surveys. As a result, this type of research will let you collect both quantitative and qualitative data .

Conduct the research . You can collect your data or assemble it from another source. In most instances, governments make cross-sectional datasets available to the public (through censuses) that can help with your research. The World Bank and World Health Organization also provide cross-sectional datasets on their websites.

Analyze the data . Data analysis will depend on the type of data collection method you use.

  • Advantages and disadvantages of cross-sectional studies

Are you considering whether a cross-sectional study is an ideal approach for your next research? It’s an efficient and effective way to gather data. Check out some of the key advantages and disadvantages of cross-sectional studies.

Advantages of cross-sectional research

Quick to conduct

Multiple outcomes are researched at once

Relatively inexpensive

Used as a basis for further research

Researchers gather all variables at a single point in time

It’s possible to measure the prevalence of all factors

Ideal for descriptive analysis

Disadvantages of cross-sectional research

Preventing other variables from influencing the study is challenging

Researchers cannot infer cause-and-effect relationships

Requires large, heterogeneous samples, which increases the chances of sampling bias

The select population and period may not be representative

  • When to use a cross-sectional design

Cross-sectional studies are useful when:

You need answers to questions regarding the prevalence and incidence of a situation, belief, or condition.

Establishing the norm in a particular demographic at a specified time. For instance, what is the average age for completing studies in Dallas?

Justifying the need to conduct further research on a specific topic. With cross-sectional research, you can infer a correlation without determining a direct cause. This makes it easier to justify conducting other investigations.

  • The bottom line

A cross-sectional study is essential when researching the prevailing characteristics in a given population at a single point in time. Cross-sectional studies are often used to analyze demography, financial reports, and election polls. You could also use them in medical research or when building a marketing strategy, for instance.

Are cross-sectional studies quantitative or qualitative?

Cross-sectional research can be both qualitative and quantitative.

Do cross-sectional studies have control groups?

Cross-sectional studies don’t need a control group as the selected population is not based on exposure.

What are the limitations of cross-sectional studies?

Limitations of cross-sectional studies include the inability to make causal inferences, study rare illnesses, and access incidence. Researchers select a subject sample from a large and heterogeneous population.

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Critical Appraisal Resources for Evidence-Based Nursing Practice

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What is an Analytical Cross-Sectional Study?

Pro tips: analytical cross-sectional study checklist, articles on cross-sectional study design and methodology.

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An analytical cross-sectional study is a type of quantitative, non-experimental research design. These studies seek to "gather data from a group of subjects at only one point in time" (Schmidt & Brown, 2019, p. 206).  The purpose is to measure the association between an exposure and a disease, condition or outcome within a defined population.  Cross-sectional studies often utilize surveys or questionnaires to gather data from participants (Schmidt & Brown, 2019, pp. 206-207).  

Schmidt N. A. & Brown J. M. (2019). Evidence-based practice for nurses: Appraisal and application of research  (4th ed.). Jones & Bartlett Learning. 

Each JBI Checklist provides tips and guidance on what to look for to answer each question.   These tips begin on page 4. 

Below are some additional  Frequently Asked Questions  about the Analytical Cross-Sectional Studies  Checklist  that have been asked students in previous semesters. 

Frequently Asked Question Response
A confounder or confounding factor/confounding variable is often referred to as a third variable that could potentially impact the study's results. Read a definition and description  . Confounding factors/variables or confounders may be listed in the study's limitations section or within the study's main results section. 
Check for   or regression analysis in the study's data analysis/statistical analysis section. Read a definition and description  . 

For more help:  Each JBI Checklist provides detailed guidance on what to look for to answer each question on the checklist.  These explanatory notes begin on page four of each Checklist. Please review these carefully as you conduct critical appraisal using JBI tools. 

Kesmodel U. S. (2018). Cross-sectional studies - what are they good for?   Acta Obstetricia et Gynecologica Scandinavica ,  97 (4), 388–393. https://doi.org/10.1111/aogs.13331

Pandis N. (2014). Cross-sectional studies .  American Journal of Orthodontics and Dentofacial Orthopedics ,  146 (1), 127–129. https://doi.org/10.1016/j.ajodo.2014.05.005

Savitz, D. A., & Wellenius, G. A. (2023). Can cross-sectional studies contribute to causal inference? It depends .  American Journal of Epidemiology ,  192 (4), 514–516. https://doi.org/10.1093/aje/kwac037

Wang, X., & Cheng, Z. (2020). Cross-sectional studies: Strengths, weaknesses, and recommendations .  Chest ,  158 (1S), S65–S71. https://doi.org/10.1016/j.chest.2020.03.012

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  • Protocol for the development of a reporting guideline for umbrella reviews on epidemiological associations using cross-sectional, case-control and cohort studies: the Preferred Reporting Items for Umbrella Reviews of Cross-sectional, Case-control and Coh…
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  • http://orcid.org/0000-0003-4877-7233 Marco Solmi 1 , 2 , 3 , 4 ,
  • Kelly Cobey 5 ,
  • http://orcid.org/0000-0003-2434-4206 David Moher 6 ,
  • http://orcid.org/0000-0003-3890-1683 Sanam Ebrahimzadeh 7 ,
  • http://orcid.org/0000-0001-9019-4125 Elena Dragioti 8 , 9 ,
  • Jae Il Shin 10 ,
  • Joaquim Radua 11 , 12 ,
  • http://orcid.org/0000-0001-5877-8075 Samuele Cortese 13 , 14 , 15 , 16 , 17 ,
  • Beverley Shea 18 ,
  • Nicola Veronese 19 ,
  • http://orcid.org/0000-0001-8341-3991 Lisa Hartling 20 ,
  • Michelle Pollock 21 ,
  • http://orcid.org/0000-0001-7462-5132 Matthias Egger 22 , 23 ,
  • http://orcid.org/0000-0002-9451-9094 Stefania Papatheodorou 24 , 25 ,
  • http://orcid.org/0000-0003-3118-6859 John P Ioannidis 26 ,
  • Andre F Carvalho 27
  • 1 Psychiatry , University of Ottawa , Ottawa , Ontario , Canada
  • 2 Department of Child and Adolescent Psychiatry , Charité Universitätsmedizin , Berlin , Germany
  • 3 Department of Mental Health , The Ottawa Hospital , Ottawa , Ontario , Canada
  • 4 Ottawa Hospital Research Institute (OHRI) , University of Ottawa , Ottawa , Ontario , Canada
  • 5 Open Science and Meta-Research Program , University of Ottawa Heart Institute , Ottawa , Ontario , Canada
  • 6 Ottawa Methods Centre , Ottawa Hospital Research Institute , Ottawa , Ontario , Canada
  • 7 Ottawa Hospital Research Institute , Ottawa , Ontario , Canada
  • 8 Pain and Rehabilitation Centre, and Department of Medical and Health Sciences , Linkopings universitet , Linkoping , Sweden
  • 9 Research Laboratory Psychology of Patients, Families, and Health Professionals, Department of Nursing, School of Health Sciences , University of Ioannina , Ioannina , Greece
  • 10 Yonsei University College of Medicine , Seoul , The Republic of Korea
  • 11 Institut d'Investigacions Biomediques August Pi i Sunyer , Barcelona , Spain
  • 12 Institut d'Investigacions Biomediques August Pi i Sunyer, CIBERSAM , University of Barcelona , Barcelona , Spain
  • 13 Centre for Innovation in Mental Health, School of Psychology, Faculty of Environmental and Life Sciences , University of Southampton , Southampton , UK
  • 14 Clinical and Experimental Sciences (CNS and Psychiatry), Faculty of Medicine , University of Southampton , Southampton , UK
  • 15 Solent NHS Trust , Southampton , UK
  • 16 Hassenfeld Children’s Hospital at NYU Langone , New York University Child Study Center , New York , NY , USA
  • 17 DiMePRe-J-Department of Precision and Rigenerative Medicine-Jonic Area , University of Bari "Aldo Moro" , Bari , Italy
  • 18 Ottawa Health Research Institute , Ottawa , Ontario , Canada
  • 19 University of Palermo , Palermo , Italy
  • 20 Pediatrics , University of Alberta , Edmonton , Alberta , Canada
  • 21 Institute of Health Economics , Edmonton , Alberta , Canada
  • 22 Institute of Social & Preventive Medicine , University of Bern , Bern , Switzerland
  • 23 Population Health Sciences, Bristol Medical School , University of Bristol , Bristol , UK
  • 24 Department of Epidemiology , Rutgers School of Public Health , Piscataway , NJ , USA
  • 25 Harvard TH Chan School of Public Health , Boston , Massachusetts , USA
  • 26 Stanford University , Stanford , California , USA
  • 27 IMPACT (Innovation in Mental and Physical Health and Clinical Treatment) Strategic Research Centre, School of Medicine, Barwon Health , Deakin University , Geelong , VIC, Australia
  • Correspondence to Dr Marco Solmi; marco.solmi83{at}gmail.com

Introduction Observational studies are fraught with several biases including reverse causation and residual confounding. Overview of reviews of observational studies (ie, umbrella reviews) synthesise systematic reviews with or without meta-analyses of cross-sectional, case-control and cohort studies, and may also aid in the grading of the credibility of reported associations. The number of published umbrella reviews has been increasing. Recently, a reporting guideline for overviews of reviews of healthcare interventions (Preferred Reporting Items for Overviews of Reviews (PRIOR)) was published, but the field lacks reporting guidelines for umbrella reviews of observational studies. Our aim is to develop a reporting guideline for umbrella reviews on cross-sectional, case-control and cohort studies assessing epidemiological associations.

Methods and analysis We will adhere to established guidance and prepare a PRIOR extension for systematic reviews of cross-sectional, case-control and cohort studies testing epidemiological associations between an exposure and an outcome, namely Preferred Reporting Items for Umbrella Reviews of Cross-sectional, Case-control and Cohort studies (PRIUR-CCC). Step 1 will be the project launch to identify stakeholders. Step 2 will be a literature review of available guidance to conduct umbrella reviews. Step 3 will be an online Delphi study sampling 100 participants among authors and editors of umbrella reviews. Step 4 will encompass the finalisation of PRIUR-CCC statement, including a checklist, a flow diagram, explanation and elaboration document. Deliverables will be (i) identifying stakeholders to involve according to relevant expertise and end-user groups, with an equity, diversity and inclusion lens; (ii) completing a narrative review of methodological guidance on how to conduct umbrella reviews, a narrative review of methodology and reporting in published umbrella reviews and preparing an initial PRIUR-CCC checklist for Delphi study round 1; (iii) preparing a PRIUR-CCC checklist with guidance after Delphi study; (iv) publishing and disseminating PRIUR-CCC statement.

