MULTIVARIATE BEHAVIORAL RESEARCH多元行为研究
MULTIVARIATE BEHAVIORAL RESEARCH(英文缩写 MULTIVAR BEHAV RES),ISSN 0027-3171,eISSN 1532-7906,中文译名:多元行为研究 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 3.085 | Q1 |
| 2022 | 3.800 | Q1 |
| 2023 | 5.300 | Q1 |
| 2024 | 3.500 | Q1 |
| 2025 | 3.500 | Q1 |
MULTIVARIATE BEHAVIORAL RESEARCH 最新收录文献
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1. When Nesting is Nuisance in Cluster Sampled Longitudinal Research.
1. 当嵌套成为聚类抽样纵向研究中的困扰时PMID:日期:2026-10-03Cluster sampling is an efficient means of recruiting and retaining an optimal sample size for a longitudinal study, but results in sample data nested at three levels (repeated measures [level 1] collected from participants [level 2] sampled from within cluster units [level 3]) when research questions often only involve the first two. Researchers have three options when analyzing cluster sampled longitudinal data to avoid inferential errors when the third level of nesting is nuisance: 1) model it, 2) control it, or 3) a hybrid combination of both. The purpose of this paper is twofold. First, a smaller-scale Monte Carlo simulation was conducted to determine if options to control for nuisance nesting can achieve comparable parameter estimate accuracy, confidence interval coverage, and statistical power levels obtainable when nuisance nesting is modeled. Second, we demonstrate the three methods to address nuisance nesting using data from a previously published multi-site longitudinal study. The and syntax scripts used to impute missingness and analyze the example data in keeping with a "nesting is nuisance" approach are included.
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2. Affect Dynamics or Response Bias? Extreme Response Style in Daily-Life Assessments.
PMID:日期:2026-09-11Intensive longitudinal data (ILD) are increasingly used to study affect dynamics such as intraindividual variability and inertia. These parameters are often interpreted as substantive individual differences, yet they may be biased by stable response style tendencies, in particiular extreme response style (ERS). In the present study, we investigated whether ERS is systematically associated with affect dynamics estimated from multiple experience sampling method (ESM) datasets (total ). Using a joint Bayesian IRTree-location-scale modeling approach, we estimated person-specific ERS alongside affective variability and inertia across multiple ESM datasets and synthesized study-level associations using a meta-analytic approach. We find that higher ERS is consistently associated with greater intraindividual variability. In contrast, we find no evidence that ERS was related to affective or emotional inertia. These results suggest that commonly used measures of intraindividual variability may partly reflect systematic response behavior rather than solely genuine affective fluctuations. Ignoring ERS may therefore bias variability estimates obtained from location-scale and related multilevel dynamic models. Overall, this study highlights the importance of accounting for response styles when modeling affect dynamics and interpreting person-specific variability parameters in ILD research.
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3. The Problem of Temporal Misalignment in Longitudinal Causal Research.
PMID:日期:2026-08-30A common goal of longitudinal research in psychology and related disciplines is the study of causal effects over time. An often overlooked assumption in this context is temporal alignment, referring to a match between the timescale at which a longitudinal process operates, the timescale at which data were collected, and the time frame in measurements. We present multiple scenarios, representing both the analysis of intensive longitudinal data and panel data, in which this assumption is violated due to time aggregation (i.e., aggregating scores over multiple occasions) and/or systematic undersampling (i.e., measurement intervals that are too long in relation to the longitudinal process). Our simulations demonstrate the large biases that can occur in estimates of lag-0 and lag-1 effects using various modeling approaches, even when there is no effect in the data. We discuss implications of these results and briefly consider future lines of methodological research to address this problem.
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4. Modeling Between-Group Differences in Dynamics of Psychological Constructs in the Presence of Measurement Non-Invariance: A Stepwise Approach.
PMID:日期:2026-08-26Researchers analyzing intensive longitudinal data often aim to detect between-group differences in dynamics of psychological constructs, such as positive and negative affect. These constructs are usually latent (not directly observable). To validly compare groups of individuals in their dynamic processes, the latent constructs must be measured equally across the individuals. If this is not the case - that is, if measurement invariance is violated for some items - this partial non-invariance must be accounted for. Otherwise, the group-specific estimates of the dynamic process may be biased. To aid researchers in obtaining unbiased group-specific estimates despite partial measurement non-invariance, we present an extension of the Three-Step Latent Vector Autoregression, a novel method that separates the estimation of the measurement model and the structural model. We expand this method to multi-group modeling, where differences in structural parameters (e.g., regression coefficients) are captured by allowing these parameters to vary across the groups of individuals. Measurement heterogeneity between and within these groups is captured by group- or person-specific MM parameters. Through two simulation studies, we demonstrate that the method performs well in obtaining correct estimates of a dynamic process despite the presence of measurement heterogeneity. The method is further illustrated with an empirical example.
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5. Exploratory Continuous-Time Modeling (Expct): Extracting Dynamic Features from Irregularly Spaced Time Series.
PMID:日期:2026-08-24When collecting intensive longitudinal data in the social, behavioral and biomedical sciences irregularly spaced data (i.e., data that are not collected at equal intervals) are the norm rather than the exception. However, most of the techniques and models that are used to analyze these data in practice were designed for equally spaced data. Although continuous time vector autoregressive models (CT-VAR) have been recently used to examine the relationship between variables across time differences, the current implementation of these models makes strong assumptions about the functional relationships between variables within the system. The current paper introduces the novel exploratory continuous-time models (), which allow for the continuous estimation of multivariate (or univariate) predictors with multivariate outcomes with regularly or irregularly-spaced data. As demonstrated in the Monte-Carlo simulation study, the models are able to efficiently model complex auto- and cross-regressive system dynamics without bias. The model: (1) addresses intensive data across virtually any time scheme, (2) allows for data collected at different time scales to predict themselves and one another, and (3) makes fewer assumptions about the functional forms of the longitudinal system dynamics between variables.
