Time and change: A meta-analysis of temporal decisions in longitudinal studies

Longitudinal research has grown in popularity in the field of management and organizations. However, the literature has neglected to consider the important ways in which researchers' temporal decisions can influence observed change in longitudinal studies. Researchers must make a set of temporal decisions to capture change, such as the temporal precision of the hypothesized form of change, the selection of a sample that is expected to exhibit the change, the choice of variables to be measured repeatedly, the frequency of measurements, and the time interval between measurements. However, these decisions typically are based on "educated guesses," which makes their effects on the observed change unclear. In this paper, we develop a conceptual framework to explain how temporal decisions influence observed change and validate it by meta-analyzing longitudinal studies (k = 268). Specifically, we found that observed change is affected by hypotheses (i.e., temporal precision), the sample (i.e., presence of a change trigger), variables (i.e., variable type and rating source), and measurement occasions (i.e., frequency and time interval). These findings offer insights into the importance of making informed temporal decisions. The implications of our findings are broad and applicable across research streams and theoretical traditions.