Ethics and dissemination PRIUR-CCC has been approved by The Ottawa Health Science Network Research Ethics Board and has obtained consent (20220639-01H). Participants to step 3 will give informed consent. PRIUR-CCC steps will be published in a peer-reviewed journal and will guide reporting of umbrella reviews on epidemiological associations.

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This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bmjopen-2022-071136

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STRENGTHS AND LIMITATIONS OF THIS STUDY

This protocol for reporting guidance of umbrella reviews of epidemiological associations is needed to address specific reporting challenges of observational studies.

This protocol follows the guidance for reporting checklist, which is standard in the field.

This study is limited from attrition rates, which is typical of Delphi studies.

Introduction

There is evidence that the number of systematic reviews and meta-analyses in the literature has increased geometrically over the past two decades. 1 2 Due to the increasing number of systematic reviews and meta-analyses on a given topic over the years, the field of knowledge synthesis has developed systematic reviews of systematic reviews, also called ‘reviews of reviews’, ‘overviews of (systematic) reviews’, ‘meta-reviews’ or ‘umbrella reviews’. 3–8 Umbrella reviews can include interventional studies or observational studies. 9 Overviews of reviews and umbrella reviews ideally aim to provide a comprehensive and systematic synthesis following the steps of a systematic review (ie, literature search, methodological quality appraisal, quantitative analysis where feasible and appropriate, etc) with systematic reviews as the unit of analysis. 2 9 10 The field has seen a sharp increase in the number of published overviews of reviews and umbrella reviews over the past decade. For example, just limiting to umbrella reviews, approximately 56 were published in 2010, while 560 (10 times increase in yearly publications) were published in 2021 (PubMed (umbrella review)).

There is important variability between and within the approach of overviews of reviews and umbrella reviews, making results hardly comparable. 11–13 This heterogeneity is not surprising, considering the large heterogeneity in the conception and implementation of both systematic reviews and meta-analyses. 14 15 The methodology used to conduct systematic reviews and meta-analyses, as well as the quality of reporting, can affect conclusions and potentially lead to misleading interpretation of findings, misinforming policy makers, professional organisations and regulatory bodies, practitioners, patients, the public and other stakeholders. The quality and credibility of evidence synthesis efforts is also largely based on the quality of the credibility of their unit of inclusion (ie, individual studies). There is consensus that randomised controlled trials start from a higher credibility in the evidence-based medicine pyramid, and lower credibility is assigned to observational studies, which are more prone to bias. Interventional studies are not free from limitations, but in general experimental designs such as randomised controlled trials can protect from a number of biases, such as confounding by indication, or reverse causality. By contrast, observational evidence is prone to these and other biases, including excess of significance bias. 16 Among observational studies, different study designs are adapted depending on the research question to be answered. For instance, studies measuring prognostic accuracy or prediction models are typically cohort studies that need internal development of the model, internal and external validation, calibration and accuracy measures. 17 Cross-sectional studies can instead be used to investigate biomarkers or diagnostic accuracy of a given construct/test, or the prevalence of a disease. Other research questions, and typically epidemiological associations between two factors are generally explored with cohort, case-control and cross-sectional studies. 18 19 More specifically, among studies investigating epidemiological associations, cross-sectional studies are typically used to measure the association between two factors, neglecting the direction of such association, while case-control and cohort studies are frequently used to measure associations between a construct of interest, and putative risk factors, 1 14 20–23 or its outcomes, 24 25 with the exposure occurring before the outcome. Research questions for which umbrella reviews of observational studies can be used are reported in table 1 .

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Frequent research questions for which overviews of reviews of observational studies are used

Reporting guidelines, defined as ‘ checklist, flow diagram or explicit text to guide authors in reporting a specific type of research, developed using explicit methodology’, 26 can be useful to improve the transparency, quality and reporting of individual studies, reviews or umbrella reviews. Interventional and observational evidence pose different methodological quality and reporting challenges, which are reflected from different reporting guidance for the two study designs. For observational evidence, several checklists of essential information to be reported in a paper are available for individual studies, systematic reviews and meta-analyses. For observational studies, the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) Network has disseminated Standards for Reporting Diagnostic accuracy studies, 27 27 Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis 28 and Strengthening the Reporting of Observational Studies in Epidemiology. 29 For systematic reviews, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 (PRISMA 2020) 30 is a broad guide inclusive of a range of primary study designs, and more specific statements are available, that is, the PRISMA for Diagnostic Test Accuracy, 31 and PRISMA for reviews including harms outcomes, 32 or the guidance on conducting systematic reviews and meta-analyses of observational studies on aetiology 33 ( table 2 ). The different checklists addressing different study designs and research questions well reflect their different methodological challenges, from original research to evidence synthesis.

Key reporting guidelines across different research questions that can be addressed with observational studies

Regarding overview of reviews and umbrella reviews, virtually all reporting checklists proposed so far have focused on interventional evidence. The first proposed checklist 34 was based on A MeaSurement Tool to Assess systematic Reviews quality assessment tool (AMSTAR) 35 and Cochrane guidance. 36 The same year, one further checklist for overviews of reviews 37 merged evidence from PRISMA for abstracts, 38 the Preferred reporting items for overviews of systematic reviews (PRIO) checklist 39 and the Overview Quality Assessment Questionnaire. 40 Then, a checklist for overviews of reviews 41 was developed based on Cochrane recommendations, 36 and the older versions of PRISMA 42 (and not PRISMA 2020 30 ), and AMSTAR 35 43 (and not AMSTAR 2 44 ). Later, checklist was developed for systematic reviews of reviews including harms, called PRIO, 39 based on an older version of PRISMA, 42 PRISMA harms 32 and PRISMA for systematic review protocols. 45 The same group also published a checklist for abstract of overviews of reviews of healthcare interventions. 46 Recently, the Preferred Reporting Items for Systematic Reviews of Systematic Reviews/Meta-analyses (PRIOR) statement has been published to guide reporting of overviews of reviews of health interventions, 3 adhering to EQUATOR guidance. 26 In PRIOR protocol, 3 authors acknowledged that several relevant sources exist that provide guidance on overviews of reviews or umbrella reviews, but they did not adhere to guidance endorsed by the EQUATOR Network.

Regarding umbrella reviews (ie, observational evidence investigating epidemiological associations), to the best of our knowledge no EQUATOR-adherent guidance has been developed, registered with or disseminated by EQUATOR group, nor any specific checklist has been previously proposed. Given that PRIOR focuses on interventional evidence, and that different reporting guidelines are needed for observational evidence (cross-sectional, case-control, cohort studies) on epidemiological associations versus interventional evidence, the aim of this project is to develop evidence-based and agreement-based guideline PRIOR-extension for reporting umbrella reviews (ie, cross-sectional, case-control, cohort studies testing epidemiological associations), adhering to established guidance 26 and building on PRIOR statement. 3 Specifically, this project will yield a Preferred Reporting Items for Umbrella Reviews of Cross-sectional, Case-control and Cohort studies (PRIUR-CCC), which will be published in a peer-reviewed journal and available via a dedicated website.

Methods and analysis

Transparency statement.

We have also submitted this protocol to The Ottawa Health Science Network- Research Ethics Board and have obtained consent (20220639-01H). All participants to the Delphi survey will give written informed consent, which they will be able to withdraw at any time (yet anonymous responses cannot be withdrawn). This protocol is publicly available at medrxiv.org (MEDRXIV/2022/283572).

All study data and materials will be publicly available.

Study design

This study will follow EQUATOR guidance for developing reporting checklists, 26 and will be composed on four steps, namely project launch, literature review, Delphi survey and guideline statement preparation ( figure 1 ).

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Development of PRIUR-CCC statement flow diagram. EQUATOR, Enhancing the QUAlity and Transparency Of health Research.

Project launch

Project launch will consist of reaching an agreement on roles and responsibilities of core team members (eg, identifying stakeholders that will participate in the Delphi study). A core group of researchers have prepared the protocol of the project. These authors will be the core team of the project, and have extensive record of umbrella reviews on cross-sectional, case-control and cohort studies investigating epidemiological associations. The project’s day-to-day steps and its finalisation will be responsibility of shared first authors, and last author. All other authors will contribute to the protocol, literature review, initial set of checklist items, Delphi survey and final PRIUR-CCC statement. The identification and involvement of stakeholders will follow the Practical Guidance for Involving Stakeholders in Health Research from the Multi Stakeholder Engagement Consortium. 47 Reporting of involvement of patients and public will adhere to Guidance for Reporting Involvement of Patients and the Public 2 (GRIPP2). 48

Literature narrative review

We will review the systematic reviews conducted in the context of PRIOR development regarding available guidance to conduct umbrella reviews. Then, since two systematic reviews of umbrella reviews have already been conducted by the members of this project, 2 12 those two systematic reviews will be used to assess methodology and reporting of included umbrella reviews. Given that PRIUR-CCC will not focus on diagnostic test accuracy and prediction models, we will not cover those study designs as they are out of scope for PRIUR-CCC. After having reviewed PRIOR documentation and having reviewed the two previous systematic reviews of umbrella reviews, we will extract key methodological factors from identified umbrella reviews, and publish a review of methodology and reporting of previous umbrella reviews. The findings of this review will be used to identify PRIOR domains and items where changes are needed, and to produce an initial PRIOR checklist to start the Delphi study with.

