PowerLapim

An application to conduct power analysis for linear and quadratic longitudinal actor-partner interdependence models in intensive longitudinal dyadic designs

Datos Bibliográficos

ID2930701
AutoresGinette Lafit (0000-0002-8227-128X, Research Group of Quantitative Psychology and Individual Differences, KU Leuven, Leuven, Belgium, autor de correspondencia), Luc Sels (0000-0002-3485-9599, Ghent University), Janne K Adolf (Research Group of Quantitative Psychology and Individual Differences, KU Leuven, Leuven, Belgium), Janne Adolf (0000-0001-6064-9803, KU Leuven), Tom Loeys (0000-0003-4551-5502, Ghent University), Eva Ceulemans (0000-0002-7611-4683, Research Group of Quantitative Psychology and Individual Differences, KU Leuven, Leuven, Belgium)
Año2022
Volumen39
Número10
Páginas3085-3115
Fecha de publicación2022-10-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaJournal of Social and Personal Relationships (JOURNAL)
Identificadores de la revistaISSN: 0265-4075 • E-ISSN: 1460-3608
EditorialSAGE Publications Inc (PUBLISHER)
DOI10.1177/02654075221080128
OpenAlexW4220911841
IdiomaEN
Citas recibidas8
Referencias citadas45

The longitudinal actor-partner interdependence model (L-APIM) is used to study actor and partner effects, both linear and curvilinear, in dyadic intensive longitudinal data. A burning question is how to conduct power analyses for different L-APIM variants. In this paper, we introduce an accessible power analysis application, called PowerLAPIM, and provide a hands-on tutorial for conducting simulation-based power analyses for 32 L-APIM variants. With PowerLAPIM, we target the number of dyads needed, but not the number of repeated measurements for both partners (which is often fixed in longitudinal studies). PowerLAPIM allows to study moderation of 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. Building on existing study that can yield estimates and thus plausible values of relevant model parameters, we illustrate how to perform a power analysis for a future study. In this illustration, we also demonstrate how to run a sensitivity analysis, to assess the impact of uncertainty about the model parameters, and of changes in the number of repeated measurements

Autoregressive model · Categorical variable · Covariate · Dyad · Econometrics · Longitudinal data · Longitudinal study · Machine learning · Moderation · Multilevel model · Partner effects · Power (physics · Quadratic equation · Statistics · Attachment and Relationship Dynamics · Child and Adolescent Psychosocial and Emotional Development · Computer Science · Mathematics · Mental Health Research Topics · Psychology · Social Psychology