Cross-sectional Research

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Cross-sectional Research

50th Class Reunion.

Age is often used as a subject variable in developmental psychology studies. Frequently, a cross‐sectional method is used when investigating the relationship between age and other variables of interest. The cross‐sectional method assesses several different groups at approximately the same time and then compares these groups. Typically, the groups are of different ages. For example, one study measured intellectual development in people ranging from 25 to 81 years of age (Schaie, 1994). Overall, the results showed that the average intelligence of each group increased to early or middle adulthood and then declined. For example, numeric ability increased from 25 to 46 years of age and then declined. Based on this finding, the reader might think that intelligence, or at least the ability to process numbers, might increase to about the age of 46 and then start to decrease. However, he or she cannot confidently conclude this because age is confounded with several other variables. To illustrate this, let us compare two age groups: 46‐year‐olds and 81‐year‐olds. These two groups differ in age, the variable of interest, but they also differ in a host of other ways that may also account for the difference in numerical ability. For example, the older age group may have less education, less experience with recent technology, more test‐taking anxiety because they have not taken a test for a longer period of time, and less motivation. Additionally, the measured intelligence of the older group may differ from the younger group because they are slower (not less intelligent), and the test may be biased against the older group (it might use language less familiar to the older group). Therefore, though the cross‐ sectional study may show an interesting difference across ages, neither the reader nor the researcher can be certain regarding the variable or combination of variables to which this difference could be attributed.

Advantages of cross-sectional designs

• Cross‐sectional research is relatively quick and less expensive to conduct because many different age groups can be assessed at once.
• The results of cross‐sectional studies are often interesting and suggest relationships between variables that can stimulate theories and experiments to help clarify the nature of these relationships.
• These studies can help provide support for, or disconfirm, theories developed in more artificial settings (such as in a laboratory).
• They allow us to study variables, like age, that cannot be manipulated experimentally.

Limitations of cross-sectional designs

• These designs are always confounded. Therefore, cause‐and‐effect relationships can never be determined.
• The possibility that the groups differ in multiple ways may artificially exaggerate the differences between groups.

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