Delphi survey

We will conduct a Delphi study. Delphi studies use social science survey techniques to structure communication between participants in order to drive consensus and make a collective decision. 49 Typically, Delphi studies use several rounds of surveys in which participants, vote on issues. Between rounds, results of voting are aggregated and anonymised. They are then presented back to participants along with their own individual scores, and feedback on why others voted as they did. 50 51 Among others, two strengths of this method include allowing for effective communication, limiting direct confrontation between individuals and giving participants the opportunity to consider the group’s thoughts and to compare and adjust their own score in the next round. Participants : we will aim to include 100 participants in Delphi study round 1, as done in PRIOR development. 3 Participants to the Delphi study will meet any of the following criteria: (a) they have publication track of umbrella reviews in various fields, or (b) have publication track in Delphi surveys or (c) have publication track of reporting checklists of cross-sectional, case-control and cohort studies, or editors of peer-reviewed journals that published umbrella reviews on observational evidence or that have interest in umbrella reviews, or (d) funders of research or meta-research, or (e) practitioners, (f) policy makers, (g) evidence synthesis associations (eg, Cochrane, Campbell collaboration, Joanna Briggs Institute, others). 3 Participants will be recruited via a two-step process. First, we have established a core group of participants based on criteria (a) or (b) or (c) described above (project launch), and past solid collaboration track record, that are authoring the present protocol. Second, additional stakeholders will be invited, according to (a) to (h) criteria above. Delphi study methods : this Delphi study will consist of three rounds. For the first two Delphi rounds, we will ask participants to complete an online survey which will be administered using Google Forms, structuring forms based on purpose-built platform for Delphi survey development and management. 52 The third round will consist of a facilitated online consensus group meeting (using Zoom 53 ).

Age, gender, geographical area ( https://www.who.int/countries ) and stakeholders group will be collected anonymously. Similar to what has been done for scoping reviews, 54 we will build on existing PRIOR statement, which parallels PRISMA 2020 30 statement, for consistency and continuity with existing established reporting guidance for evidence syntheses. All participants will be asked what items of PRIOR 3 will have to (i) remain unchanged, (ii) what will have to be changed and how, (iii) what will have to be removed (three answer options, one possible choice). In addition, participants will have the possibility to propose new items. As a starting point, the core team composed of authors of this protocol will provide a set of suggested items for participants to vote on. This starting set of items will be built based on experience in umbrella reviews. Participants will respond indicating their agreement/disagreement to have each item included in the PRISMA-CCC reporting guideline. Participants will be provided with a free-text box to fill in with additional comments to explain why they voted how they did or to propose wording amendments to the item, or new items. To decide to keep or remove a PRIOR item, or to add a new item, will require a minimum of 80% consensus among participants (based on findings from a systematic review of Delphi studies 55 ). Items where an 80% consensus has not been reached, as well as changes to PRIOR items and new items will be voted on again in round 2. We anticipate the survey will take about 20 min to complete and will provide participants with a 3-week window to take part, with reminders sent after 1 and 2 weeks, respectively. We will pilot test the survey among the authors of the present protocol.

All participants who completed round 1 of the Delphi will be re-invited to take part in round 2. Items which achieved consensus in round 1 will be shared with the participants. We will then ask participants to re-vote on any items that did not reach consensus or that were newly suggested. When re-voting on items that appeared in round 1, participants will also be provided with all comments provided by participants to justify their responses. Again, an 80% of consensus will be used to determine what items to include/exclude from the PRIUR-CCC guideline. If a new proposed item will be overlapping with existing items, Delphi moderator will add a comment to the new proposed item pointing to existing overlapping item.

The core group and a purposeful selection of participants will be invited to round 3. We will aim to ensure representation from each of our geographically diverse institutions, each of the stakeholder groups and demographic variables including gender. Based on previous Delphi surveys and feasibility, we will invite no more than 30 participants in total to ensure feasibility to round 3. 56 57 Round 3 will be moderated by core group members authoring the protocol, who will rotate every two items. We will present all participants with the results (ie, frequency of responses for each item, comments, changes and new items) of round 2 of the Delphi prior to the meeting and summarise these again briefly at the start of the consensus meeting. Participants will have the opportunity to discuss outstanding items one-by-one. They will then be asked to vote anonymously using real-time voting technology available via Zoom on each of these items. While diverse time zones will present challenges in other ways a virtual meeting may foster equity, diversity and inclusion of participants who might otherwise not have had funding or capacity to travel to an in-person event.

For completers and drop-outs, demographics and responses will be presented using descriptive statistics using SPSS28. We will identify which items have and have not reached consensus for inclusion or exclusion based on our definition of 80% agreement and report this information for each Delphi round. The list of items identified for inclusion in PRIUR-CCC will be collated after round 3 and we will report the outcomes of participants ranking of these items in a table.

The project has not started yet, and will be started in January 2024 and completed by December 2024.

Guidance statement

Co-first and last authors of this protocol will prepare the first draft of final PRIUR-CCC statement, that will then be approved after reiteration with other authors. This PRIUR-CCC statement will include the report of the whole project, the PRIUR-CCC checklist, the PRIUR-CCC flow diagram and an explanatory document that will inform on how to use PRIUR-CCC, with examples. PRIUR-CCC statement will be published in peer-reviewed journals, and on dedicated platforms. The Delphi process reporting will be informed by the Conducting and REporting of DElphi Studies checklist 58 ( table 3 ).

CREDES checklist for survey studies

Ethics and dissemination

PRIUR-CCC has been approved by The Ottawa Health Science Network-Research Ethics Board and have obtained consent (20220639-01H). Participants to step 3 will give informed consent. PRIUR-CCC steps will be published in a peer-reviewed journal and will guide reporting of umbrella reviews on epidemiological associations.

Patient and public involvement

Reporting of involvement of patients and public will adhere to GRIPP2. 48 All authors completing all three rounds of Delphi process and the core group will be invited to review and finally coauthor the publication.

This protocol for reporting guidance of umbrella reviews of epidemiological associations is needed to address specific reporting challenges of observational studies. PRIUR-CCC will provide a reporting framework to guide future umbrella reviews of observational studies assessing epidemiological associations, that can be used from researchers, reviewers, funders and editors to evaluate the transparency and quality of reporting of umbrella reviews, across different research questions. It will follow the guidance for reporting checklist, which are standard in the field. We propose that having reporting guidelines is of crucial relevance when included studies follow an observational design, with high baseline risk of confounding factors and numerous sources of bias potentially guiding misleading results. 11 59 We aim to publish the report of this project including the final PRIUR-CCC guidelines. The main limitation of this study will be inherent in the Delphi study design, where only completers of all rounds fully contribute to the project.

Ethics statements

Patient consent for publication.

Not applicable.

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X @dmoher, @CorteseSamuele, @eggersnsf

MS and KC contributed equally.

Contributors MS, KC, LH and AFC designed the protocol, drafted the first version of the protocol and finalised the final version. DM, SE, ED, JIIS, JR, SC, BS, NV, MP, ME, SP and JPI approved the initial study design, and revised multiple times the protocol and approved the final version.

Funding LH is supported by a Canada Research Chair in Knowledge Synthesis and Translation.LH is supported by a Canada Research Chair in Knowledge Synthesis and Translation.LH is supported by a Canada Research Chair in Knowledge Synthesis and Translation.

Competing interests MS received honoraria/has been a consultant for Angelini, Lundbeck, Otsuka.

Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the 'Methods' section for further details.

Provenance and peer review Not commissioned; externally peer reviewed.

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Cross Sectional Study

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A cross-sectional study is a research design used to gather data from a population or sample at a specific point in time. It aims to provide a snapshot of a particular phenomenon or explore the relationship between variables at a given moment. Unlike longitudinal studies that track individuals over time, cross-sectional studies focus on a single interval.

Whether you are investigating an unstudied topic or just can’t afford to spend too much time on research, cross sectional designs can work wonders. All you need is a single time and many different participants. Sounds easy, right? And it should be if you follow this guide from our writing service . Get ready for lots of insights and useful examples as you read our blog post. But first things first – let’s begin with the basics. 

What Is a Cross Sectional Study: Definition

A cross-sectional study is a type of observational research that allows assembling data from many different subjects at one point. Scientists usually rely on specific variables to pick the participants. As descriptive research, a cross-sectional study is used to observe something that already exists in a cohort. Thus, you won’t need to adjust or change variables.  Here are the main attributes that set cross-sectional studies apart from other types of research:

  • The population members are observed only once.
  • Various traits can be examined simultaneously.
  • Researchers don’t control the variables.
  • Method allows investigating predominant qualities within a group.

As a rule, cross sectional studies are carried out in developmental psychology. However, research paper writers also widely use this type of study in economics, education, medicine and social sciences. 

Cross-Sectional Study: When to Use

A cross-sectional study is used to explore the characteristics that are dominant in a specific group of people at a particular time. Researchers opt for this method when having to choose between time or expenses. It’s a time-wise option, especially if the data you have was gathered only once. Cross-sectional studies don’t require repeated experiments, and, thus, are budget-friendly.

Example of cross sectional study use case You want to find out how many people currently work remotely in your district. You only should learn the current number of individuals who work from home. For this reason, a cross-sectional study is preferred.

Descriptive Cross Sectional Study vs Analytical Cross Sectional Study

Depending on their main purpose, cross sectional studies can be either descriptive or analytical.  A descriptive cross sectional study is aimed at the prevalence of some characteristics in a population. It only describes the outcome. An analytical cross sectional study requires that you look for a relationship between the cause and outcome.

Example You are examining the occurrence of cardiovascular disease. In a descriptive research design, you will look for the prevalence of cardiovascular disease in older individuals. Meanwhile, in analytical research you will focus on recent radiation exposure as the main reason for heart diseases.

Cross-Sectional Study: How to Implement

There are two ways to conduct a cross sectional study design:

  • Use the data collected by another researcher/ organization.
  • Run your own research.

In the first case, you can use national or local government’s registers, surveys or reports by international organizations. Such data is easy to retrieve from official websites. On the flip side, your research question may differ, so do variables.  If you decide to do your own cross-sectional research, make sure you follow these steps:

  • Select participants using inclusion and exclusion criteria. Include only those subjects that have necessary attributes that will help to answer your research question. Consider such factors as age, gender , social status to include individuals in your research.
  • Examine the influence and results at the same time. Try to find an association between variables of interest. Sometimes, researchers may observe only the outcomes in subjects.
  • Measure the prevalence of traits within your population. Collect and analyze data about traits that prevail in your chosen group. You can also estimate odd ratios to explore the relation between variables.

