PowerLAPIM: An Application to Conduct Power Analysis for Linear and Quadratic Longitudinal Actor-Partner Interdependence Models in Intensive Longitudinal Dyadic Designs

Abstract

The longitudinal actor-partner interdependence modeling framework (L-APIM) is often used to study actor and partner effects in dyadic intensive longitudinal data. To capture curvilinear actor and partner patterns, the L-APIM can be extended to include quadratic actor and partner effects. A burning question is how to conduct power analyses for different L-APIM variants. In this paper, we introduce a power analysis application, called PowerLAPIM, and provide a hands-on tutorial for conducting simulation-based power analyses for 32 L-APIM variants, many of which include quadratic effects. With PowerLAPIM, we target the number of dyads needed, but not the number of repeated measurements for both partners, because this is usually fixed in many longitudinal dyadic studies. PowerLAPIM allows studying moderation of the linear and quadratic actor and partner effects by incorporating time-varying covariates or a categorical dyad-level predictor to test group differences. We also provide the functionality to account for serial dependency in the outcome variable by including autoregressive effects. We illustrate how to perform a power analysis for a longitudinal dyadic study using PowerLAPIM based on data from 94 heterosexual couples for which both partners simultaneously reported on their feelings and experiences several times a day for one week.

A new sample size planning approach for the VAR(1) model: Predictive Accuracy Analysis

“…This is important and promising information since it implies that the recommended sample size based on PAA is less dependent on specific model parameter values and, hence, will be less impacted by uncertainty and miss-specification of the VAR(1) model in step 1 of the analysis. Indeed, in sample size planning research, how to obtain these parameter values is a crucial bottleneck and always comes with uncertainty (Lafit et al, 2021(Lafit et al, , 2023Lakens, 2022;Lane and Hennes, 2018). Going a step further, guidelines can be derived.…”

A primer on studying effects of relationship duration in dyadic research: Contrasting cross-sectional and longitudinal approaches

“…When researchers are interested in the description of change from a certain starting point, using a standardized entry point has been found fruitful, for example, studying newlyweds (e.g., Karney & Bradbury, 1995) or previously unacquainted persons (e.g., Kenny, 2020) to standardize the baseline of relationship duration. As with cross-sectional designs, aiming for high power is recommended and tools for dyadic power analysis can be used to derive sample size estimates when planning studies (Lafit et al, 2021;Lane & Hennes, 2018). However, known issues of longitudinal designs also exist in dyadic research, including selection effects and selective and natural dropout (e.g., Baltes, 1968;Gistelinck & Loeys, 2019;Heck & Thomas, 2009;Maxwell & Cole, 2007).…”