A cross-sectional study is a type of research study widely used in economic, social, health, and marketing research. A cross-sectional study provides a snapshot of the distribution of factors and outcomes in a population at a specified period of time. In this type of study the prevalence of specific factors and outcomes can be calculated for a given population (community, state) and levels of exposure to factors and outcome status can be compared. In contrast to other study designs, cross-sectional studies sample individuals not based on their outcome status or the presence of a particular risk factor; rather, the presence of factors and outcomes are determined simultaneously.
Cross-sectional studies are very useful from the policy and public health point of view because, for example, they can provide a picture of the burden of a particular disease in a population and measure the prevalence of risk factors, such as smoking, in the population. However, this type of study is limited in its ability to give rise to inferences about causality. Cross-sectional studies are also very useful in monitoring conditions in a population, such as the surveillance of specific diseases (e.g., diabetes) or important risk factors (e.g., obesity), or in monitoring socioeconomic characteristics of the population (e.g., unemployment).
Cross-sectional studies offer several advantages over other types of research design. Compared to longitudinal cohort studies, which are studies that follow individuals with and without a specific risk factor over time to observe the occurrence of outcomes, cross-sectional studies are cheaper and can be carried out faster. Cross-sectional studies also allow for examining multiple factors and multiple outcomes in one single study. Generally, cohort studies can evaluate only one risk factor at a time, and case-control studies, a type of study that selects participants based on their outcome status, can evaluate only one outcome at a time. Another strength of cross-sectional studies is that when they are based on a representative sample of the population, their results can be generalized to the overall population from which the sample came. Analyses of surveys using representative samples require special analytic techniques to account for the sampling probability—the probability of being selected as a participant in the study—since this type of survey may over-sample segments of the population (e.g., minority groups) to make sure they are adequately represented in the survey.
A major limitation of cross-sectional studies is called temporality bias. Since risk factors and outcomes are measured simultaneously, it is not possible to know whether the factor preceded the occurrence of the outcome, which is a criterion for determining causality. For example, in a cross-sectional study relating unemployment to heart disease, we cannot determine whether being unemployed contributed to the development of disease, or whether heart disease caused people to lose their jobs, perhaps by making them too sick to continue working. Another limitation that is particularly important in medical research is that in cross-sectional studies, diseases with longer duration are overrepresented because people who have the disease for a longer period are more likely to be included in the sample compared to people who quickly recovered or died from it. Thus, the association observed between risk factors and the disease may reflect survival rather than etiology. This limitation is often referred to as length bias or prevalent case bias.
Opinion polls are cross-sectional studies. They are commonly used in political research to determine voters’ preferences for candidates in an election, people’s perceptions of government policies, and the distribution of these preferences and perceptions by segments of the population such as gender, race/ethnicity, and age group. In market research, cross-sectional surveys are conducted to examine consumer preferences for certain products, shopping patterns, and the impact of advertisements. Social scientists use cross-sectional surveys to examine societal factors, such as the association of social capital, a measure of neighborhood cohesion, collective efficacy, and social trust with adolescent pregnancy and domestic violence. Such studies have found, for example, that neighborhoods with higher social capital have lower prevalence of teen pregnancy and family violence.
In the education field cross-sectional studies are used to examine factors associated with academic achievement. For example, Gwen Glew conducted a survey of elementary schools across the United Stated that reported a high prevalence of bullying and found that bully victims have low academic achievement and feel unsafe at school. Cross-sectional studies are widely used in medical research to identify risk factors associated with disease and explore associations among risk factors, as well as factors associated with health care. These studies have helped to identify the risk factors for heart disease (smoking, hypertension, cholesterol), determine the association of psychosocial factors (e.g., self-efficacy) and dietary behaviors or physical activity, and examine ethnic and gender inequalities in access to health care (e.g., access to cardiovascular procedures).
An example of the use of cross-sectional studies in surveillance is the National Health and Nutrition Examination Survey (NHANES). In this national survey a representative sample of the population living in the United States has a comprehensive examination and completes questionnaires about their lifestyle behaviors. Such a survey reports on access to health care, the prevalence of overweight and obesity, dietary patterns and physical activity levels across U.S. states, and other health-related factors. The NHANES is repeated periodically and allows researchers to compare trends in health behaviors and conditions over time. The Youth Risk Behavior Survey (YRBS) is another example of a national cross-sectional survey. The YRBS, a school-based survey of adolescents living in the United States, reports on the prevalence of lifestyle behaviors such as smoking, drug use, sexual behavior, fruit and vegetable intake, and physical activity levels.
SEE ALSO Causality; Demography; Demography, Social; Polls, Opinion; Public Health; Public Policy; Research, Longitudinal; Risk; Social Science; Survey; Surveys, Sample
Glew, Gwen M., Ming-Yu Fan, Wayne Katon, Frederick P. Rivara, and Mary A. Kernic. 2005. Bullying, Psychosocial Adjustment, and Academic Performance in Elementary School. Archives of Pediatrics and Adolescent Medicine 159: 1026-1031.
Rothman, Kenneth, and Sander Greenland. 1998. Modern Epidemiology. Philadelphia: Lippincott-Raven.
Way, Sandra, Brian K. Finch, and Deborah Cohen. 2006. Hispanic Concentration and the Conditional Influence of Collective Efficacy on Adolescent Childbearing. Archives of Pediatrics & Adolescent Medicine 160: 925-930.
Zolotor, Adam J., and Desmond K. Runyan. 2006. Social Capital, Family Violence, and Neglect. Pediatrics 117: 1124-1131.
Carmen R. Isasi
"Research, Cross-Sectional." International Encyclopedia of the Social Sciences. . Encyclopedia.com. (September 20, 2018). http://www.encyclopedia.com/social-sciences/applied-and-social-sciences-magazines/research-cross-sectional
"Research, Cross-Sectional." International Encyclopedia of the Social Sciences. . Retrieved September 20, 2018 from Encyclopedia.com: http://www.encyclopedia.com/social-sciences/applied-and-social-sciences-magazines/research-cross-sectional