Conducting a cross-sectional research study undeniably requires much effort. Sometimes, it’s better to buy research paper online or ask professionals to ‘ write my research paper .’

Cross Sectional Study vs Longitudinal Study

Now let’s look at the difference between a cross sectional study and a longitudinal study . Cross-sectional studies are executed to gather information from a population only once. Meanwhile, longitudinal studies are used to examine a small group of participants a number of times.

Many different people 

Small group

Specific point in time

Several times

Easy to run

More complex

Longitudinal research is more time-consuming and requires more resources. For this reason, you should be 100% sure that there is some kind of correlation between variables. And that’s exactly what cross-sectional research can help you with. As a cheap and easy option, it allows you to collect initial information about the subjects. This should be enough to decide whether it’s worth continuing further research. 

Advantages and Disadvantages of Cross Sectional Study

Cross sectional research is the best choice when it comes to gathering some basic information about some population. Advantages of cross sectional studies include:

  • Cheap data collection methods
  • Timesaving research option
  • Measurements of several variables at a time
  • Guidance to further experimental studies.

Limited time is one of the disadvantages of cross sectional studies. As an experiment that takes place only once, it also has some other limitations: 

  • Difficulty to identify causal relationship
  • No opportunity for long-lasting observation
  • Cohort effect among participants who share experience.

Cross Sectional Study Example

Now that you know all ins and outs, let’s review an example of cross sectional study. It should give you an idea of what this type of research should focus on.

Cross-sectional research example Researchers examine the influence of vitamin C  consumption on blood vessels. They first conduct cross-sectional research to identify if there is any change in blood vessels in those individuals who take in vitamin C. If there is some impact, researchers will want to explore this further.

Cross Sectional Research: A Word From StudyCrumb

Like any other research method, a cross-sectional study takes practice. Not that much as you would need to complete a longitudinal research, though… And yet, you should remember that this method won’t work if you want to identify a cause-and-effect relationship. Opt for this type of research if you want to run an initial experiment or just lack time.

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FAQ About Cross Sectional Research Design

1. what is cross sectional correlational study.

As a correlational study, cross-sectional research is used to examine the association between two or more variables. However, as with any other correlational research, you won’t have any chance to manipulate the cause (an independent variable). 

2. Is cross-sectional study qualitative?

In most instances, a cross-sectional study involves working with numbers and specific measurements, and, thus, is quantitative. However, sometimes researchers can also use this method to collect qualitative data or analyze both types of data.

3. How is cross sectional data collected?

Cross sectional data is usually gathered with the help of surveys, polls or self-administered questionnaires. These tools allow researchers to quickly collect information from a large population. However, surveys aren't always accurate and can lead to invalid results.

4. What evidence level is a cross-sectional study?

Based on the validity and overall quality, the evidence level of a cross-sectional study is rather low. The design takes the VI place in the hierarchy since it offers evidence from a single study.

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Predictors of perceived healthcare professionals’ well-being in work design: a cross-sectional study with multigroup pls structural equation modeling.

what is cross sectional study design in research methodology

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Nesic, D.; Slavkovic, M.; Zdravkovic, N.; Jerkan, N. Predictors of Perceived Healthcare Professionals’ Well-Being in Work Design: A Cross-Sectional Study with Multigroup PLS Structural Equation Modeling. Healthcare 2024 , 12 , 1277. https://doi.org/10.3390/healthcare12131277

Nesic D, Slavkovic M, Zdravkovic N, Jerkan N. Predictors of Perceived Healthcare Professionals’ Well-Being in Work Design: A Cross-Sectional Study with Multigroup PLS Structural Equation Modeling. Healthcare . 2024; 12(13):1277. https://doi.org/10.3390/healthcare12131277

Nesic, Danijela, Marko Slavkovic, Nebojsa Zdravkovic, and Nikola Jerkan. 2024. "Predictors of Perceived Healthcare Professionals’ Well-Being in Work Design: A Cross-Sectional Study with Multigroup PLS Structural Equation Modeling" Healthcare 12, no. 13: 1277. https://doi.org/10.3390/healthcare12131277

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  • Published: 24 June 2024

Modern contraceptive utilization and associated factors among postpartum women in Kena Woreda, Konso Zone, South Ethiopian Regional State, Ethiopia, 2023: mixed type community based cross-sectional study design

  • Abdulkerim Hassen Moloro   ORCID: orcid.org/0000-0002-6081-4108 1 ,
  • Solomon Worku Beza 2 &
  • Million Abate Kumsa 2  

Contraception and Reproductive Medicine volume  9 , Article number:  31 ( 2024 ) Cite this article

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Metrics details

Even though family planning 2020 has made remarkable progress about solving the issue of unmet need for family planning, 70% of women in a developing countries who do not want to conceive are not using it. There are limited research that provided detail information regarding barriers of modern contraceptive utilization during postpartum period in the study area. In addition, previous study also recommended that to conduct using mixed quantitative and qualitative design for further investigations to answer these “why” questions and narrow these gaps.

This study aimed to assess postpartum modern contraceptive utilization and associated factors among postpartum women in Kena woreda, Konso zone, South Ethiopian Regional State, Ethiopia, 2023.

A mixed type community based cross-sectional study design was conducted among 605 women in Kena woreda, from September 1–30/2023 out of 628 sampled mothers. Multistage sampling technique was used to select study participant and data was collected using semi-structured pretested questionnaire and entered in to Epi data version 3.1 and then exported to STATA version 14 for analysis for quantitative. The association between variables was analyzed using bivariate and multivariable binary logistic regression and level of significant determined with adjusted odd ratio at 95% CI and P-value less than < 0.05. After translation and transcription, manual thematic analysis was applied to the qualitative data.

The prevalence of modern contraceptive use among women during postpartum period in Kena woreda was found to be 39.01% [95% CI: 35.18–42.96%]. Menses resumed (AOR = 1.63; 95% CI: 1.02, 2.59), linked to the family planning unit during their child`s immunization (AOR = 2.17; 95% CI: 1.45, 3.25), family planning counselling during antenatal care visit (AOR = 1.63; 95% CI: 1.10, 2.42) and good knowledge towards modern contraceptive (AOR = 1.53; 95% CI: 1.03, 2.26) were factors associated with postpartum contraceptive utilization. Partner oppose, myths and misconception, need for excess family size, religious prohibition, fear of side effect,menses not resumed, lack of counselling and privacy room, and lack of transportation to health facility were barriers to modern postpartum contraceptive utilization.  

Conclusions and recommendations

The utilization of postpartum contraceptives was found to be lower than the target set by the 2020/21 national reproductive health strategy plan, which aimed to increase contraceptive method usage to 50%. Menses resumed, family planning counselling during antenatal care visit, linked to the family planning unit during child immunization and good knowledge were factors associated to modern postpartum contraceptive utilization. Strengthening service integration and family planning counseling during antenatal care visits and encourage mothers to start using modern family planning methods before menses resume are important. Overcoming barriers including partner opposition, myths, religious beliefs, fear of side effects, lack of counseling at health facilities, and transportation challenges is essential.

Introduction

Family planning is described as the ability of individuals and couples to achieve their desired number of children in a family when they have children, the age range between children, and the timing of their births by using contraceptive methods [ 1 , 2 ]. Postpartum modern contraceptive utilization is defined as women who have ever used any type of family planning method or avoidance of closely spaced pregnancies and unintended pregnancy during the first 12 months after she gave birth [ 3 ]. Intrauterine contraceptive device (IUD), implants, injectable, pills, and emergency contraception are among the modern contraceptives [ 4 ].

Globally, approximately 810 deaths associated with pregnancy and childbirth are recorded daily and about 94% of these maternal deaths occur in low-income and middle-income countries [ 5 ]. Sub-Saharan Africa accounts for approximately 66% of maternal death when the majority of their causes are preventable [ 6 ]. Ethiopia has a larger share of the World maternal death as 14,000 maternal deaths reported as of the year 2017; hence Ethiopia has vast homework to achieve SDG4 & 5 by 2030 [ 6 ]. The magnitude of postpartum modern contraception utilization in Ethiopia ranges from 12.05 to 80.32% [ 7 , 8 ].

Contraceptive use has reduced maternal death by 40% in the last 25 years worldwide and a further 30% of maternal death would fall if all women who want to avoid pregnancy use an effective contraceptive method in developing countries [ 9 ]. Increasing the accessibility of contraception methods among postpartum women is an important strategy because women may initiate sexual activity before obtaining FP methods at their 6-week postpartum visit [ 10 , 11 ]. The government of Ethiopia is working intensively to ensure affordable and accessible contraceptive methods. The country had written the Health Sector Transformation Plan to reach additional 6.2 million women and increase contraceptive prevalence to 55% until 2020 E.C.

Even though family planning 2020 has made remarkable progress about solving the issue of unmet need for family planning, 70% of women in a developing country who do not want to conceive are not using it [ 12 ]. In addition, the Ethiopian demographic health survey (EDHS) 2016 reported that 1in 5 married women had an unmet need for FP [ 13 , 14 , 15 ].

According to different cross-sectional-based studies; reproductive health characteristics like resumed menses, resumed sexual intercourse and fertility desire [ 16 ], mothers who received antenatal care service [ 17 ], women who gave birth with the assistance of a skilled birth attendant [ 18 ],maternal age, educational status [ 19 ], religious [ 20 ], poor economic status [ 21 ], and knowledge about contraceptive method [ 22 ] were significant factors of postpartum family planning utilization.

As the WHO recommended for better maternal and child health outcomes, postpartum women should wait for an interval of at least 2 years following a live birth before getting pregnant again [ 23 ]. In an effort to achieve a better future for all, SDG 3 targeted to reduce the maternal mortality ratio to less than 70 per 100,000 live births by 2030. The SDG plans to ensure universal access to sexual and reproductive health-care services including for family planning [ 24 ], which will be measured by the proportion of reproductive age women who have their need for FP satisfied with modern methods of contraception.

In the study area, there is limited research on postpartum contraceptive utilization. Additionally, due to traditional norms, contraceptive use has not been widely accepted. To address this gap, a qualitative study was conducted to explore sociocultural, perceptual, and economic barriers related to the utilization of modern postpartum contraceptives. Depending on previous research, which recommended a mixed quantitative and qualitative approach [ 25 ], this study aimed to assess postpartum modern contraceptive utilization and associated factors among women who delivered within the last twelve months in the Kena woreda, Konso zone of the SNNP Region, Ethiopia.

