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• Cross-Sectional Study | Definition, Uses & Examples

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.

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.

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

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

Sometimes only cross-sectional data 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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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 (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 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.

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.

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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Cross-Sectional Study: Definition, Designs & Examples

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

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

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

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

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

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

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

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

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

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

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

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

Analytical Studies

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

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

Descriptive Studies

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

Simple and Inexpensive

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

Minimal room for error

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

Multiple variables and outcomes can be researched and compared at once

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

The data can be a starting point for future research

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

Limitations

Does not help determine cause and effect.

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

Report bias is probable

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

The timing of the snapshot is not always representative

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

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

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

Cross-Sectional vs. Longitudinal

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

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

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

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

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

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

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

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

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

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

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

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

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

Further Information

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

1. Are cross-sectional studies qualitative or quantitative?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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• Cross-Sectional Study | Definitions, Uses & Examples

Cross-Sectional Study | Definitions, Uses & Examples

Published on 5 May 2022 by Lauren Thomas .

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

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

Table of contents

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

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

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

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

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

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

Descriptive vs analytical studies

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

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

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

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

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

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

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

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

Disadvantages

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

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

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

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

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

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15 Cross-Sectional Study Examples

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

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

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

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

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

Cross-Sectional Studies: Definition and  Overview

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

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

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

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

Cross-Sectional vs. Longitudinal Research

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

1. Cross-Sectional Research

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

2. Longitudinal Research

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

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

Differences between Cross-Sectional and Longitudinal Research

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

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

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

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

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

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

Cross-Sectional Study Examples

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

Strengths and Weaknesses of Cross- Sectional Research

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Dave Cornell (PhD)

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

• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 25 Positive Punishment Examples
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Critical Appraisal Resources for Evidence-Based Nursing Practice

• Levels of Evidence
• Systematic Reviews
• Randomized Controlled Trials
• Quasi-Experimental Studies
• Case-Control Studies
• Cohort Studies
• Analytical Cross-Sectional Studies

What is an Analytical Cross-Sectional Study?

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

• Qualitative Research

E-Books for Terminology and Definitions

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. 

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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Common Errors In Proposal Writing: A Cross Sectional Study Among Post Graduate Medical Trainees At Peshawar

Affiliations.

• 1 Research Cell, Khyber College of Dentistry Peshawar, Orthodontics Department, Khyber College of Dentistry Peshawar, Pakistan.
• 2 KP Health Department, Swat, Pakistan.
• 3 KP Health Department, Dir Lower,Khyber College of Dentistry Peshawar, Pakistan.
• 4 KP Health Department, Nowshera, Oral Pathology Department, Khyber College of Dentistry Peshawar,Pakistan.
• PMID: 31933307

Background: Proposal writing before starting research study is the key component of the any research project and quality of the research depends upon how the proposal was designed and planned. Objectives of this study was to determine the frequency of most common errors in proposal writing by post graduate medical residents of College of physician and surgery of Pakistan (CPSP) at Peshawar.

Methods: A cross sectional study was carried at Khyber College of Dentistry (KCD) Peshawar from August 2017 to May 2018. We conducted the reviewed of Form "S" of 43 proposals through convenience sampling. Each Form S consists of 34 questions. All the questions were dichotomous which were presented in the form of frequency and percentages. Data were analysed by SPSS-22.

Results: Out of 43 proposal, the result shows that 53.5% (n=23) of the candidates have not explained the introduction in their own words while suitable statistical tests were not mentioned in more than half of the 67.4% (n=29) proposal. References were not written in Vancouver style 51.2% (n=22) as well as hypothesis was not applicable in 62.8% (n=27) of the studies. However only 39.5% (n=60.5) of the trainees phrased the hypothesis properly.

Conclusions: Majority of the candidates were unable to write the proposal according to the recommended guidelines. Application of the appropriate statistical measures was found as a challenge for the candidates. Similarly, objectives were not clearly defined in terms of SMART concept.

Keywords: CPSP; Medical education; Medical Errors; Proposal Medical Writing; Postgraduate medical Trainee.

