Author:

Keywords:

Social Sciences, Psychology, Psychology, Multidisciplinary, power analysis, Monte Carlo simulation, intensive longitudinal designs, linear mixed-effects models, multilevel autoregressive models, open materials, OPTIMAL EXPERIMENTAL-DESIGNS, DAILY-LIFE STRESS, SAMPLE-SIZE, STATISTICAL POWER, EMOTIONAL REACTIVITY, DEPRESSIVE SYMPTOMS, TIME, ACCURACY, SIMULATION, PREDICTOR, C14/19/054#55213456, 5201 Applied and developmental psychology, 5204 Cognitive and computational psychology

Abstract:

In recent years the popularity of procedures to collect intensive longitudinal data, such as the Experience Sampling Method, has immensely increased. The data collected using such designs allow researchers to study the dynamics of psychological functioning, and how these dynamics differ across individuals. To this end, the data are often modeled with multilevel regression models. An important question that arises when designing intensive longitudinal studies is how to determine the number of participants needed to test specific hypotheses regarding the parameters of these models with sufficient power. Power calculations for intensive longitudinal studies are challenging, because of the hierarchical data structure in which repeated observations are nested within the individuals and because of the serial dependence that is typically present in this data. We, therefore, present a user-friendly application and step-by-step tutorial to perform simulation-based power analyses for a set of models that are popular in intensive longitudinal research. Since many studies use the same sampling protocol (i.e., a fixed number of at least approximately equidistant observations) within individuals, we assume this protocol fixed and focus on the number of participants. All included models explicitly account for the temporal dependencies in the data by assuming serially correlated errors or including autoregressive effects.