Study area and period

The Kena woreda is located in Konso zone, at 650 Km southwestern of Addis Ababa and found in the South Ethiopia Regional State of Ethiopia. The Kena woreda has a total population of 307,321, of whom 148,070 are men and 159,251 women and 15,048 households, with the average of 5.24 family sizes according to the 2007 national census which was conducted by the Central Statistical Agency of Ethiopia (CSA). The socioeconomic status of the community is characterized by local sectors such as beekeeping, cotton weaving, and agriculture. The Kena woreda divided into 13 kebeles. There are 4 governmental health centers, 19 health post, 3 private clinics and 1 drug store. All government health facilities and private clinics currently providing health services including MCH services in the kena woreda. The study was conducted from September 1–30, 2023 in Kena woreda.

Study design

A mixed type community-based cross sectional study, supplemented by qualitative was conducted.

Source population

All women who gave childbirth within the last twelve months and live in the Kena woreda prior to this study were considered as a source population.

Study population for quantitative study

All randomly selected mothers who gave birth in the last twelve months, in the selected kebeles of Kena woreda were considered as a study population.

Study population for qualitative study

All purposively selected mothers with history of postnatal contraceptive use and non-users, health extension workers (HEWs), husbands of family planning user and non-user, Maternal and child health coordinators and health professionals who work family planning service were considered as a study population for qualitative study.

Inclusion criteria

All women’s who give birth in the last 12 months period before data collection regardless of their birth outcome were included in the study.

Exclusion criteria

Woman who lived in the study area for less than 6 months.

Women who cannot communicate and critically ill.

Exclusion criteria for qualitative part

Participants interviewed in the quantitative survey were excluded from in-depth interview.

Sample size determination

Sample size determination for both quantitative and qualitative.

Sample size for both first objective and second objective were calculated using Open Epi online software version 3.0.1 to use large sample size. Accordingly sample size for first objective was calculated using single population proportion formula considering the following assumptions: 54.7% prevalence of postpartum family planning utilization from study conducted in Addis Zemen town, South Gondar, Ethiopia in 2019 [ 25 ], 5% margin of error and 95% CI and and finaly 381 obtained. Since it is multi stage sampling technique used, design effect applied and multiplied by 1.5 becomes 571 and by adding a 10% non-response rate, the first objective sample size becomes 628.

Qualitative data were collected using In-depth interviews among postnatal mothers who use and not use contraceptive, husband and from key informants. The number of sample was determined using the principle of “saturation”- women and key informants were asked to participate in interviews until additional interviews did not provide additional evidence about the main themes of interest and a total of 13 (2 modern contraceptive users mother, 6 non-users, 1 husband, 4 health care provider 2 health extension workers, and 2 maternal and child health care coordinator) participants included in In-depth interviews.

Sampling procedure for quantitative and qualitative study

A multistage sampling technique was used to select the study participants. In the six kebeles, there were 1298 women who gave childbirth in the last twelve months in the six kebeles. First, six representative kebeles were selected from thirteen kebeles using simple random sampling technique (lottery methods). In the second stage, computer generated sampling method was used to select the households from six selected kebeles. The sample frame of households (mothers who delivered in the last one year) was prepared in all selected kebeles from mother’s registration book which was found from health extension workers. Then study participants were proportionally allocated for each selected kebeles (See figure 1 ).

Finally, the calculated 628 study participants were selected through computer generated random sampling technique. One mother was selected per household. If two or more eligible women were encountered in one household, only one was included using lottery method and if no eligible women are identified in the selected household, the next household nearest to eligible household located in a clockwise direction was visited until the desired sample size were achieved. If the participant in the selected household was not present at the time of data collection, three revisits were made to interview the woman. The next household replaced for those absent household after tree revisit.

figure 1

Schematic representation of sampling techinque to assess postpartum modern contraceptive utilization among postpartum women who gave birth in the last 12 months in Kena woreda, Konso zone, South Ethiopia Regional State of Ethiopia, 2023

For qualitative data, the heterogeneous (maximum variation) type of purposive sampling technique was employed to select the participants until data saturation were reached. Modern contraceptive method users and non-users, husbands, health extension workers, maternal and child health care coordinator, and health professionals work on family planning service were included in the qualitative study. All participants were selected purposively based on their close relationship with mother and involvement in the community. The numbers of in-depth interview were determined based on level of saturation of the required information. Thirteen participants reached saturation, and a total of 13 participants were included for qualitative data.

Dependent variables

Postpartum modern contraceptive utilization.

Independent variables

Socio demographic variables : Age, marital status, educational status, religion, ethnicity, occupational status of the mother, and husband educational status and occupation,.

Knowledge : Heard any methods, type of contraceptive, source of information, time of contraceptive start and benefit of family planning.

Reproductive and maternal health service use-related characteristics : Parity, birth interval, pregnancy status, discuss about family planning with partner/husband, ANC care, ANC Family planning counseling, postnatal care, postnatal FP counselling, place of delivery, delivery assistant, immunization visit for child, reproductive intention, menstrual resumption, sexual resumption, and postpartum period in weeks.

Operational and term definitions

Modern contraceptives : Sterilization (male and female), intrauterine devices, implants, oral contraceptives, condoms (male and female), injectable, emergency contraceptive pills and spermicidal agents [ 26 ].

Postpartum contraceptive utilization : Utilization of the postpartum family planning was defined as a postpartum woman using any one of modern postpartum FP methods (progesterone-only pills, intrauterine contraceptive device, injectables, dual method, sterilisation (permanent FP method) or implants) during the first 12 months after she gave birth [ 27 , 28 , 29 ]. The utilization was measured by mothers’ words by yes or no options for use (yes = 1, no = 0).

Knowledge of postpartum contraceptive : Mother was considered to have good knowledge if she correctly answered greater than or equal to the mean score of the total knowledge assessing questions and considered as poor knowledge if she answered less than to the mean score of the total knowledge assessing questions [ 8 , 30 , 31 ].

Data collection tool and procedure

Data collection tool and procedure for quantitative and qualitative.

The quantitative data were collected via interviewer administered approach by using semi-structured and pretested questionnaires adapted from different literatures [ 8 , 16 , 20 , 25 , 28 , 31 , 32 ]. The tool first prepared in English language after reviewing different literature. The data were collected by trained 5 Diploma Nurse in which 4 are data collectors and 1 is supervisors from selected Keble’s. Training was given for data collectors and supervisors for one day on data collection procedures, interview techniques and confidentiality of the information obtained from the respondents. Finally, data collectors were interviewed participants face to face at home for 25–30 min.

The qualitative data were collected using in-depth interviews by two individuals who have previous experience until information saturation was reached. Audio record and note were taken for each interview, after obtaining voluntary written or oral signed consent from the interview participants.

Data quality control

Data quality control for quantitative and qualitative.

Data quality was ensured during collection, entry and analysis. Before conducting the main study, pretest was carried out on 5% [ 31 ] of the sample size in Fasha kebele which was non-selected kebele and necessary modification was made. The principal investigator and supervisors were conducted a day-to-day on-site supervision during the whole period of data collection. At the end of each day, the questionnaires were reviewed and checked for completeness and accuracy by the supervisors and investigator. Then corrective modification was made by the principal investigators.

To maintain validity, the questionnaire was prepared in English and then translated to local language (Konso language version) and back translated to English by language experts to ensure consistency and accuracy. Data were checked for completeness, accuracy, clarity, and consistency before being entered into the software. Proper coding and categorization of data was maintained for the quality of the data to be analyzed.

Open ended in-depth interview questions taken from different literatures were used for qualitative data collection. The data was collected by the principal investigator by audio recording and note taking. The accuracy for the qualitative component was addressed by ensuring credibility, dependability and transferability. Furthermore, sufficient time (30–40 min) objective and impartial view maintained to collect data were added the reliability of the data.

Data Processing and Analysis for quantitative and qualitative

The pre coded data were entered into Epi data version 3.1software and exported to STATA version 14 software for statistical analysis. Data exploration, editing and cleaning were undertaken before analysis. Descriptive statistics such as mean and standard deviation were used for continuous data and percentage, frequency, tables and cross tabulation were used for categorical data.

Regarding multi-collinearity among independent variables, STATA itself was controlled and checked via variance inflation factor (VIF) and tolerance. Variable VIF greater than 10 and tolerance less than 0.1 were removed in STATA. Binary logistic regression analysis was conducted to see factor associated with modern contraceptive use and variables with P value less than 0.25 in bivariable analysis will be transferred for multivariable logistic regression to control the confounders.

Final model fitness were checked by Hosmer and Lemeshow test and model adequacy was declared when p-value > 0.05. Accordingly, final model of Hosmer and Lemeshow showed 0.098. The significance was checked and declared using p-value less than 0.05 and 95% confidence interval in the final model. Strength of association was interpreted by using adjusted odds ratio with 95% confidence interval.

Primarily, audio recorded data were heard repeatedly until the principal investigator became intimately familiar with the contents. The audio taped qualitative data were first transcribed in to original language and then translated to English language by principal investigator. Then, codes or terms were identified and tallied to come up with same categories, which later used to establish themes based on the objective of the study. Unique concepts were identified and trials were made to elaborate more and reported on the final result. Finally, thematic analysis was done manually and the findings were triangulated with the quantitative one.

Ethical consideration

Ethical approval was obtained from the Institutional Ethical Review committee (IERC) of GAMBY Medical and Business College with IERC protocol number (Ref.No_GMBCIB/IRB/846/2023). Following the approval by IERC, official letter of co-operation were written to Kena woreda health administration office and in turn the woreda health administration office was wrote letters to each selected kebeles and village’s administration office in order to get permission and cooperation. The oral informed consent from the respondent was obtained after the purpose and objective of study explained. To ensure confidentiality, a name of respondents was replaced by code numbers. Participants were given the chance to ask any doubt about the study and made free to refuse or stop the interview at any moment they want.

