Multivariate Behavioral Research最新文献

Growth curve modeling has been widely used in many disciplines to understand the trajectories of growth. Two popular forms utilized in the real-world analyses are the linear and quadratic growth curve models. These models operate on the assumption that measurements are conducted exactly at pre-set time or intervals. In essence, the reliability of these models is deeply tied to the punctuality and consistency of the data collection process. However, in real-world data collection, this assumption is often violated. Deviations from the ideal measurement schedule often emerge, resulting in measurement error in time and consequent biased responses. Our simulation findings indicate that such error can skew estimations, especially in quadratic GCM. To account for the measurement error in time, we introduce a Bayesian growth curve model to accommodate the error in the individual time values. We demonstrate the performance of the proposed approach through simulation studies. Furthermore, to illustrate its application in practice, we provide a real-data example, underscoring the practical benefits of the proposed model.

Latent class (LC) analysis is a model-based clustering approach for categorical data, with a wide range of applications in the social sciences and beyond. When the data have a hierarchical structure, the multilevel LC model can be used to account for higher-level dependencies between the units by means of a further categorical LC variable at the group level. The research interest of LC analysis typically lies in the relationship between the LCs and external covariates, or predictors. To estimate LC models with covariates, researchers can use the one-step approach, or the generally recommended stepwise estimators, which separate the estimation of the clustering model from the subsequent estimation of the regression model. The package multilevLCA has the most comprehensive set of model specifications and estimation approaches for this family of models in the open-source domain, estimating single- and multilevel LC models, with and without covariates, using the one-step and stepwise approaches.

When using ecological momentary assessment data (EMA), missing data is pervasive as participant attrition is a common issue. Thus, any EMA study must have a missing data plan. In this paper, we discuss missingness in time series analysis and the appropriate way to handle missing data when the data is modeled as an idiographic discrete time continuous measure state-space model. We found that Missing Completely at Random, Missing At Random, and Time-dependent Missing At Random data have less bias and variability than Autoregressive Time-dependent Missing At Random and Missing Not At Random. The Kalman filter excelled at handling missing data under most conditions. Contrary to the literature, we found that using a variety of methods, multiple imputations struggled to recover the parameters.

This study addresses the challenge of doubly-censoring effects in longitudinal data structures, particularly within latent growth curve models (LGCMs). Censoring can severely bias estimates and inferences, distorting the relationships between growth factors and covariates. To combat this issue, this study introduces the Generalized Tobit estimator (GBIT), an advancement of the conventional Tobit model, designed to handle mixed censoring effects in longitudinal data. The objectives of this study were threefold: (a) to develop GBIT for doubly-censored data, (b) to evaluate GBIT's performance in LGCMs under mixed censoring, and (c) to examine the impact of such censoring on covariate effects and outcomes within LGCMs. A Monte Carlo simulation was conducted to assess GBIT's effectiveness to handle doubly-censoring effects in the LGCM framework, demonstrating its ability to provide unbiased estimates even in the presence of significant censoring. Also, GBIT was applied for empirical data positing doubly-censoring effects, further supporting the use of GBIT, particularly in situations involving doubly-censored data.

Accounting for the complexity of psychological theories requires methods that can predict not only changes in the means of latent variables - such as personality factors, creativity, or intelligence - but also changes in their variances. Structural equation modeling (SEM) is the framework of choice for analyzing complex relationships among latent variables, but the modeling of latent variances as a function of other latent variables is a task that current methods only support to a limited extent. In this article, we develop a Bayesian framework for Gaussian distributional SEM, which broadens the scope of feasible models for latent heteroscedasticity. We use statistical simulation to validate our framework across four distinct model structures, in which we demonstrate that reliable statistical inferences can be achieved and that computation can be performed with sufficient efficiency for practical everyday use. We illustrate our framework's applicability in a real-world case study that addresses a substantive hypothesis from personality psychology.

