Multivariate Behavioral Research最新文献

Standardized Estimates of Second-Order Latent Growth Models: A Comparison of Alternative Latent-Standardization Methods. 二阶潜在增长模型的标准化估计：潜在标准化方法的比较。

IF 3.5 3区心理学

Multivariate Behavioral Research Pub Date : 2025-10-06 DOI: 10.1080/00273171.2025.2543240

Yifan Wang, Zhonglin Wen, Kit-Tai Hau, Tonglin Jin

{"title":"Standardized Estimates of Second-Order Latent Growth Models: A Comparison of Alternative Latent-Standardization Methods.","authors":"Yifan Wang, Zhonglin Wen, Kit-Tai Hau, Tonglin Jin","doi":"10.1080/00273171.2025.2543240","DOIUrl":"10.1080/00273171.2025.2543240","url":null,"abstract":"Second-order latent growth models (LGMs) have garnered considerable attention and are increasingly utilized in longitudinal data analyses of latent constructs comprised of multiple items. The growth parameter estimates in these models are intrinsically linked to the model identification methods. Latent-standardization (identification) methods, in which the latent variable is standardized at a reference time point (e.g., eta-1), yield theoretically unique and interpretable growth parameters. Traditional latent-standardization methods indirectly standardize eta-1 via the first-order component of the second-order LGM by constraining item intercepts and/or loadings. Such methods require a two-step modeling procedure and do not truly standardize eta-1. This article proposes a 1-stage method that indirectly standardizes eta-1 through the second-order component of the model by constraining the mean and variance of the level factor. This new single-step modeling method ensures eta-1 is truly standardized, with a mean of 0 and a variance of 1. Theoretical, simulated, and empirical comparisons are conducted across different latent-standardization methods, demonstrating the target accuracy and implementation simplicity of the proposed 1-stage method.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-18"},"PeriodicalIF":3.5,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analyzing Count Data in Single Case Experimental Designs with Generalized Linear Mixed Models: Does Serial Dependency Matter? 用广义线性混合模型分析单例实验设计中的计数数据：序列依赖性重要吗？

IF 3.5 3区心理学

Multivariate Behavioral Research Pub Date : 2025-10-01 DOI: 10.1080/00273171.2025.2561945

Haoran Li, Wen Luo

{"title":"Analyzing Count Data in Single Case Experimental Designs with Generalized Linear Mixed Models: Does Serial Dependency Matter?","authors":"Haoran Li, Wen Luo","doi":"10.1080/00273171.2025.2561945","DOIUrl":"https://doi.org/10.1080/00273171.2025.2561945","url":null,"abstract":"Single-case experimental designs (SCEDs) involve repeated measurements of a small number of cases under different experimental conditions, offering valuable insights into treatment effects. However, challenges arise in the analysis of SCEDs when autocorrelation is present in the data. Recently, generalized linear mixed models (GLMMs) have emerged as a promising statistical approach for SCEDs with count outcomes. While prior research has demonstrated the effectiveness of GLMMs, these studies have typically assumed error independence, an assumption that may be violated in SCEDs due to serial dependency. This study aims to evaluate two possible solutions for autocorrelated SCED count data: 1) to assess the robustness of previously introduced GLMMs such as Poisson, negative binomial, and observation-level random effects models under various levels of autocorrelation, and 2) to evaluate the performance of a new GLMM and a linear mixed model (LMM), both of which incorporate an autoregressive error structure. Through a Monte Carlo simulation study, we have examined bias, coverage rates, and Type I error rates of treatment effect estimators, providing recommendations for handling autocorrelation in the analysis of SCED count data. A demonstration with real SCED count data is provided. The implications, limitations, and future research directions are also discussed.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-25"},"PeriodicalIF":3.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145208454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic Fit Index Cutoffs for Time Series Network Models. 时间序列网络模型的动态拟合指标截止。

IF 3.5 3区心理学

Multivariate Behavioral Research Pub Date : 2025-10-01 DOI: 10.1080/00273171.2025.2561943