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• Research Design*
• Sampling Studies
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Basic Understanding of Study Types and Formulating Research Question for a Clinical Trial

Dipankar de.

Department of Dermatology, Venereology and Leprology, Postgraduate Institute of Medical Education and Research, Chandigarh, India

Sanjay Singh

1 Department of Dermatology and Venereology, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India

Do we know everything about our specialty? Does our literature reflect everything we should know? Does current literature suggest what we should explore in our research considering the lacunae in present evidence? The answer to the first two questions is no while for the last question is definitely yes. Solving each question poses several other questions to solve. Clinical research largely follows step ladder pattern. Understanding lacunae in existing literature requires interest and expertise in the given field as well as thorough review of the relevant literature. It is indeed important to know ‘where the boundary between current knowledge and ignorance lies’.[ 1 ]

Types of Clinical Studies

Clinical research studies are broadly of two types: observational studies and experimental or interventional studies.

Observational Studies

In observational studies, as the name suggests, the observer only observes as such or with tools/relevant investigations and does not interfere with the natural course of the disease/clinical condition. Observational studies are cross-sectional, case control or cohort studies.

Case control studies tell us about association between an exposure and an outcome, while cohort studies assess causality. In case control studies, the outcome of interest has already occurred. Comparing cases (those who already have the outcome of interest) with controls (those who do not have the outcome of interest), researchers look into the past with the aim of finding out the association of exposure and the outcome. In the cohort studies, two groups of individuals, one exposed and one not exposed to a “suspected” exposure causing the outcome, are followed prospectively in time to know whether the exposure actually leads to the outcome. Cohort study may also be performed with a single cohort (i.e. without a control group), where a group of individuals sharing a common characteristic are followed up forward in time to know the outcome of interest.

In a cross-sectional study, study participants are seen only at a point of time and information of interest about them is recorded. Cross-sectional studies are of two types: descriptive and analytical. Descriptive cross-sectional studies are one dimensional and they are done to find prevalence of the disease. They do not attempt to establish association between two parameters in a given disease or condition. On the contrary, analytical cross-sectional studies one tends to assess whether one parameter is associated with the other. However, since time is not a factor in cross-sectional studies, the study being a snapshot in time, the direction of association, that is, one causing the other cannot be established in analytical cross-sectional studies.

Cross-sectional studies are the commonest observational studies performed in dermatological disease and have several advantages. They are easy and quick to perform and economical as study participants are not followed in time. Moreover, several factors can be assessed at a time. The main disadvantage is that a cross-sectional study does not establish causality. Despite this limitation, they are very important as such studies give an idea about associations and pave the way for other observational study types that look into the causality. These studies cannot measure incidence of disease and are not suitable for diseases with low prevalence. A majority of the case control studies reported in dermatology are actually analytical cross-sectional studies.

Sometimes good studies have been misclassified as far as the study type is concerned. For example, in studies designated as case control studies, the cases and controls were not traced back in time. Such a study design is an example of analytical cross-sectional study. Similarly, in studies designated as cohort studies, the study participants were not followed up in time. Another common misclassification is “prospective cross-sectional study.” These studies are actually prospectively carried out cross-sectional studies, where research protocol is first framed and then study participants are evaluated at a single point. For a prospective study, a given study participant has to be followed up in time.

Interventional Studies: Clinical Trials

Clinical trial is an interventional or experimental study, where the investigator intervenes with treatment or determines the exposure to identify how one treatment arm fares in comparison to the other, the intervention actually changes the progress of the disease. One may also see the effect of one treatment only (single arm trial, which could also be called a prospective cohort study). When the effects of two treatments are compared, one group of patients, called the experimental group, receives the experimental drug, and another group, called the control group, receives the placebo or an active comparator drug.

Research Question

As all clinical research endeavors to address a research question, framing the primary research question is of utmost importance as it influences the study design, sample size required and the resources that may be required. All study questions along with the primary research question should be developed at the beginning of the study. Primary research question should never be compromised because the study hypothesis and objectives are framed based on the primary question. Loading a single study with several questions is likely to increase the complexity of the study design and statistical analysis.

