The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences Volume 1: Building a Program of Research
Book contents
- The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences
- Cambridge Handbooks in Psychology
- The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences
- Copyright page
- Dedication
- Contents
- Figures
- Tables
- Contributors
- Preface
- 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
- Index
- References
13 - Cross-Sectional Studies
from Part III - Data Collection
Published online by Cambridge University Press: 25 May 2023
Book contents
- The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences
- Cambridge Handbooks in Psychology
- The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences
- Copyright page
- Dedication
- Contents
- Figures
- Tables
- Contributors
- Preface
- 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
- Index
- References
Summary
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.
- Type
- Chapter
- Information
- The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral SciencesVolume 1: Building a Program of Research, pp. 269 - 291Publisher: Cambridge University PressPrint publication year: 2023
References
Further Reading
The following are sources that describe various aspects of cross-sectional studies.
Axelson, O., Fredriksson, M., & Ekberg, K. (1994). Use of the prevalence ratio v the prevalence odds ratio as a measure of risk in cross sectional studies. Occupational and Environmental Medicine, 51(8), 574. https://doi.org/10.1136/oem.51.8.574Google Scholar
Barros, A. J. & Hirakata, V. N. (2003). Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio. BMC Medical Research Methodology, 3, 21. https://doi.org/10.1186/1471-2288-3-21Google Scholar
Brumback, B. & Berg, A. (2008). On effect-measure modification: Relationships among changes in the relative risk, odds ratio, and risk difference. Statistics in Medicine, 27(18), 3453–3465. https://doi.org/10.1002/sim.3246Google Scholar
Campbell, D. & Fiske, D. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81–105.CrossRefGoogle ScholarPubMed
Colditz, G. A. (2010). Overview of the epidemiology methods and applications: strengths and limitations of observational study designs. Critical Reviews in Food Science and Nutrition, 50 Suppl 1, 10–12. https://doi.org/10.1080/10408398.2010.526838CrossRefGoogle Scholar
Freemantle, N., Marston, L., Walters, K., et al. (2013). Making inferences on treatment effects from real world data: propensity scores, confounding by indication, and other perils for the unwary in observational research. BMJ, 347, f6409. https://doi.org/10.1136/bmj.f6409CrossRefGoogle ScholarPubMed
Garger, J. (2020). A definition of single source bias in social science research. Available at: www.johngarger.com/blog/a-definition-of-single-source-bias-in-social-science-research.Google Scholar
Hennekens, C. H. & Buring, J. E. (1987). Epidemiology in Medicine. Lippincott Williams & Wilkins.Google Scholar
Hughes, K. (1995). Odds ratios in cross-sectional studies. International Journal of Epidemiology, 24(2), 463–464, 468. https://doi.org/10.1093/ije/24.2.463Google Scholar
Hulley, S, B., Cummings, S. R. Browner, W. S., et al. (2001). Designing Clinical Research, 2nd ed. Lippincot Williams & Wilkins.Google Scholar
Kleinbaum, D., Kupper, L., & Morgenstern, H. (1982). Epidemiologic Research. John Wiley & Sons.Google Scholar
Lee, J. (1994). Odds ratio or relative risk for cross-sectional data? International Journal of Epidemiology, 23(1), 201–203. https://doi.org/10.1093/ije/23.1.201Google Scholar
Martinez, B. A. F., Leotti, V. B., Silva, G. S. E., et al. (2017). Odds ratio or prevalence ratio? An overview of reported statistical methods and appropriateness of interpretations in cross-sectional studies with dichotomous outcomes in veterinary medicine. Frontiers in Veterinary Science, 4, 193. https://doi.org/10.3389/fvets.2017.00193Google Scholar
Mellis, C. M. (2020). How to choose your study design. Journal of Paediatrics and Child Health, 56(7), 1018–1022. https://doi.org/10.1111/jpc.14929Google Scholar
Mitchell, T. (1985). An evaluation of the validity of correlational research conducted in organizations. Academy of Management Review, 10(2), 192–205.Google Scholar
Moore, D., & McCabe, G. (2002). Introduction to the Practice of Statistics, 4th ed. WH Freeman and Company.Google Scholar
Mukaka, M. M. (2012). Statistics corner: A guide to appropriate use of correlation coefficient in medical research. Malawi Medical Journal, 24(3), 69–71. https://www.ncbi.nlm.nih.gov/pubmed/23638278Google Scholar
Pandis, N. (2014a). Cross-sectional studies. American Journal of Orthodontics and Dentofacial Orthopedics, 146(1), 127–129. https://doi.org/10.1016/j.ajodo.2014.05.005Google Scholar
Pandis, N. (2014b). Introduction to observational studies: part 2. American Journal of Orthodontics and Dentofacial Orthopedics, 145(2), 268–269. https://doi.org/10.1016/j.ajodo.2013.11.002Google Scholar
Pearce, N. (2004). Effect measures in prevalence studies. Environmental Health Perspectives, 112(10), 1047–1050. https://doi.org/10.1289/ehp.6927CrossRefGoogle ScholarPubMed
