Visontay et al. (2020) CTMA Recently, a study by Visontay, Mewton, Sunderland, and Slade (2020) published in Drug and Alcohol Dependence used cross-temporal meta-analysis (CTMA) to estimate changes in young adults’ harmful alcohol consumption between 1989 and 2015. Although compelling, the results of this study rest upon the application of CTMA, which has been long criticized for its reliance on a variety of tenuous assumptions (see Donnellan, Trzesniewski, & Robins, 2009; Trzesniewski, Donnellan, & Robins, 2008).
Talk about generations is everywhere and particularly so in organizational science and practice. Recognizing and exploring the ubiquity of generations is important, especially because evidence for their existence is, at best, scant.
Our recent paper on generations published in Public Policy & Aging Report has been featured in an OUP blog post.
When seeking information about the influence of generations, policy makers are often faced with more questions than answers. One reason for this is the nearly ubiquitous nature of generations. Generations have been used to explain every- thing from shifts in broadly defined social phenomena (e.g., antiwar movements; Dunham, 1998) to the demise of marmalade (Gough, 2018). Likewise, owing to the fact that the modern workplace offers increasing opportunities for interactions among (relatively) older and younger coworkers, generations and especially generational differences have been used to describe a number of work-related phenomena, processes, and policies (for reviews, see Costanza, Badger, Fraser, Severt, & Gade, 2012; Costanza & Finkelstein, 2015).
It is common to broadly group people of different ages into “generations” and to speak of distinctions between such groups in terms of “generational differences.” The problem with this practice, is that there exists no credible scientific evidence that (a) generations exist, (b) that people can be reliably classified into generational groups, and (c) that there are demonstrable differences between such groups.
With COVID-19 presenting as a global pandemic, we have noticed an emerging rhetoric concerning “the COVID- 19 Generation,” both anecdotally and across various media outlets.
The proper estimation of age, period, and cohort (APC) effects is a pervasive concern for thestudy of a variety of psychological and social phenomena, inside and outside of organizations.
The proper estimation of age, period, and cohort (APC) effects is a pervasive concern for the study of a variety of psychological and social phenomena, inside and outside of organizations. One analytic technique that has been used to estimate APC effects is cross-temporal meta- analysis (CTMA). While CTMA has some appealing qualities (e.g., ease of interpretability), it has also been criticized on theoretical and methodological grounds. Furthermore, CTMA makes strong assumptions about the nature and operation of cohort effects relative to age and period effects that have not been empirically tested. Accordingly, the goal of this paper was to explore CTMA, its history, and these assumptions. Using a Monte Carlo study, we demonstrate that in many cases, cohort effects are misestimated (i.e., systematically over- or underestimated) by CTMA. This work provides further evidence that APC effects pose intractable problems for research questions where APC effects are of interest.