Hulley et al. [ 2 ] suggest the use of FINER criteria for development of good research question. FINER is the acronym for (F)easibility, (I)nteresting, (N)ovel, (E)thical and (R)elevant. Feasibility in terms of availability of adequate number of study subjects within limited study duration is the most important factor to consider in studies conducted in India as majority of the studies form a part of postgraduate thesis. Next important concern is availability of funds and technical expertise as funding for dermatological research is often limited. The study question should be interesting and novel. After all the efforts of conducting a research, it needs to be published for wider dissemination of information gathered. Relevance and correct methodology are most important aspects in a study. However, novelty and interest in the subject are important aspects considered by the reviewing editor for further review. Otherwise, it stands a high chance of getting nixed in the bud irrespective of the strength in the study design. The study should be ethically justified to stand the chance of getting approved by relevant ethics committee. The question should be relevant too. For example, studying keratinocyte or melanocyte tumors in Indian context may not be relevant to clinical practice due to their rare occurrence.

Though FINER criteria determine the overall importance of the study question, the formatting of a research question follows PICOT.[ 3 ] It is the acronym of (P)opulation or (P) atients of interest, (I)ntervention, (C)omparison, (O)utcome of interest and (T)ime required for the study that is sufficient to capture the outcome of interest. Defining study population or patients based on inclusion and exclusion criteria is important and it determines validity of the results of the study. Wide inclusion criteria improve the external validity and wider generalizability of the results of the study in clinical practice. Study that does not lead to knowledge generalizable to a larger population is not ethical as it puts study subjects to unnecessary study-related risk with no benefit to anyone. “Intervention” in “PICOT” is only relevant for interventional studies. Other elements in PICOT may be helpful in formulating a study question for observational studies.

The clinical question leads to development of the hypothesis of the study. When formally testing for statistical significance, the research hypothesis should be tested as null hypothesis. Null hypothesis means that there is no difference in outcome between two arms. If there is no significant statistical difference, the null hypothesis cannot be rejected. On the contrary, if there is significant difference, the null hypothesis is rejected in favor of alternative hypothesis which says that one treatment is superior to the other.

Let us try to develop a research question. Literature search suggests that topical corticosteroids remain the gold standard for management of oral erosive lichen planus. Several other drugs including cyclosporine, methotrexate, acitretin and so on have been tried and they are found to be effective. However, evidence for their effectiveness in comparison to topical corticosteroids (gold standard) is not robust.

Let us find whether acitretin is effective in oral lichen planus. Going by the FINER criteria, it should be Feasible (confirmation of diagnosis of oral lichen planus by histopathology, number of patients in given study period, relatively low-cost drugs, clinical assessment of improvement with treatment and treatment outcome), Interesting and Novel (it assesses the place of acitretin in challenging clinical problem in the form of oral lichen planus, no randomized clinical trial of acitretin in this indication is available and thus novel), Ethical (since acitretin is commonly used in other indications and also in lichen planus, in carefully selected patient population, ethical issue should not be a problem) and Relevant (relevant in day-to-day dermatology practice).

For this study, Population consists of patients with erosive oral lichen planus. However, acitretin must not be used in women of childbearing potential due to its long-term teratogenic effect. Therefore, effective patient population will be adult men or women of non-child bearing age group. This can be taken care in the study methodology (exclusion criteria). Intervention here is oral capsule acitretin 25 mg once a day or dose adjusted as per body weight. The comparator group is topical triamcinolone acetonide oral paste (0.1%). As there is no validated scoring system for oral lichen planus, one can use 90% (arbitrarily chosen) reduction in pain/discomfort using 10-point visual analogue scale as primary outcome measure. One may decide to study a patient over 4 months as this is likely to be sufficient for 90% reduction in pain score as determined from prior clinical experience. This represents time in PICOT format.

A drug which shows efficacy in longer time may not be clinically relevant. Investigator has to decide the time in which the effect of a drug is considered reasonable and clinically relevant. Suppose a drug shows efficacy, but takes a long time to do so, it may be considered clinically irrelevant. It is also likely that those who fail to respond by 4 months may drop out from study if they are continued on same treatment which has been ineffective, and this may be ethically unjustifiable too.