Siwei Liu, Christopher M Crawford, Zachary F Fisher, Kathleen M Gates

{"title":"Dynamic Fit Index Cutoffs for Time Series Network Models.","authors":"Siwei Liu, Christopher M Crawford, Zachary F Fisher, Kathleen M Gates","doi":"10.1080/00273171.2025.2561943","DOIUrl":"https://doi.org/10.1080/00273171.2025.2561943","url":null,"abstract":"In this study, we extend the dynamic fit index (DFI) developed by McNeish and Wolf to the context of time series analysis. DFI is a simulation-based method for deriving fit index cutoff values tailored to the specific model and data characteristics. Through simulations, we show that DFI cutoffs for detecting an omitted path in time series network models tend to be closer to exact fit than the popular benchmark values developed by Hu and Bentler. Moreover, cutoff values vary by number of variables, network density, number of time points, and form of misspecification. Notably, using 10% as the upper limit of Type I and Type II error rates, the original DFI approach fails to identify cutoffs for detecting an omitted path when effect size and/or sample size is small. To address this problem, we propose two alternatives that allow for the derivation of cutoffs using more lenient criteria. DFIA extends the original DFI approach by removing the upper limit of Type I and Type II error rates, whereas DFIB aims at maximizing classification quality measured by the Matthews correlation coefficient. We demonstrate the utility of these approaches using simulation and empirical data and discuss their implications in practice.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-22"},"PeriodicalIF":3.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145208475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the Ratio Between Point-Polyserial and Polyserial Correlations for Non-Normal Bivariate Distributions. 非正态二元分布的点-多序列与多序列相关性的比值。

IF 3.5 3区心理学

Multivariate Behavioral Research Pub Date : 2025-09-30 DOI: 10.1080/00273171.2025.2561947

Alessandro Barbiero

{"title":"On the Ratio Between Point-Polyserial and Polyserial Correlations for Non-Normal Bivariate Distributions.","authors":"Alessandro Barbiero","doi":"10.1080/00273171.2025.2561947","DOIUrl":"https://doi.org/10.1080/00273171.2025.2561947","url":null,"abstract":"It is a well-known fact that for the bivariate normal distribution the ratio between the point-polyserial correlation (the linear correlation after one of the two variables is discretized into k categories with probabilities <math><mrow><msub><mrow><mi>p</mi></mrow><mi>i</mi></msub></mrow><mtext>,</mtext></math> <math><mrow><mi>i</mi><mo>=</mo><mn>1</mn><mo>,</mo><mo>…</mo><mo>,</mo><mi>k</mi></mrow></math>) and the polyserial correlation <math><mrow><mi>ρ</mi></mrow></math> (the linear correlation between the two normal components) remains constant with <math><mrow><mi>ρ</mi></mrow><mtext>,</mtext></math> keeping the <math><mrow><msub><mrow><mi>p</mi></mrow><mi>i</mi></msub></mrow></math>'s fixed. If we move away from the bivariate normal distribution, by considering non-normal margins and/or non-normal dependence structures, then the constancy of this ratio may get lost. In this work, the magnitude of the departure from the constancy condition is assessed for several combinations of margins (normal, uniform, exponential, Weibull) and copulas (Gauss, Frank, Gumbel, Clayton), also varying the distribution of the discretized variable. The results indicate that for many settings we are far from the condition of constancy, especially when highly asymmetrical marginal distributions are combined with copulas that allow for tail-dependence. In such cases, the linear correlation may even increase instead of decreasing, contrary to the usual expectation. This implies that most existing simulation techniques or statistical models for mixed-type data, which assume a linear relationship between point-polyserial and polyserial correlations, should be used very prudently and possibly reappraised.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-17"},"PeriodicalIF":3.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145202157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Impact of Temporal Expectation on Unconscious Inhibitory Processing: A Computational Analysis Using Hierarchical Drift Diffusion Modeling. 时间期望对无意识抑制加工的影响：使用分层漂移扩散模型的计算分析。

IF 3.5 3区心理学

Multivariate Behavioral Research Pub Date : 2025-09-23 DOI: 10.1080/00273171.2025.2561944

Yongchun Wang, Jinlan Cao, Wandong Chen, Zhengqi Tang, Tingyi Liu, Zhen Mu, Peng Liu, Yonghui Wang

