15 Cross-Sectional Study Examples

cross-sectional research examples advantages and disadvantages, explained below

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.

two circles representing heterogenous vs homogenous research with the heterogenous sample demonstrating diverse participants with various characteristics and the homogenous sample demonstrating participants with at least one similar characteristic
  • 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.

A visual representation of a cross-sectional group of people, demonstrating that the data is collected at a single point in time and you can compare groups within the sample

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.

a visual representation of a longitudinal study demonstrating that data is collected over time on one sample so researchers can 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

AdvantagesDisadvantages
1. Efficient and Inexpensive: Utilizes existing databases for efficient and inexpensive data collection.1. Cannot Infer Causality: It’s impossible to determine causality due to observational nature and single-point data collection.
2. Easily Identifies Risk Factors: Helps identify risk factors related to certain outcomes in fields like medicine or psychology.2. Reliance on Self-Report Measures: Relies on self-reported data, which may not always be accurate due to factors like social desirability bias or lack of self-awareness.
3. Can Compare Subgroups: Allows comparison of different segments within a large sample.3. Sampling Issues: There may be issues with sample’s characteristics, such as lack of heterogeneity or small size.
4. Lots of Data: Offers large datasets with numerous variables that can be analyzed for insights using advanced statistical procedures.4. Response Rates: There can be poor response rates, limiting the dataset and potentially affecting the results of the study.
5. Generalizability: Cross-sectional studies that are representative of larger populations can establish plausible generalizability, unlike qualitative observational methods. 5. Cannot Establish Causation: While a longitudinal study can establish clear correlations (e.g. “the men in the cohort had higher income than the women in the cohort”), they cannot explain what caused those correlations.

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

Conclusion

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.

References

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.

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

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

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