Power analysis for conditional indirect effects: A tutorial for conducting Monte Carlo simulations with categorical exogenous variables.

Conceptual and statistical models that include conditional indirect effects (i.e., so-called "moderated mediation" models) are increasingly popular in the behavioral sciences. Although there is ample guidance in the literature for how to specify and test such models, there is scant advice regarding how to best design studies for such purposes, and this especially includes techniques for sample size planning (i.e., "power analysis"). In this paper, we discuss challenges in sample size planning for moderated mediation models and offer a tutorial for conducting Monte Carlo simulations in the specific case where one has categorical exogenous variables. Such a scenario is commonly faced when one is considering testing conditional indirect effects in experimental research, wherein the (assumed) predictor and moderator variables are manipulated factors and the (assumed) mediator and outcome variables are observed/measured variables. To support this effort, we offer example data and reproducible R code that constitutes a "toolkit" to make up for limitations in other software and aid researchers in the design of research to test moderated mediation models.

Interrater reliability for multilevel data: A generalizability theory approach.

Current interrater reliability (IRR) coefficients ignore the nested structure of multilevel observational data, resulting in biased estimates of both subject- and cluster-level IRR. We used generalizability theory to provide a conceptualization and estimation method for IRR of continuous multilevel observational data. We explain how generalizability theory decomposes the variance of multilevel observational data into subject-, cluster-, and rater-related components, which can be estimated using Markov chain Monte Carlo (MCMC) estimation. We explain how IRR coefficients for each level can be derived from these variance components, and how they can be estimated as intraclass correlation coefficients (ICC). We assessed the quality of MCMC point and interval estimates with a simulation study, and showed that small numbers of raters were the main source of bias and inefficiency of the ICCs. In a follow-up simulation, we showed that a planned missing data design can diminish most estimation difficulties in these conditions, yielding a useful approach to estimating multilevel interrater reliability for most social and behavioral research. We illustrated the method using data on student-teacher relationships. All software code and data used for this article is available on the Open Science Framework: https://osf.io/bwk5t/. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Analytical power calculations for structural equation modeling: A tutorial and Shiny app.

Conducting a power analysis can be challenging for researchers who plan to analyze their data using structural equation models (SEMs), particularly when Monte Carlo methods are used to obtain power. In this tutorial, we explain how power calculations without Monte Carlo methods for the χ2 test and the RMSEA tests of (not-)close fit can be conducted using the Shiny app "power4SEM". power4SEM facilitates power calculations for SEM using two methods that are not computationally intensive and that focus on model fit instead of the statistical significance of (functions of) parameters. These are the method proposed by Satorra and Saris (Psychometrika 50(1), 83-90, 1985) for power calculations of the likelihood ratio test, and that described by MacCallum, Browne, and Sugawara (Psychol Methods 1(2) 130-149, 1996) for RMSEA-based power calculations. We illustrate the use of power4SEM with examples of power analyses for path models, factor models, and a latent growth model.

Self-Reports and Caregivers' Proxy Reports of Unmet Needs of Persons With Dementia: Implications for Both Partners' Health-Related Quality of Life.

OBJECTIVE: This study examined in a large sample of dementia caregiving dyads the associations between both partners' reports of unmet needs in persons with dementia (PwDs) and both partners' health-related quality of life (HRQOL). METHODS: This was a cross-sectional self-report survey of 521 community-dwelling dyads in a pragmatic trial in the Netherlands. The Camberwell Needs Assessment was used to measure PwDs' unmet needs. Both partners' self-reported their HRQOL using the EuroQol-5. RESULTS: Controlling for covariates, PwDs' self-reported greater unmet needs were significantly associated with PwDs' and caregivers' lower self-reported HRQOL (actor effect; b = -0.044, ß = -0.226, z = -3.588, p <0.001 and partner effect; b = -0.021, ß = -0.131, z = -2.154, p = 0.031). Caregivers' proxy reports were greater than PwDs' self-reported unmet needs (Δ=0.66,χ2(1)=55.881,p<.0001). CONCLUSION: Clinicians should use caution in relying on caregiver proxy reports of PwDs' needs and HQOL alone regarding healthcare decision making.