We provide conceptual introductions to missingness mechanisms—missing completely at random, missing at random, and missing not at random—and state-of-the-art methods of handling missing data—full-information maximum likelihood and multiple imputation—followed by a discussion of planned missing designs: Multiform questionnaire protocols, 2-method measurement models, and wave-missing longitudinal designs. We reviewed 80 articles of empirical studies published in the 2012 issues of the Journal of Pediatric Psychology to present a picture of how adequately missing data are currently handled in this field. To illustrate the benefits of using multiple imputation or full-information maximum likelihood and incorporating planned missingness into study designs, we provide example analyses of empirical data gathered using a 3-form planned missing design.

In a frequentist framework, the exact fit of a structural equation model (SEM) is typically evaluated with the chi-square test and at least one index of approximate fit. Current Bayesian SEM (BSEM) software provides one measure of overall fit: the posterior predictive p value (PPP χ2 ). Because of the noted limitations of PPP χ2 , common practice for evaluating Bayesian model fit instead focuses on model comparison, using information criteria or Bayes factors. Fit indices developed under maximum-likelihood estimation have not been incorporated into software for BSEM. We propose adapting 7 chi-square-based approximate fit indices for BSEM, using a Bayesian analog of the chi-square model-fit statistic. Simulation results show that the sampling distributions of the posterior means of these fit indices are similar to their frequentist counterparts across sample sizes, model types, and levels of misspecification when BSEMs are estimated with noninformative priors. The proposed fit indices therefore allow overall model-fit evaluation using familiar metrics of the original indices, with an accompanying interval to quantify their uncertainty. Illustrative examples with real data raise some important issues about the proposed fit indices' application to models specified with informative priors, when Bayesian and frequentist estimation methods might not yield similar results. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Several intraclass correlation coefficients (ICCs) are available to assess the interrater reliability (IRR) of observational measurements. Selecting an ICC is complicated, and existing guidelines have three major limitations. First, they do not discuss incomplete designs, in which raters partially vary across subjects. Second, they provide no coherent perspective on the error variance in an ICC, clouding the choice between the available coefficients. Third, the distinction between fixed or random raters is often misunderstood. Based on generalizability theory (GT), we provide updated guidelines on selecting an ICC for IRR, which are applicable to both complete and incomplete observational designs. We challenge conventional wisdom about ICCs for IRR by claiming that raters should seldom (if ever) be considered fixed. Also, we clarify how to interpret ICCs in the case of unbalanced and incomplete designs. We explain four choices a researcher needs to make when selecting an ICC for IRR, and guide researchers through these choices by means of a flowchart, which we apply to three empirical examples from clinical and developmental domains. In the Discussion, we provide guidance in reporting, interpreting, and estimating ICCs, and propose future directions for research into the ICCs for IRR. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Examining interactions among predictors is an important part of a developing research program. Estimating interactions using latent variables provides additional power to detect effects over testing interactions in regression. However, when predictors are modeled as latent variables, estimating and testing interactions requires additional steps beyond the models used for regression. We review methods of estimating and testing latent variable interactions with a focus on product indicator methods. Product indicator methods of examining latent interactions provide an accurate method to estimate and test latent interactions and can be implemented in any latent variable modeling software package. Significant latent interactions require additional steps (plotting and probing) to interpret interaction effects. We demonstrate how these methods can be easily implemented using functions in the semTools package with models fit using the lavaan package in R, and we illustrate how these methods work using an applied example concerning teacher stress and testing.