Socio-demographic characteristics

Out of the 628 respondents sampled, six hundred five women participated in the study, resulting in a response rate of 96.34%. The mean age of the mothers was 28.28 (SD ± 4.91), ranging from 17 to 39 years. Majority of the respondents 286(47.27%) were followers of the protestant religion and 476 (78.68%) of the study participants belong to the Konso ethnicity. In this study, 564 (93.22%) of the mothers were married and 440 (23.33%) were housewives. Of the mothers, 419 (69.26%) were unable to read and write followed by primary education level which represented, 71(11.74%)(See Table 1 ).

Reproductive and maternal health characteristics

Mothers had a mean parity of 2.75 (SD ± 1.56), and the mean value of living children was 2.71 (SD ± 1.55). In terms of their reproductive intention, 210 (34.71%) mothers desire to have children, while 197 (32.56%) desire to space out their children over time. More over half of the mothers, 349 (57.69%), stated that their periods had returned after giving birth, and more than three-quarters, 442(73.06%), reported that they had resumed their sexual activities. Almost all mothers, 559(92.4%) had ANC visit for last pregnancy and 241(43.11%) had four and above times ANC visit.

Regarding family planning counselling, more than half of the mothers, 298(53.31%) was counselled during ANC visit and 180(29.75%) mothers were counselled during postnatal care visit. 390(64.46%) of the mothers, had delivered at health institution and 215(35.54%) of the mothers had assisted by midwifes during delivery. Majority of respondents, 543 (89.75%) had visit immunization clinic for child and 177(32.6%) had linked to family planning unit during child immunization. (See Table 2 ).

Knowledge on postpartum modern contraception use

More than three-fourth of the study participants, 467 (77.19%) had heard at least one modern contraceptive method. Majority of the mothers, 440(94.22%) heard about injectable FP methods and 471 (77.85%) responded that postpartum family planning should be started to use after two months of delivery. Majority of the respondents, 536 (88.6%) said about the benefit of postpartum family planning methods utilization is to limit the number of children. Regarding the overall knowledge on postpartum family planning, majority of respondents, 353 (58.35%) had good knowledge of PPFP methods ( See Table  3 ).

Modern contraceptive use in the postpartum period

In this study, the prevalence of modern contraceptive use among women in the postpartum period was found to be 236 (39.01%)[95% CI: 35.18–42.96%]( See Fig.  2 ). The most commonly used contraceptive method was injectable 118 (50%) followed by implants 89 (37.71%) and pills method users 26(11.02%). One hundred fifty nine (67.37%) of the mothers, who currently use FP methods started using the methods following menstruation, 40 (16.95%) of postpartum mothers began using before menstruation resumed, and 37 (15.68%) of women began using it immediately after giving birth. The majority of mothers, 189 (80.08%) receive PPFP via the public health system, followed by private health facilities 37 (15.68%) and pharmacies/drug sellers 10 (4.24%). The reasons for not using contraceptives during postpartum period for 147 (39.84%) of the participants was feeling of want to deliver soon. Next to this, others 82(22.22%) reported that due to menses not resumed ( See Fig.  3 ).

figure 2

Modern contraceptive use of women in postpartum period in Kena woreda, Konso zone, South Ethiopia Regional State of Ethiopia, 2023 ( n  = 605)

figure 3

Reasons for not using modern contraceptive during the postpartum period among women in Kena woreda, Konso zone, South Ethiopia Regional State of Ethiopia, 2023 ( n  = 605)

Factors associated with postpartum modern contraceptive use

Based on bivariable logistic regression, mothers age, ANC visit, ANC counselling about family planning, visit immunization clinic for child, linked to family planning during child’s immunization, menses resumed, resumed sexual activity, postpartum period in weeks, and good knowledge towards modern contraceptives were factors have p-value less than 0.25 and all were transferred to multivariable logistic regression analysis to control the effect of confounder.

Factors which have p-value greater than or equal to 0.25 in bivariable logistic regression for all categories were not considered for multivariable logistic regression analysis. However, in the multivariable logistic regression analysis, mothers aged 15–24, mothers linked to family planning from immunization clinic, postpartum period from 39 to 50 weeks, and good knowledge were identified as predictors of postpartum modern contraceptive use among mothers ( p  < 0.05). Due to multicollinearity STATA itself ommitted the variable named "visit immunization clinic" in the multivariable reggression. For bivariable analysis both confidence interval and p-values were presented but for multivariable analysis only confidence intervals were presented.

Accordingly, findings from multivariable logistic regression showed that, odds of using modern contraceptive during postpartum period among menses resumed mothers were 1.63 more likely than their counterpart (AOR = 1.63; 95% CI: 1.02,2.59). Similarly, odds of mothers who are linked to the FP unit during their child`s immunization were 2.17 more likely to use modern contraceptive method during postpartum period than their counterpart (AOR = 2.17; 95% CI: 1.45, 3.25). Again this study showed that the odds of using modern contraceptive methods during postpartum period among mother counselled about family planning during ANC visit were 1.63 more likely than their counterparts (AOR = 1.63; 95% CI: 1.10, 2.42). Furthermore,  mothers who have good knowledge towards modern contraceptive were 1.53 more likely to use modern contraceptive method during postpartum period than their counterpart (AOR = 1.53; 95% CI: 1.03, 2.26)( See Table  4 ).

Qualitative findings

In-depth interview was conducted among 13 participants (2 health extension workers, 4 non-contraceptive user women, 2 husbands, 2 women who have previous history of contraceptive use, 1 midwifery women who work in ANC at health center and 2 male who coordinate and work at maternal and child health center) to explore the barriers of postpartum contraceptive use. The study saturation was obtained within thirteen participants. To triangulate the findings, in-depth and key informant interviews findings were summarized into themes that emerged during interviews. Generally, most repeatedly mentioned barriers by the participants were categorized in to three main themes (sociocultural barriers, individual barriers and health service related barriers) which included eight sub themes (categories) in it.

Sociocultural barriers

The sociocultural barriers include four sub-themes: husband/partner oppose, myths and misconception on contraceptive, need for excess family size, and religious prohibition.

Sub-theme: Husband/partner oppose

During in-depth interviews, nearly all participants consistently highlighted that opposition from husbands serves as a significant barrier to utilizing postpartum contraceptive methods. According to their ideas, women seeking to use contraception must first obtain their husband’s permission. Additionally, they emphasized that women who practice family planning without their husband’s awareness may face serious consequences, including warnings, divorce, physical violence, and family conflicts.

One postpartum mother, 30 years old, expressed her feeling to space her pregnancies by three years due to a previous one-year interval. However, her husband opposed this plan. She stated, ‘ My pregnancies are always close together, and I don’t want to be pregnant again until our child is three years old. Unfortunately, my husband doesn’t allow me to use any modern contraceptive methods ” IDI participant (Postpartum contraceptive non-user) .

During the interviews, a 25-year-old postpartum mother shared her perspective. She mentioned that she currently has an 8-month-old child and prefers not to become pregnant again for the next three years. However, her husband disagrees. Her statements are stated in the following quotes: ‘… my child is now 8 months old, and I don’t want to conceive until my child is 3 years old, but my husband doesn’t allow me to seek contraceptive services at health facilities ” IDI participant (Pospartum contraceptive non user) .

Furthermore, one postpartum woman, aged 22, reported that she had previously used the injectable contraceptive (Depo Provera). However, her husband expressed opposition, leading her to discontinue its use. She supported her statement with the following quote: ‘ I have used Depo Provera three times in the past, but I stopped after my husband recognized it and warned me to discontinue ” IDI participant (Postpartum contraceptive user) .

Sub-theme: myths and misconception on contraceptive

During our interviews, many participants emphasized a prevailing belief in the community that modern contraceptive use is contingent upon having a balanced diet. If a husband cannot provide sufficient food, it is believed that contraceptive use may lead to illness and negative health outcomes. Consequently, women who lack access to a balanced diet are discouraged from using contraceptives. For instance, a 31-year-old postpartum mother expressed her desire to use contraceptive methods but hesitated due to the perceived dietary requirements. Her partner, who had used contraceptives before, informed her about this, and her husband also cautioned against using them without an adequate and balanced diet. She evidenced her idea in the following quote “……… I want to use contraceptive methods, but I fear it due to it needs a balanced diet as informed from my partner who used it previously. In addition, my husband also told me that not to use without enough and balanced diet”. IDI women (postpartum mothers who are non-users of contraceptives).

Among the community, there is a misconception that contraceptive use prevents future pregnancies and leads to infertility after discontinuation. During a key informant interview, a 32-year-old health care provider shared her ideas in the following quote “…. most of the mothers believe that they cannot become pregnant once they have used and removed contraceptives. Additionally, they perceive that contraceptives cause infertility ” IDI key infromant participants (Health care provider).

Sub-theme: need for excess family size

Another sociocultural factors act as barriers to contraceptive use. One such factor is the cultural desirability of having excess children and maintaining a large family size. Participants consistently reported that their communities associate having many children with love and respect. Consequently, families strive for a larger family size to gain esteem within their clans and society. As a result, husbands and other family members often discourage women from using contraceptive methods, believing that having more children enhances their status. For instance, a 36-year-old husband in an interview stated, “ Our culture encourages us to have excess children to earn love and respect within our clan and society. Additionally, we seek marital alliances with families that have large family sizes, as we believe that if a mother can bear many children, her daughters will also do the same ”. ( IDI participants, Husband of the non-user mother )

Sub-theme: religious prohibition

Another’s barriers reported by participants was religious prohibition. Some community members perceive contraceptive use as tantamount to ending a life that has already been conceived, and thus consider it a sin. They hold the belief that God provides for our children and us. A 27-year-old non-user mother expressed her perspective: “ I am still having children, and I want to continue having them without gaps. God (Egzaber) sustains all of us, including our children, and shapes our destinies in terms of what we eat and how we live. Therefore, contraceptive utilization is deemed sinful” IDI participants (postpartum non-user mother) .

Individual barriers

The individual barriers include two sub-themes; fear of side effect, and menses not resumed.

Sub-theme: fear of side effect

The majority of respondents acknowledged that fear of side effects is a significant barrier to the low utilization of postpartum contraceptives. A 28-year-old non-user mother mentioned that she had heard about side effects such as bleeding and headaches from her friend. Based on her friend’s experience, she was hesitant to use contraceptives herself. The participant’s quote reflects this concern: “…. I want to use contraceptive methods, but I fear side effects like excessive bleeding, dizziness, and headaches, which my friend experienced. Additionally, my friend stopped using contraceptives and advised me not to use them either ” IDI participant (Non-user postpartum mother) .