There is a growing interest of researchers in meta-analytic methods for comparing variances as a means to answer questions on between-group differences in variability. When measurements are fallible, however, the variance of an outcome reflects both the variance of the true scores and the error variance. Consequently, effect sizes based on variances, such as the log variability ratio (lnVR) or the log coefficient of variation ratio (lnCVR), may thus not only reflect between-group differences in the true-score variances but also differences in measurement reliability. In this article, we derive formulas to correct the lnVR and lnCVR and their sampling variances for between-group differences in reliability and evaluate their performance in simulation studies. We find that when the goal is to meta-analyze differences between the true-score variances and reliability differs between groups, our proposed corrections lead to accurate estimates of effect sizes and sampling variances in single studies, accurate estimates of the average effect and the between-study variance in random-effects meta-analysis, and adequate type I error rates for the significance test of the average effect. We discuss how to deal with problems arising from missing or imprecise group-specific reliability estimates in meta-analytic data sets and identify questions for further methodological research.

In this work, we introduce a multiple-group longitudinal IRT model that accounts for skewed latent trait distributions. Our approach extends the model proposed by Santos et al. in 2022, which introduced a general class of longitudinal IRT models. The latent traits follow a multivariate skew-normal distribution, induced by an antedependence structure with centered skew-normal errors. Additionally, latent mean trajectories are modeled using quadratic curves, while structured covariance matrices capture within-participant dependencies. A three-parameter probit model is employed for dichotomous items. Bayesian parameter estimation and model fit assessment are conducted through a hybrid MCMC algorithm, combining the FFBS sampler with Metropolis-Hastings steps. The model's effectiveness is demonstrated through an application to real data from the Longitudinal Study of the 2005 School Generation in Brazil (GERES project), where it outperforms the normal model by better capturing asymmetry in latent traits. A simulation study further supports its robustness across various test conditions.

Neural networks like variational autoencoders have been proposed as a statistical tool to fit item factor models to data. Advantages are that high dimensional models can be estimated more efficiently as compared to conventional approaches. In this study, we demonstrate advantages of a specific autoencoder as a tool for amortized joint maximum likelihood estimation of item factor models. Contrary to contemporary joint maximum likelihood estimation and marginal maximum likelihood estimation, no additional parameter constraints are necessary to ensure standard asymptotic theory to apply. In a simulation study, the performance of the autoencoder is compared to constrained joint maximum likelihood and various forms of marginal maximum likelihood under different distributions for the factor scores. Results show that the amortized joint maximum likelihood estimates of the factors scores are overall less biased as compared to the other approaches. We illustrate the use of the autoencoder in two real data examples.

As the popularity of the experience-sampling methodology rises, there is a growing need for suitable analytical procedures. These studies often aim to separate fleeting situation-specific influences from more enduring ones. Latent state-trait (LST) models can make this differentiation. This tutorial discusses multiple-indicator wide-format LST models suitable for experience-sampling data. We outline second-order and first-order model specifications, their advantages and disadvantages, and make the assumptions of first-order specifications explicit. These LST models are very flexible, allowing for various different models and for testing invariance assumptions. However, their specification is tedious and error-prone. This tutorial introduces a new user-friendly browser app and R-function for experience sampling models in the R-package lsttheory. Extending on existing models, the software also allows to add covariates, which can further explain the stable components. Throughout the tutorial, we answer exemplary research questions about well-being in everyday life with data from a five-day experience-sampling study. An autoregressive model with indicator-specific traits was most appropriate for the data and revealed relatively high consistency, implying that well-being depends more strongly on the person than the current situation. Of the Big Five, extraversion, emotional stability and agreeableness are predictive of trait well-being. We conclude with recommendations about model fit and comparisons.

When studying effect heterogeneity between different subgroups (i.e., moderation), researchers are frequently interested in the mediation mechanisms underlying the heterogeneity, that is, the mediated moderation. For assessing mediated moderation, conventional methods typically require parametric models to define mediated moderation, which has limitations when parametric models may be misspecified and when causal interpretation is of interest. For causal interpretations about mediation, causal mediation analysis is increasingly popular but is underdeveloped for mediated moderation analysis. In this study, we extend the causal mediation literature, and we propose a novel method for mediated moderation analysis. Using the potential outcomes framework, we obtain two causal estimands that decompose the total moderation: (i) the mediated moderation attributable to a mediator and (ii) the remaining moderation unattributable to the mediator. We also develop a multiply robust estimation method for the mediated moderation analysis, which can incorporate machine learning methods in the inference of the causal estimands. We evaluate the proposed method through simulations. We illustrate the proposed mediated moderation analysis by assessing the mediation mechanism that underlies the gender difference in the effect of a preventive intervention on adolescent behavioral outcomes.