PowerLapim
An application to conduct power analysis for linear and quadratic longitudinal actor-partner interdependence models in intensive longitudinal dyadic designs
Bibliographic Data
| ID | 2930701 |
|---|---|
| Authors | Ginette Lafit (0000-0002-8227-128X, Research Group of Quantitative Psychology and Individual Differences, KU Leuven, Leuven, Belgium, corresponding author), 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) |
| Year | 2022 |
| Volume | 39 |
| Issue | 10 |
| Pages | 3085-3115 |
| Publication date | 2022-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Social and Personal Relationships (JOURNAL) |
| Journal identifiers | ISSN: 0265-4075 • E-ISSN: 1460-3608 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/02654075221080128 |
| OpenAlex | W4220911841 |
| Language | EN |
| Citations received | 8 |
| References cited | 45 |
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
Beyond snapshots
Shedding some light on the relationship between measurement error and statistical power in multilevel models applied to intensive longitudinal designs
Finding the Optimal Number of Persons ( N ) and Time Points ( T ) for Maximal Power in Dynamic Longitudinal Models Given a Fixed Budget
Half Empty and Half Full? Biased Perceptions of Compassionate Love and Effects of Dyadic Complementarity
Beyond the Individual
A Renewal of Dyadic Structural Equation Modeling With Latent Variables
PowerLapim
Introduction to the special issue
Power Analysis for Parameter Estimation in Structural Equation Modeling
Structural equation modeling with interchangeable dyads.
Beyond Power Calculations
Estimating power in (generalized) linear mixed models
Testing Similarity Effects with Dyadic Response Surface Analysis
Quantifying explained variance in multilevel models
Statistical power in two-level models
Centering predictor variables in cross-sectional multilevel models
Simr
Hierarchical linear models
Sexual Frequency Predicts Greater Well-Being, But More is Not Always Better
On Standardizing Within-Person Effects
The Importance of Temporal Design
Moderation in the actor–partner interdependence model
All's well that ends well? A test of the peak‐end rule in couples’ conflict discussions
When power analyses based on pilot data are biased
The occurrence and correlates of emotional interdependence in romantic relationships
Attachment anxiety and the curvilinear effects of expressive suppression on individuals’ and partners’ outcomes
Longitudinal actor, partner, and similarity effects of personality on well-being
All or nothing
Power struggles
PowerLapim
| Unique citing works | 8 |
|---|---|
| Citations per year | 2 |
| Citation span | 2022 - 2026 (5) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 8 |