In addition, another 28-year-old mother stated in the following quotes, “……I am aware that my friend used the injectable contraceptive (Depo-Provera) in the past, and she found it quite challenging. She mentioned experiencing loss of appetite and significant weight loss.’ IDI participant (Non-user postpartum mother) .

Furthermore, another 20-year-old mother shared, ‘……I’ve been using the injectable contraceptive Depo-Provera for two years, and I’ve faced some challenges. These include loss of appetite, bleeding, and significant weight loss. If I completed the schedule, I will remove it” ’ IDI participant (Postpartum contraceptive user mother) .

Sub-theme: Menses not resumed

Another frequently reported factor was the lack of resumed menstruation. A 34-year-old non-user mother expressed that she did not consider using contraceptives. She justified her decision by saying, ‘…since my menstruation has not resumed, I don’t want to use it” IDI women (postpartum non-user mother) .

Health service related barriers

The health service related barriers include two sub-themes; lack of counselling and privacy room, lack of accessibility to contraceptive methods and lack of transportation to health facility.

Sub-theme: lack of counselling and privacy room

According to feedback from in-depth interview participants, the lack of counseling poses a significant barrier to the utilization of postpartum contraception. A 26-year-old healthcare provider highlighted the challenge of insufficient counseling for mothers who deliver in our health facilities due to the absence of a dedicated family planning counseling room. Additionally, she mentioned that counseling and implanon procedures are conducted in the delivery room. Her sample quotes reinforce the idea that “……. despite efforts to provide counseling, patient overcrowding and busy schedules hinder comprehensive postpartum counseling. As a result, counseling is sometimes delivered at the bedside, during delivery, and during antenatal care (ANC) visits ” IDI key informant participants (Health care provider.

A 29-year-old healthcare provider mentioned that “……. they provide counseling to mothers who come for child immunization. They observed that mothers are more likely to use postpartum contraceptives when they receive counseling. The provider believes that if both the husband and mother receive counseling simultaneously, they would be more likely to use contraceptives ” IDI Key informant participants (Health care provider) .

Sub-theme: lack of accessibility to contraceptive methods

Many respondents emphasized that lack of transportation access, poor road infrastructure, and long distances to health facilities contributed to low contraceptive use. Health care providers and maternal and child health coordinators noted that most mothers preferred using Implanon and injectable contraceptives (such as Dipo Provera). However, shortages of these contraceptives sometimes prevented their use. During an interview a 32-year-old health care provider highlighted that “…… mothers often complained about transportation challenges due to distance from health facilities. As a result, they faced difficulties attending antenatal and postnatal care. The shortage of specific contraceptive methods further compounded the issue ” IDI Key informant participants (Health care provider).

In this study the prevalence of modern contraceptive use among women in the postpartum period was found to be 39.01% [95% CI: 35.18–42.96%]. This finding is comparatively in line with other studies conducted in Ethiopia, rural Tigray region 38.3% [ 33 ], Oromia regional state 40.7% [ 34 ], Hawassa Town 38.6% [ 35 ] Durame Town 36.7% [ 36 ] and Debre Berhan 41.6% [ 16 ]. The findings also in line with studies conducted in Nigeria 39.8% [ 37 ], low-income countries of SSA was 37.41% and Eastern Africa 41.36% [ 38 ] and Nepal 37% [ 39 ].

However, the finding is comparatively lower than to previous studies conducted in different parts of Ethiopia namely, south Gondar 54.7% [ 25 ], Debre Tabor town 63% [ 40 ], Northwest Ethiopia 60.6% [ 32 ], Gozamen district 46.7% [ 41 ], Bahir Dar city 48.8% [ 42 ], Gondar 45.8% [ 30 ], Arba Minch town 44% [ 43 ], Aksum 48% [ 28 ], Gida Ayana district Oromia region 45.4% [ 44 ], Addis Ababa 80.3% [ 7 ], Ganta-Afeshum District Eastern Tigray region 68.1% [ 45 ], Butajira 47% [ 46 ], Dessie Town 44% [ 17 ], and Addis Zemen 44% [ 28 ]. The possible discrepancy for this prevalence may be justified by the fact that the study area is might be attributed to low maternal health care service utilization. This study was also conducted mainly in rural kebeles where low educational status, poor counselling about contraceptive and lack of transportation to health facilities might be a reason for less utilization of PPFP compared to studies mentioned above as most of them were conducted in the town [ 47 , 48 ]. Therefore addressing disparities in contraceptive utilization requires a multifaceted approach that considers regional context, education and counselling, and access to healthcare services. By promoting modern contraceptive methods, maternal and child health outcomes can also be improved while advancing human rights and sustainable development [ 49 ].

This finding also found to be lower compared with studies conducted in Rural Kenya 86.3% [ 50 ], Mexico 47% [ 51 ], Kenya 46% [ 52 ], Tanzania 46% [ 53 ], Ntchisi district hospital Malawi 75% [ 22 ], and Rwanda 51% [ 54 ]. The possible reason for this discrepancy might be attributed to the differences in policy, variation in service accessibility, and maternal health care service utilization [ 55 ].

This finding is also comparatively greater than to previous studies conducted in different parts of Ethiopia namely, Burie District 20.7% [ 56 ], Dabat 10.3 [ 19 ], Dello-Mena 14.3% [ 57 ], Lode Hetosa 15% [ 58 ], Kebribeyah Town, Somali Region, Eastern Ethiopia 12.3% [ 58 ], Dubti town 30.1% [ 59 ] and 2016 EDHS secondary data analysis report 23% [ 60 ]. It was also higher than studies conducted in Uganda 28% [ 61 ], India 17% [ 62 ], Nepal 32.8% [ 63 ], Western Africa 9.45% and Central Africa 6.9% [ 38 ] and Burundi 20% [ 54 ]. The difference might be attributed to cultural background of the populations, relatively high unmet need for family planning and reproductive characteristic variation [ 64 ]. Therefore addressing policy gaps, cultural and social context, unmet family planning needs, and individual reproductive characteristics can lead to better maternal health care utilization [ 48 ].

This study showed that socio-demographic, maternal and reproductive characteristics and knowledge level of the mothers were significantly associated with the utilization of modern PPFP methods. Accordingly, odds of using modern contraceptive during postpartum period among mothers of menses resumed were 1.63 more likely than counterpart. This agrees with the study done in Gondar town, South Gondar, East Gojam Zone, Northwest Ethiopia, Debre Berhan Town, Aroressa District, Southern Ethiopia and Malawi [ 16 , 22 , 25 , 30 , 32 , 41 , 65 ]. This might be due to postpartum women whose menses have returned after delivery may assume that they are at risk of getting pregnant, so this can encourage them to start postpartum FP methods early [ 66 ]. The other probable reason might be those women whose menses have resumed, and at the same time, their sexual activities may resume. Because of this, they may perceive that they are at risk of unintended pregnancy. The qualitative finding also supports this idea, which showed that resumption of menses encourages women to utilize postpartum contraceptive. Therefore, addressing socio-demographic factors, promoting education, enhancing partner communication, and providing targeted counseling can contribute to increased utilization of modern FP methods during the postpartum period [ 31 ].

This findings also showed that mothers who are linked to the FP unit during their child`s immunization were 1.96 times more likely to use modern contraceptive methods during postpartum period. This is in line with studies conducted in Injibara town, Northwest and Southern Ethiopia [ 31 , 32 , 46 ]. The possible explanation might be that child immunization creates a good opportunity for counseling about the advantage of FP utilization, birth space, and rising maternal and child health-related issues. Therefore to ensure these, maternal health care services and regular immunization services are a continuous point of contact to provide information about FP, offer services, and link women to PPFP services [ 67 ]. The qualitative finding also supports this idea, which showed that opportunity of counseling about the advantage of FP encourages women to utilize postpartum contraceptive. Therefore, integrating FP services with childhood immunization can improve contraceptive uptake and overall maternal and child health outcomes [ 68 ].

The odds of using modern contraceptive methods during postpartum period among women counselled about family planning during antenatal care visit were 1.63 more likely to utilize postpartum contraceptive than their counterpart. This finding is in line with studies conducted in Northern Ethiopia, Somalia Region, Eastern Ethiopia, North west Ethiopia and Aroressa District, Southern Ethiopia [ 8 , 28 , 32 , 65 ] which indicated that mother counselled about family planning during antenatal care were more utilize contraceptive than counterpart. This might be due to those women who utilize family planning methods may be properly counseled by health care providers during their ANC visits about the available methods of FP and the consequences of frequent childbirths [ 69 ]. The qualitative finding also supports this idea, which showed that counselled women about the FP in the health facilities encouraged using postpartum contraceptive. Therefore, addressing misconceptions and fears related to contraceptive methods during ANC visits can help reduce unmet need for contraception. Integrating family planning services within ANC facilities can improve access and utilization. When women receive counseling during pregnancy, they are more likely to continue using contraception postpartum [ 70 ].

Mothers who have good knowledge towards modern contraceptive were 1.53 more likely to use modern contraceptive method during postpartum period. This finding is similar with studies conducted in Gondar Town, Injibara town, South Gondar, Butajira and Gozamen district [ 25 , 30 , 31 , 41 , 46 ]. The possible reason might be having good knowledge of FP methods may increase the chance of utilizing modern PPFP methods and suggested that time of starting postpartum family encourage mothers to initiate postpartum contraceptive utilization within one year after delivery [ 31 , 71 , 72 ]. Therefore, enhancing knowledge about modern contraceptive methods among mothers can lead to better family planning practices and contribute to maternal and child health [ 73 ].

Findings from the qualitative data summarized that, husband/partner oppose, myths and misconception on contraceptive, need for excess family size, religious prohibition, fear of side effect, menses not resumed, lack of counselling and privacy room, lack of accessibility to contraceptive methods and lack of transportation to health facility were barriers to use modern postpartum family planning. This findigs is similar with a facility-based cross-sectional study conducted in Western Ethiopia, which explored the barriers and determinants of postpartum family planning (PPFP) uptake among women visiting Maternal, Neonatal, and Child Health (MNCH) services in public health facilities [ 74 ]. This findings also similar with study conducted in zanzibar which stated that limited access to services, fear of side effects, cultural or religious opposition were barriers to modern contraceptive utilizations. To address these issues, increased knowledge about family planning methods and involving husbands in education and counseling during pregnancy, childbirth, and the postpartum period are crucial steps.