{"title":"The Impact of Temporal Expectation on Unconscious Inhibitory Processing: A Computational Analysis Using Hierarchical Drift Diffusion Modeling.","authors":"Yongchun Wang, Jinlan Cao, Wandong Chen, Zhengqi Tang, Tingyi Liu, Zhen Mu, Peng Liu, Yonghui Wang","doi":"10.1080/00273171.2025.2561944","DOIUrl":"https://doi.org/10.1080/00273171.2025.2561944","url":null,"abstract":"Numerous studies have shown that motor inhibition can be triggered automatically when the cognitive system encounters interfering stimuli, even a suspicious stimulus in the absence of perceptual awareness (e.g., the negative compatibility effect). This study investigated the effect of temporal expectation, a top-down active preparation for future events, on unconscious inhibitory processing both in the local expectation context on a trial-by-trial basis (Experiment 1) and in the global expectation context on a block-wise basis (Experiment 2). Modeling of the behavioral data using a drift-diffusion model showed that temporal expectation can accelerate the evidence accumulation and improve response caution, regardless of context. Importantly, the acceleration is lower when the target is consistent with the suspicious response tendency induced by the subliminal prime than when the target is inconsistent with that, which is significantly correlated with the behavioral RTs (i.e., the compatibility effect). The results provide evidence for a framework in which temporal expectation enhances inhibitory control of unconscious processes. The mechanism is likely to be that temporal expectation enhances the activations afforded by subliminal stimuli and the strength of cognitive monitoring, so that the cognitive system suppresses these suspicious activations more strongly, preventing them from escaping and interfering with subsequent processing.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-20"},"PeriodicalIF":3.5,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145126559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Residual Structural Equation Modeling with Nonnormal Distribution. 非正态分布残差结构方程建模。

IF 3.5 3区心理学

Multivariate Behavioral Research Pub Date : 2025-09-18 DOI: 10.1080/00273171.2025.2545371

Ming-Chi Tseng

引用次数: 0

Regularized Cross-Sectional Network Modeling with Missing Data: A Comparison of Methods. 缺失数据的正则化截面网络建模：方法的比较。

IF 3.5 3区心理学

Multivariate Behavioral Research Pub Date : 2025-09-17 DOI: 10.1080/00273171.2025.2551373

Carl F Falk, Joshua Starr

{"title":"Regularized Cross-Sectional Network Modeling with Missing Data: A Comparison of Methods.","authors":"Carl F Falk, Joshua Starr","doi":"10.1080/00273171.2025.2551373","DOIUrl":"https://doi.org/10.1080/00273171.2025.2551373","url":null,"abstract":"Many applications of network modeling involve cross-sectional data of psychological variables (e.g., symptoms for psychological disorders), and analyses are often conducted using a regularized Gaussian graphical model (GGM) employing a lasso, also known as the graphical lasso or glasso. Appropriate methodology for handling missing data is underdeveloped while using glasso, precluding the use of planned missing data designs to reduce participant fatigue. In this research, we compare three approaches to handling missing data with glasso. The first resembles a two-stage estimation approach-borrowed from the covariance structure modeling literature-whereby a saturated covariance matrix among the items is estimated prior to using glasso. The second and third approaches use glasso and the expectation-maximization (EM) algorithm in a single stage and either use EBIC or cross-validation for tuning parameter selection. We compared these approaches in a simulation study with a variety of sample sizes, proportions of missing data, and network saturation. An example with data from the Patient Reported Outcomes Measurement Information System is also provided. The EM algorithm with cross-validation performed best, but all methods appeared to be viable strategies under larger samples and with less missing data.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-19"},"PeriodicalIF":3.5,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145076528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamics Between Asynchronously Measured Variables: A Multilevel Approach to Momentary Affect and Morning Sleep Reports. 动态之间的异步测量变量：一个多层次的方法瞬间影响和早晨睡眠报告。

IF 3.5 3区心理学

Multivariate Behavioral Research Pub Date : 2025-09-15 DOI: 10.1080/00273171.2025.2551370

Sophie W Berkhout, Noémi K Schuurman, Koen Niemeijer, Peter Kuppens, Ellen L Hamaker