Let us now see what could be the research question and study hypothesis in this scenario. One way of putting the research question would be “Does acitretin 25 mg once a day reduce pain score by 90% in comparison to topical triamcinolone acetonide (0.1%) applied three times a day among adult male patients suffering from erosive oral lichen planus over a 4-month treatment duration?” The Null hypothesis (H0) for this study would be “acitretin 25 mg once a day is as effective as topical triamcinolone acetonide (0.1%) applied three times a day in reducing pain score by 90% in patients of erosive oral lichen planus over a 4-month treatment duration.” Alternative hypothesis (H1) would be that acitretin is more effective than triamcinolone or vice versa.

Once the study question is framed and hypothesis of the study is in place, the best methods to answer the study question by accepting or refuting the study hypothesis are devised. These will be dealt in the subsequent sections.

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Cross Sectional Study Research Proposal Examples

Type of paper: Research Proposal

Topic: Education , Development , Risk , Ethics , Study , Information , Sociology , Time

Published: 02/12/2020

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INTRODUCTION

Developmental research studies assess changes over an extended period of time. These studies take place over, months, years, decades, or even lifetimes. Considering the extended stretches of time there are certain inherent problems involved in developmental research studies. Chief among them is that subject may drop out, move away or even suffer a premature death during the course of the study. Other concerns include outside life events that may interrupt the focus of the subjects. There are three main Research Designs formulated in order to develop the highest possible quality data. These include Longitudinal Studies, Cross Sectional Studies and Cross Sequential Studies.

Evaluation of the Research Designs

Longitudinal Studies follow subjects, or groups of subjects and tract their development over terms of months and years. This allows the research to develop very high quality quantitative data and qualitative data but it does have potential drawbacks. There is the time factor, not just the time span, but also the amount of time the researchers must spend tracking and periodically studying their subjects. Additionally, this design model is most likely to be influenced by subject mortality and other risk factors. Cross Sectional Studies lessen the time and therefore, the potential risk factors that are encountered by researchers who utilize longitudinal studies. These studies evaluate different ages at the same time by evaluating similar groups at different ages rather than tracking one group over the course of time. These studies are valuable in a variety of ways, it takes far less time to conduct a cross sectional study than it does to conduct a longitudinal study or a cross sequential study. The groups are more likely to express their own personal opinion consistent with the greater social values extant at the time. Cross Sequential Studies are an amalgam of both longitudinal studies and cross sectional studies. These studies track different age groups over a shorter term of years than those of the longitudinal studies. By doing that the researcher maximizes the developmental value and can account for evolving environmental influences as well as having immediate research study data.

Potential Study Issues

Tools to conduct this research can include secondary participation, in person observations, and case studies and content analysis. . Due to the socially sensitive nature of the topic and the tendency for prevarication individuals tend to have had in previous studies, in person interviews are the most likely to reveal unspoken information as well as cold facts since the added benefit of researcher observation comes into play. In person interviews, along with secondary tools also provide data that is specific to the span of individuals at that time and is not subject to rapidly shifting social mores. Case studies and content analysis do not provide these advantages.

Our Learning Team determined that the most effective developmental research study format to evaluate the changes in sexual attitudes over time. Considering the extended stretches of time and the effects of peer, social, political and economic changes that can affect individuals it is the most effective method to collect a “snap shot” of a broad demographic of age, social positioning and values that would provide the needed quality of quantitative and qualitative data for our study.

BIBLIOGRAPHY

ALL Psych On Line. (2013). Research Methods - Research Designs. Retrieved 08 10, 2013, from ALL Psych On Line - The Virtual Psychology Classroom: http://allpsych.com/researchmethods/developmentalresearch.html Swan, W. A. (2008, 09 14). The Three Main Types of Data Collection Tools. Retrieved 08 03, 2013, from Scienceray: http://scienceray.com/technology/information/the-three-main-types-of-data-collection-tools/

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