The study was used primary data and is community-based mixed methods which would help to know the real practice and dig out barriers that hinder the utilization of PPFP at the community level. However, social desirability bias might be the challenge when a woman answers the questions and women were asked sexual and reproductive behavior might diminish honest responses due to the cultural sensitiveness of the issue.

Conclusion and recommendation

In this study, the prevalence of utilization of modern postpartum family planning methods during the postpartum period among postpartum women in konso district was low compared to the WHO recommendation for postpartum women. Menses resumption, link to the family planning unit during their child`s immunization, family planning counselling during antenatal care visit and good knowledge towards modern contraceptive were found to be significantly associated with postpartum contraceptive utilization. The qualitative, data also identified need for excess family size, religious prohibition, fear of side effect, menses not resumed, lack of counselling and privacy room, lack of accessibility to contraceptive methods and lack of transportation to health facility were barriers to use modern postpartum contraceptive utilization.

Therefore, enhancing family planning (FP) counseling during antenatal care (ANC) visits, delivery, and child immunization is crucial to minimize missed opportunities for postpartum women to access contraceptive methods. These interactions with healthcare providers serve as effective entry points for providing accurate information about FP options and reducing missed chances for postpartum women to adopt modern contraceptive methods. Additionally, empowering women through increased educational access plays a vital role in enhancing their knowledge about modern contraceptive utilizations at healthcare facilities.

Its also important to strengthen community-based education and counseling programs to address misconceptions and provide accurate information, promote awareness about contraceptive options through religious leaders and community influencers and improve access to contraceptive methods by expanding distribution points and ensuring transportation options for individuals seeking postpartum care. Encouraging bilateral discussions between women and their husbands about reproductive matters is essential also essential points. Furthermore, promoting the early use of modern FP methods before the resumption of menses can help mitigate the risk of unintended pregnancies.

Data availability

Data for this article is avaliable up on reasonable request for principal investigator.

Abbreviations

Antenatal care

Confidence interval

Census Statistical Agency

In-depth Interview

Ethiopian Demographic Health Survey

Health Extension Worker

Health Worker

In-Depth Interview

Key Informant Interview

Southern Nation and Nationalities People

World Health Organization

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The authors would like to thank GAMBY College, regional health office, zone health office, woreda health bureau and administrative office, Woreda health extension worker, data collectors, supervisors and study participants for their cooperation in the study.

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Abdulkerim Hassen Moloro and Million Abate Kumsa : designed the study and methods, performed analysis and interpretation and wrote the main manuscript text. Dr. Solomon Worku Beza assisted in designing the study and methods, came up with the first draft, interpretation, prepared figures and critically revised the manuscript. All authors read and approved final manuscript.

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Moloro, A.H., Beza, S.W. & Kumsa, M.A. Modern contraceptive utilization and associated factors among postpartum women in Kena Woreda, Konso Zone, South Ethiopian Regional State, Ethiopia, 2023: mixed type community based cross-sectional study design. Contracept Reprod Med 9 , 31 (2024). https://doi.org/10.1186/s40834-024-00292-w

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    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.

  5. Cross-Sectional Study

    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 ...

  6. Cross-Sectional Study in Research

    A cross-sectional study is a type of observational research design that analyzes data from a population, or a representative subset, at one specific point in time. Unlike longitudinal studies that observe the same subjects over a period of time to detect changes, cross-sectional studies focus on finding relationships and prevalences within a ...

  7. Methodology Series Module 3: Cross-sectional Studies

    Abstract. Cross-sectional study design is a type of observ ational study design. In a cross-sectional study, the investigator measur es the outcome and th e exposures in the study participan ts at ...

  8. LibGuides: Quantitative study designs: Cross-Sectional Studies

    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.

  9. The Definition and Use of a Cross-Sectional Study

    Cross-Sectional vs. Longitudinal Studies . 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.

  10. Methodology Series Module 3: Cross-sectional Studies

    Abstract. Cross-sectional study design is a type of observational study design. In a cross-sectional study, the investigator measures the outcome and the exposures in the study participants at the same time. Unlike in case-control studies (participants selected based on the outcome status) or cohort studies (participants selected based on the ...

  11. Cross-Sectional Research Design

    This chapter addresses the peculiarities, characteristics, and major fallacies of cross-sectional research designs. The major advantage of cross-sectional research lies in cross-case analysis. A cross-sectional study aims at describing generalized relationships between distinct elements and conditions. The specific case and its particularities ...

  12. (PDF) Study Design III: cross-sectional studies

    Method: this research is a descriptive study with a cross-sectional approach. This study used the blood culture method. The media used in this study were oxgall, SSA, KIA and IMViC media.

  13. Cross-Sectional Studies : Strengths, Weaknesses, and ...

    In medical research, a cross-sectional study is a type of observational study design that involves looking at data from a population at one specific point in time. In a cross-sectional study, investigators measure outcomes and exposures of the study subjects at the same time.

  14. An introduction to different types of study design

    We may approach this study by 2 longitudinal designs: Prospective: we follow the individuals in the future to know who will develop the disease. Retrospective: we look to the past to know who developed the disease (e.g. using medical records) This design is the strongest among the observational studies. For example - to find out the relative ...

  15. What is a Cross-Sectional Study? Definition and Examples

    A cross-sectional study, or cross-sectional analysis, is a type of observational research design that involves the collection of data from a sample of individuals or subjects at a single point in time. Read this article to for more details on what a cross sectional study is, limitations, advantages, disadvantages, and examples.

  16. Cross-Sectional Studies: Strengths, Weaknesses, and ...

    Abstract. 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 ...

  17. What (Exactly) Is A Cross-Sectional Study?

    A cross-sectional study (also referred to as cross-sectional research) is simply a study in which data are collected at one point in time. In other words, data are collected on a snapshot basis, as opposed to collecting data at multiple points in time (for example, once a week, once a month, etc) and assessing how it changes over time.

  18. Cross-sectional research: A critical perspective, use cases, and

    3.1. Strengths: when to use cross-sectional data. The strengths of cross-sectional data help to explain their overuse in IS research. First, such studies can be conducted efficiently and inexpensively by distributing a survey to a convenient sample (e.g., the researcher's social network or students) (Compeau et al., 2012) or by using a crowdsourcing website (Lowry et al., 2016, Steelman et ...

  19. 13

    The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences - June 2023 ... Cross-sectional studies are a type of observational studies in which the researcher commonly assesses the exposure, outcome, and other variables (such as confounding variables) at the same time. ... Study design and choosing a ...

  20. Cross-Sectional Study

    Cross-sectional research studies are a type of descriptive research that provides information from groups. Because it is a snapshot of a moment in time, this type of research cannot be used to ...

  21. PDF Conceptualization of Cross-sectional Mixed Methods Studies in Health

    cross-sectional research, and different purposes and designs in mixed methods research. The Context of Cross-sectional Research The cross-sectional research is a research approach in which the researchers investigate the state of affairs in a population at a certain point in time (Bethlehem, 1999). Instead of using a

  22. What is a Cross-Sectional Study?

    A cross-sectional study is also known as a prevalence or transverse study. It's a tool that allows researchers to collect data across a pre-defined subset or sample population at a single point in time. The information is typically about many individuals with multiple variables, such as gender and age. Although researchers get to analyze ...

  23. A Descriptive Cross-Sectional Study: Practical and Feasible Design in

    The participants in a cross-sectional study are recruited based on the inclusion and exclusion criteria set for the study. Once the participants have been recruited for the study, the researcher follows the study to assess the exposure and the outcomes. The researcher can also study the association between these variables.

  24. Analytical Cross-Sectional Studies

    An analytical cross-sectional study is a type of quantitative, non-experimental research design. These studies seek to "gather data from a group of subjects at only one point in time" (Schmidt & Brown, 2019, p. 206). The purpose is to measure the association between an exposure and a disease, condition or outcome within a defined population.

  25. Protocol for the development of a reporting guideline for umbrella

    Introduction Observational studies are fraught with several biases including reverse causation and residual confounding. Overview of reviews of observational studies (ie, umbrella reviews) synthesise systematic reviews with or without meta-analyses of cross-sectional, case-control and cohort studies, and may also aid in the grading of the credibility of reported associations. The number of ...

  26. Cross Sectional Study: Definition, Methods and Examples

    A cross-sectional study is a research design used to gather data from a population or sample at a specific point in time. It aims to provide a snapshot of a particular phenomenon or explore the relationship between variables at a given moment. Unlike longitudinal studies that track individuals over time, cross-sectional studies focus on a single interval.

  27. Symptom clusters and their impacts on the quality of life of patients

    DESIGN Cross-sectional study. METHODS The survey contained the Memorial Symptom Assessment Scale (MSAS), Quality of Life Questionnaire-Lung Cancer 43 and a self-designed General Information Evaluation Form. Symptom clusters were identified using exploratory factor analysis (EFA) based on the symptom scores.

  28. Healthcare

    A cross-sectional study design was employed, and a convenience sampling method was used. The study participants were healthcare professionals, and a total of 427 valid surveys were collected. The partial least squares structural equation modeling (PLS-SEM) approach was deployed to test the relationship between the determinants of work design ...

  29. Cross‐sectional evaluation of a clinical decision support tool to

    This study aims to assess the effectiveness of a medication risk score (MRS)-driven clinical decision support system (CDSS) in identifying actionable MRPs and improving medication safety in the acute care discharge TOC setting. Methods. A cross-sectional analysis was conducted in a cohort of 481 subjects discharged from the acute care setting.

  30. Modern contraceptive utilization and associated factors among

    A mixed type community based cross-sectional study design was conducted among 605 women in Kena woreda, from September 1-30/2023 out of 628 sampled mothers. Multistage sampling technique was used to select study participant and data was collected using semi-structured pretested questionnaire and entered in to Epi data version 3.1 and then ...