{"title":"Dynamics Between Asynchronously Measured Variables: A Multilevel Approach to Momentary Affect and Morning Sleep Reports.","authors":"Sophie W Berkhout, Noémi K Schuurman, Koen Niemeijer, Peter Kuppens, Ellen L Hamaker","doi":"10.1080/00273171.2025.2551370","DOIUrl":"https://doi.org/10.1080/00273171.2025.2551370","url":null,"abstract":"The reciprocal relations between sleep and affect have been a common focus in psychological research. Researchers studying affective processes often collect data multiple times a day over several days. Subjective sleep quality, on the other hand, is generally measured once at the beginning of the day. This difference in measurement frequency creates a challenge when analyzing these data, because standard dynamic models are not equipped for this. Furthermore, many of the popular approaches are based on the assumption of stationarity, meaning that processes are assumed to continue throughout the night in the same way as throughout the day. In this paper, we introduce a dynamic structural equation model that incorporates reciprocal relations between momentary affect and daily measures of sleep, tackling both of these challenges and also incorporating individual differences in these relations. To demonstrate the practical applicability of this model, we make use of an empirical example of positive and negative affect. Furthermore, we aim to give researchers the means to adapt or build on this model to align it with different research questions and other asynchronously measured variables.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-21"},"PeriodicalIF":3.5,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145066350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

How to Model Ambulatory Assessments Measured at Different Frequencies: An N = 1 Approach. 如何模拟不同频率下的动态评估：N = 1方法。

IF 3.5 3区心理学

Multivariate Behavioral Research Pub Date : 2025-09-11 DOI: 10.1080/00273171.2025.2552303

Sophie W Berkhout, Noémi K Schuurman, Ellen L Hamaker

{"title":"How to Model Ambulatory Assessments Measured at Different Frequencies: An N = 1 Approach.","authors":"Sophie W Berkhout, Noémi K Schuurman, Ellen L Hamaker","doi":"10.1080/00273171.2025.2552303","DOIUrl":"https://doi.org/10.1080/00273171.2025.2552303","url":null,"abstract":"Ambulatory assessment has gained widespread popularity among researchers who study the dynamics of everyday experiences and behaviors, such as sleep patterns or emotional states. In this paper, we focus on the challenge that arises when we want to investigate the dynamic relations between variables measured at different frequencies. As a running example, we use a sleep quality variable measured once every morning and a momentary experience variable measured multiple times during the day for multiple days. We propose two N = 1 models that imply different processes; the first focuses on dynamic relations from day to day between sleep quality and a daily factor of the momentary experience variable, and the second focuses on dynamic relations from one measurement occasion to the next, which prioritizes when the variables affect each other. Additionally, we introduce a combination of these two models and demonstrate their accuracy with a simulation study. An empirical N = 1 example of daily sleep quality and momentary self-doubt demonstrates that dynamic relations exist between sleep quality and self-doubt at certain moments in the day and the daily factor of self-doubt. Researchers may adjust the proposed dynamic models to align with their own theories and to accommodate different data or research interests.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-19"},"PeriodicalIF":3.5,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145034348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mixed-Effects Frequency-Adjusted Borders Ordinal Forest: A Tree Ensemble Method for Ordinal Prediction with Hierarchical Data. 混合效应频率调整边界有序森林：一种分层数据有序预测的树集成方法。

IF 3.5 3区心理学

Multivariate Behavioral Research Pub Date : 2025-09-09 DOI: 10.1080/00273171.2025.2547416

Philip Buczak

{"title":"Mixed-Effects Frequency-Adjusted Borders Ordinal Forest: A Tree Ensemble Method for Ordinal Prediction with Hierarchical Data.","authors":"Philip Buczak","doi":"10.1080/00273171.2025.2547416","DOIUrl":"https://doi.org/10.1080/00273171.2025.2547416","url":null,"abstract":"Predicting ordinal responses such as school grades or rating scale data is a common task in the social and life sciences. Currently, two major streams of methodology exist for ordinal prediction: traditional statistical models such as the proportional odds model and machine learning (ML) methods such as random forest (RF) adapted to ordinal prediction. While methods from the latter stream have displayed high predictive performance, particularly for data characterized by non-linear effects, most of these methods do not support hierarchical data. As such data structures frequently occur in the social and life sciences, e.g., students nested in classes or individual measurements nested within the same person, accounting for hierarchical data is of importance for prediction in these fields. A recently proposed ML method for ordinal prediction displaying promising results for nonhierarchical data is Frequency-Adjusted Borders Ordinal Forest (fabOF). Building on an iterative expectation-maximization-type estimation procedure, I extend fabOF to hierarchical data settings in this work by proposing Mixed-Effects Frequency-Adjusted Borders Ordinal Forest (mixfabOF). The proposed method is shown to achieve performance advantages over fabOF and other existing RF-based prediction methods in settings with high random effect variability. For other settings, mixfabOF performs similarly to fabOF and alternative RF-based prediction methods.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-16"},"PeriodicalIF":3.5,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145024808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0