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6. Evaluating Bayesian Variable Selection Approaches for Nonlinear Random Effects Models.
6. 评估非线性随机效应模型的贝叶斯变量选择方法PMID:日期:2026-08-18Longitudinal data modeling attracts special interest from researchers and practitioners because of its capacity to help understand individual and mean trends of growth and development over time. One feature of longitudinal design is the collection of extensive information on participants to help explain the underlying growth process. However, selecting which covariates or independent variables (i.e., predictors) to include in a statistical model is challenging especially with the goals of avoiding both overfitting and underfitting. The present study demonstrates and compares multiple Bayesian variable selection methods for the selection of covariates to explain longitudinal growth processes, including both shrinkage methods (e.g., horseshoe) and stochastic variable search method (e.g., spike-and-slab priors and their extension to a Normal Mixture of Inverse Gammas), an extensive Monte Carlo simulation study using a piecewise random effects model with change point as an example. Our goal is to provide recommendations for researchers and practitioners about the advantages and limitations of Bayesian variable selection methods for variable selection in longitudinal studies, including model convergence and parameter estimation precision and accuracy.
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7. A Mechanistic Model of Symptom Dynamics: Implications for Statistical Network Analyses.
PMID:日期:2026-08-10Mental health issues, particularly depression, arise from complex symptom interactions that traditional psychiatric models often fail to capture. We address this issue by introducing a continuous-time mechanistic network model of depressive symptoms, inspired by empirical findings. The model captures bistability between healthy and depressed states, illustrates transitions and tipping points, and shows post-shock persistence. It also allows the study of resilience by simulating responses to external shocks. Importantly, we demonstrate how the same underlying dynamics can give rise to different statistical networks depending on the timing of observation, thereby showing how statistical networks estimated from simulated observations can differ in density across healthy, depressed, and shock phases, even when the underlying mechanistic couplings remain unchanged. Using synthetic data across resilience levels, we compare model-derived networks to those estimated from a large-scale dataset ( = 23,283, HELIUS study), showing strong agreement and revealing that in-degree in the mechanistic network maps onto statistical centrality. Our aim is not to propose a definitive model of depression. Rather, the model provides both a plausible representation of symptom dynamics and an illustrative framework for linking mechanistic processes to statistical symptom networks, thereby clarifying how dynamic mechanisms can underpin diverse empirical findings across individuals and populations.
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8. Romeb: An R Package for Robust Median-Based Bayesian Linear Growth Curve Modeling with Missing Data.
PMID:日期:2026-08-10Growth curve modeling (GCM) has been widely used in social and behavioral sciences to analyze longitudinal data. However, it remains a significant challenge for GCM to handle missing data in longitudinal research, especially when data are nonnormally distributed. Although the robust median-based Bayesian approach for GCM developed by Tong effectively addresses both ignorable and nonignorable missing data in various data scenarios, particularly for nonnormally distributed data, no dedicated software was previously available to implement this advanced method, posing a barrier for researchers without extensive statistical or programming backgrounds. This article introduces a newly developed R package, , which streamlines the application of the robust median-based Bayesian linear GCM approach. An empirical example is provided to demonstrate the functionality of , accompanied by multiple figures and diagnostic tests.
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9. Flexible Multiple Imputation of Missing Data in Time-Structured Longitudinal Designs.
9. 时间结构纵向设计中缺失数据的灵活多重插补PMID:日期:2026-08-06Missing data are common in longitudinal designs and are often addressed with multiple imputation (MI), either as single-level MI, which treats repeated measures as separate variables, or multilevel MI, which treats repeated measures as nested within participants. Previous research has shown that both approaches can perform well in time-structured designs but has largely focused on growth modeling applications, where the assumptions underlying multilevel MI were met. In the present article, we argue that single-level MI is a more flexible method for handling missing data in time-structured designs that requires fewer assumptions and can accommodate a wider range of analyses than multilevel MI. In this context, we also consider applications of single-level MI to longitudinal multiple-indicator designs, in which single-level MI can be extended with composite scores or dimension reduction techniques such as partial least squares (PLS) to accommodate the potentially large number of variables in these types of designs. Our results from two simulation studies suggest that single-level MI provides a flexible treatment of missing data and that PLS in particular can facilitate single-level MI in applications with many variables. We conclude by discussing implications for applied research and by illustrating the application of single-level MI in an empirical example.
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10. Comparing inference methods for causal mediation analysis with nominal mediators: A simulation and empirical study.
PMID:日期:2026-08-05In this study we advance causal mediation analysis for nominal mediators within a potential outcome framework. Through Monte Carlo simulations, we compared three inference methods for testing total natural indirect effects () and pure natural indirect effects (): non-parametric bootstrapping, parametric resampling, and Bayesian estimation. Results showed that nominal mediation models yielded accurate estimates across conditions, with both maximum likelihood and Bayesian approaches performing well. All three inference methods maintained acceptable Type I error control and achieved adequate statistical power in larger samples, with comparable performance across approaches. We provide an empirical illustration using survey data on healthcare payment types and mental health help-seeking behavior to illustrate the model's utility. Findings suggest that researchers can reliably estimate and test nominal mediation effects using any of the three inference approaches, providing a robust methodological framework for investigating causal pathways involving categorical mediating variables in social, behavioral, and health sciences.