Polychronopoulou, A., & Pandis, N. (2014). Interpretation of observational studies. American Journal of Orthodontics and Dentofacial Orthopedics, 146(6), 815–817. https://doi.org/10.1016/j.ajodo.2014.10.004Google Scholar
Reichenheim, M. E. & Coutinho, E. S. (2010). Measures and models for causal inference in cross-sectional studies: arguments for the appropriateness of the prevalence odds ratio and related logistic regression. BMC Medical Research Methodology, 10, 66. https://doi.org/10.1186/1471-2288-10-66Google Scholar
Rothman, K. J. Greenland, S., & Lash, T. L. (2008). Modern Epidemiology, 3rd ed. Lippincott Williams & Wilkins.Google Scholar
Santos, C. A., Fiaccone, R. L., Oliveira, N. F., et al. (2008). Estimating adjusted prevalence ratio in clustered cross-sectional epidemiological data. BMC Medical Research Methodology, 8, 80. https://doi.org/10.1186/1471-2288-8-80Google Scholar
Sedgwick, P. (2014). Spearman’s rank correlation coefficient. BMJ, 349, g7327. https://doi.org/10.1136/bmj.g7327Google Scholar
Setia, M. S. (2016). Methodology series module 3: Cross-sectional studies. Indian Journal of Dermatology, 61(3), 261–264. https://doi.org/10.4103/0019-5154.182410Google Scholar
Sedgewick, P. (2018). Spearman’s rank correlation coefficient, (correction). BMJ, 362, k4131. https://doi.org/10.1136/bmj.k4131Google Scholar
Stromberg, U. (1994). Prevalence odds ratio v prevalence ratio. Occupational and Environmental Medicine, 51(2), 143–144. https://doi.org/10.1136/oem.51.2.143Google Scholar
Tamhane, A. R., Westfall, A. O., Burkholder, G. A., & Cutter, G. R. (2017). Prevalence odds ratio versus prevalence ratio: choice comes with consequences. Statistics in Medicine, 36(23), 3760. https://doi.org/10.1002/sim.7375Google Scholar
Thiese, M. S. (2014). Observational and interventional study design types; an overview. Biochemia Medica, 24(2), 199–210. https://doi.org/10.11613/BM.2014.022Google Scholar
Thompson, M. L., Myers, J. E., & Kriebel, D. (1998). Prevalence odds ratio or prevalence ratio in the analysis of cross sectional data: what is to be done? Occupational and Environmental Medicine, 55(4), 272–277. https://doi.org/10.1136/oem.55.4.272Google Scholar
Thorndike, E. (1920). A constant error in psychological ratings. Journal of Applied Psychology, 4(1), 25–29.Google Scholar
Traissac, P., Martin-Prevel, Y., Delpeuch, F., & Maire, B. (1999). Regression logistique vs autres modeles lineaires generalises pour l’estimation de rapports de prevalences.[Logistic regression vs other generalized linear models to estimate prevalence rate ratios.] La Revue d’épidémiologie et de santé publique 47(6), 593–604. https://www.ncbi.nlm.nih.gov/pubmed/10673593Google ScholarPubMed
Twisk, J. W. R.(2013). Applied Longitudinal Data Analysis for Epidemiology, 2nd ed. Cambridge University Press.Google Scholar
Zocchetti, C., Consonni, D., & Bertazzi, P. A. (1995). Estimation of prevalence rate ratios from cross-sectional data. International Journal of Epidemiology, 24(5), 1064–1067. https://doi.org/10.1093/ije/24.5.1064CrossRefGoogle ScholarPubMed
References
Alexander, L., Lopes, B., Ricchetti-Masterson, K., & Yeatts, K. (2014–15). Cross-sectional Studies. UNC CH Department of Epidemiology. Available at: https://sph.unc.edu/wp-content/uploads/sites/112/2015/07/nciph_ERIC8.pdf.Google Scholar
Favero, N. & Bullock, J. (2015). How (not) to solve the problem: An evaluation of scholarly responses to common source bias. Journal of Public Administration Research and Theory, 25(1), 285–308.Google Scholar
George, B. & Pandey, S. K. (2017). We know the yin – but where is the yang? Toward a balanced approach on common source bias in public administration scholarship. Review of Public Personnel Administration, 37(2), 245–270. https://doi.org/10.1177/0734371X17698189CrossRefGoogle Scholar
Gullon, P., Bilal, U., & Franco, M. (2014). Physical activity environment measurement and same source bias. Gaceta Sanitaria, 28(4), 344–345. https://doi.org/10.1016/j.gaceta.2013.12.011Google Scholar
Lee, J. & Chia, K. S. (1994). Use of the prevalence ratio v the prevalence odds ratio as a measure of risk in cross sectional studies. Occupational and Environmental Medicine, 51(12), 841. https://doi.org/10.1136/oem.51.12.841CrossRefGoogle ScholarPubMed
Lee, J., Tan, C. S., & Chia, K. S. (2009). A practical guide for multivariate analysis of dichotomous outcomes. Annals of the Academy of Medicine, Singapore, 38(8), 714–719. https://www.ncbi.nlm.nih.gov/pubmed/19736577Google Scholar
Rodriguez-Romo, G., Garrido-Munoz, M., Lucia, A., Mayorga, J. I., & Ruiz, J. R. (2013). Asociacion entre las caracteristicas del entorno de residencia y la actividad fisica.[Association between the characteristics of the neighborhood environment and physical activity.] Gaceta Sanitaria, 27(6), 487–493. https://doi.org/10.1016/j.gaceta.2013.01.006Google Scholar
Swinscow, T. (1997). Study design and choosing a statistical test. BMJ Publishing Group. Available at: www.bmj.com/about-bmj/resources-readers/publications/statistics-square-one/13-study-design-and-choosing-statisti.Google Scholar
Szklo, M. & Javier Nieto, F. (2004). Epidemiology: Beyond the Basics. Jones and Bartlett Learning.Google Scholar
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