Cross-sectional Versus Longitudinal Surveys
An important distinction among surveys involves cross-sectional versus longitudinal studies. Cross-sectional surveys are exemplified by public opinion polls conducted during election campaigns. These polls typically use random digit dialing telephone surveys of a sample of the voting age population, and their purpose is to gauge voting preferences. The findings can be used to see whether voting preferences vary across age groups. For example, approval ratings can be examined within age decades, and one might find that the older the age group, the more likely that conservative candidates were preferred over liberal ones.
Such age-group comparisons represent inter-individual (or between individual) differences associated with age at a single point in time. They can not be interpreted as intra-individual (i.e., aging or within individual) effects such that as people grow older they become more likely to support conservative candidates. That would reflect the life course fallacy in which cross-sectional age differences are attributed solely to the aging process (Riley). In fact, age-group comparisons involve aging and cohort effects. Cohort effects reflect the fact that older individuals have not simply aged more than the younger individuals, they also went through their formative years, as well as other life course stages, during different historical periods. Consider the case where the number of years of formal education is compared across age groups. The results likely will show that each successively older age group has achieved less education. Surely this does not reflect the aging process, because that would mean we lose years of education as we age. Rather, such results reflect cohort succession, or the process by which educational aspirations and opportunities have steadily increased with each new generation.
Longitudinal studies are necessary to avoid the life course fallacy. In longitudinal studies the same sample is followed over time. Typically this involves interviewing the same respondents every year or so. Using these data one can examine intra-individual effects. Longitudinal data would likely show that the cross-sectional age-group differences in educational attainment reflected cohort succession rather than the aging process. That is, we would see that as birth cohorts age, their educational attainment levels remain largely unchanged. The drawback to longitudinal studies lies in their opportunity and tracking costs. These include identifying and obtaining baseline data on an appropriate birth cohort, and then continuing to track those individuals over time.
Even with longitudinal data there may be another problem. If the sample of persons is restricted to the members of a single birth cohort, then the results would be subject to the cohort centrism fallacy. The cohort centrism fallacy is that just because we observe changes of a certain type in one birth cohort over time does not mean that similar changes will occur in other birth cohorts (Riley). No two birth cohorts experience the same life course stages during the same historical periods. Thus, some birth cohorts have their lives shaped by a remarkably unique set of experiences, such as ‘‘the greatest generation’’ (Brokaw).
The best way to avoid both the life course and cohort centrism fallacies is to have comparable longitudinal data on several successive cohorts. This can be done two ways (Campbell). One involves designing longitudinal studies to include samples from several birth cohorts, and to follow those cohorts over a prolonged period. A more pragmatic approach involves using available data from several different birth-cohort-specific longitudinal studies that are now available courtesy of the Inter-university Consortium for Political and Social Research (ICPSR; for a complete listing see their website at www.icpsr.umich.edu).