Cross-sectional designs examine different groups of individuals at one point in time, essentially representing a slice of development using a sample with predetermined age groups or cohorts. In the previous example, delinquency was assessed over a 15-year period; in a cross-sectional design delinquency would be measured at one time with groups of individuals of up to 15 different ages. In another study, Schaie, Willis, Jay, and Chipeur (1989) were able to examine, at one data point, cognitive abilities across
77 years of age using a cross-sectional design. Therefore, this design is generally quicker, less expensive, less likely to involve attrition, and samples are more representative when compared to longitudinal designs. Nevertheless, cross-sectional designs are not without flaws. This design is highly dependent on similarity between age groups, which is often difficult to achieve because of uncontrollable and extraneous variables. Consequently, changes attributed to age may be confounded with some other variable. Similar to longitudinal designs, cross-sectional studies are subject to cohort effects because individuals in the age cohorts are born in different time periods. Cross-sectional study data can be analyzed with some of the previously described statistical techniques: ANOVA and structural equation modeling. In addition, regression is often used to analyze cross-sectional data.
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