Psychological methods最新文献

The fulfillment of measurement invariance/equivalence is considered a prerequisite for meaningfully proceeding with substantive cross-group comparisons. In the multiple-group confirmatory factor analysis approach, one model identification issue has unfortunately received little attention: the specification of a referent variable in the test of measurement invariance. A multiple-indicator multiple-cause (MIMIC) model with moderated effects (i.e., MIMIC-interaction modeling; Woods & Grimm, 2011) and a moderated nonlinear factor analysis (MNLFA; Bauer, 2017) model for detecting uniform and nonuniform measurement inequivalences in tandem were proposed to identify credible referent variables. The performance of two search strategies, constrained and free baseline models, and MIMIC-interaction and MNLFA methodologies were evaluated in a Monte Carlo simulation. Effects of different configurations of the number of inequivalent variables, type and magnitude of inequivalence, magnitude of group differences in factor means and variances, and sample size in combination with each search strategy were determined. Results showed that the constrained baseline model strategy generally outperformed the free baseline model strategy for identifying credible referent variables, functioning well when up to one-third of the observed variables were noninvariant. Moreover, MNLFA performed better than MIMIC-interaction modeling for the selection of referent variables across nearly all conditions investigated in the study. The superiority of MNLFA over MIMIC-interaction modeling was specifically evident in the models with relatively small samples, large between-group latent variance differences, or a combination of both. An empirical example was presented to demonstrate the applicability of MNLFA with the constrained baseline model strategy for referent variable selection. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Meta-analytic structural equation modeling (MASEM) allows a researcher to simultaneously examine multiple relations among variables by fitting a structural equation model to summary statistics from multiple studies. Consider, for example, a mediation model with a predictor (X), mediator (M), and outcome variable (Y). In such a model, X can be a dichotomous variable, allowing researchers to examine the direct and indirect effects of an intervention as in randomized controlled trials (RCTs). However, the natural choice of a meta-analysis of RCTs would involve standardized mean differences as effect sizes, whereas MASEM requires correlation matrices as input. This can be solved by converting standardized mean differences (Cohen's d or Hedges' g) to point-biserial correlations (r_pb). Possible conversion formulas vary across publications and conversion tools, and it is unclear which one is most appropriate for use in MASEM. The aim of this article is to describe and evaluate several conversions of standardized mean differences to point-biserial correlations in the context of RCTs. We investigate the impact of the usage of various conversions on MASEM parameter estimation using the R package metaSEM in a simulation study, varying the ratio of group sample sizes, number of primary studies, sample sizes, and missingness. The results show that a relatively unknown d-to-r_pb conversion generally performs best. However, this conversion formula is not implemented in the mainstream conversion tools. We developed a user-friendly web application entitled Effect Size Calculator and Converter (https://hdejonge.shinyapps.io/ESCACO) that converts the user's primary study statistics into an effect size suitable for use in MASEM. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

The statistical and pragmatic tension between explanation and prediction is well recognized in psychology. Yarkoni and Westfall (2017) suggested focusing more on predictions, which will ultimately produce better calibrated interpretations. Variable selection methods, such as regularization, are strongly recommended because it will help construct interpretable models while optimizing prediction accuracy. However, when the data contain a nonignorable proportion of missingness, variable selection and model building via penalized regression methods are not straightforward. What further complicates the analysis protocol is when the model performance is evaluated on both prediction accuracy and fairness, the latter is of increasing attention when the predictive outcome has societal implications. This study explored two methods for variable selection with incomplete data: the bootstrap imputation-stability selection (BI-SS) method and the stacked elastic net (SENET) method. Both methods work with multiply imputed data sets but in different ways. BI-SS implements variable selection separately on each imputed bootstrap data set and aggregates the results via stability selection, while SENET stacks all imputed data sets and fits a single pooled model. We thoroughly evaluated their performance using a suite of metrics (including area under the curve, F1 score, and fairness criteria) via three increasingly complex simulation studies. Results reveal that while BI-SS and SENET methods perform almost equally well in settings with generalized linear models, only BI-SS fares well with nested data design because of high computation demand in fitting the regularized generalized linear mixed effects models. Finally, we demonstrated both methods with an example using rich electronic health data. (PsycInfo Database Record (c) 2025 APA, all rights reserved).