Thinking Clearly About Age, Period, and Cohort Effects with Bounding Analyses

Psychological researchers are interested in how things change over time and routinely make claims about age effects (e.g., personality maturation), cohort effects (e.g., generational differences in narcissism), and sometimes period effects (e.g., secular trends in mental health). The age-period-cohort identification problem means that these claims are not possible based on the data alone: Any possible temporal pattern can be explained by an infinite number of combinations of age, period, and cohort effects. This concern holds regardless of the study design (it also applies to longitudinal designs covering multiple cohorts) and the number of observations available (it also applies if we observe the whole population). Researchers usually rely on statistical models that impose constraints to pick one specific combination of effects. Unfortunately, these constraints often remain opaque, resulting in a lack of scrutiny of the underlying assumptions. How can we reason more transparently and systematically about age, period, and cohort? Here, I summarize advances in our understanding of the precise nature of the identification problem, provide an overview of ways to move forward, and highlight one approach that is particularly transparent about assumptions: bounding analysis, a framework developed by sociologists Ethan Fosse and Christopher Winship. To illustrate this approach, I analyze how age, period, and cohort affect attitudes toward working mothers in the German General Social Survey.