Multivariate Behavioral Research最新文献

On the Latent Structure of Responses and Response Times from Multidimensional Personality Measurement with Ordinal Rating Scales.

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-12-23 DOI: 10.1080/00273171.2024.2436406

Inhan Kang

{"title":"On the Latent Structure of Responses and Response Times from Multidimensional Personality Measurement with Ordinal Rating Scales.","authors":"Inhan Kang","doi":"10.1080/00273171.2024.2436406","DOIUrl":"https://doi.org/10.1080/00273171.2024.2436406","url":null,"abstract":"In this article, we propose latent variable models that jointly account for responses and response times (RTs) in multidimensional personality measurements. We address two key research questions regarding the latent structure of RT distributions through model comparisons. First, we decompose RT into decision and non-decision times by incorporating irreducible minimum shifts in RT distributions, as done in cognitive decision-making models. Second, we investigate whether the speed factor underlying decision times should be multidimensional with the same latent structure as personality traits, or, if a unidimensional speed factor suffices. Comprehensive model comparisons across four distinct datasets suggest that a joint model with person-specific parameters to account for shifts in RT distributions and a unidimensional speed factor provides the best account for ordinal responses and RTs. Posterior predictive checks further confirm these findings. Additionally, simulation studies validate the parameter recovery of the proposed models and support the empirical results. Most importantly, failing to account for the irreducible minimum shift in RT distributions leads to systematic biases in other model components and severe underestimation of the nonlinear relationship between responses and RTs.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-30"},"PeriodicalIF":5.3,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142883392","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

Evaluating Contextual Models for Intensive Longitudinal Data in the Presence of Noise. 在存在噪声的情况下评估密集纵向数据的情境模型。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-12-15 DOI: 10.1080/00273171.2024.2436420

Anja F Ernst, Eva Ceulemans, Laura F Bringmann, Janne Adolf

{"title":"Evaluating Contextual Models for Intensive Longitudinal Data in the Presence of Noise.","authors":"Anja F Ernst, Eva Ceulemans, Laura F Bringmann, Janne Adolf","doi":"10.1080/00273171.2024.2436420","DOIUrl":"https://doi.org/10.1080/00273171.2024.2436420","url":null,"abstract":"Nowadays research into affect frequently employs intensive longitudinal data to assess fluctuations in daily emotional experiences. The resulting data are often analyzed with moderated autoregressive models to capture the influences of contextual events on the emotion dynamics. The presence of noise (e.g., measurement error) in the measures of the contextual events, however, is commonly ignored in these models. Disregarding noise in these covariates when it is present may result in biased parameter estimates and wrong conclusions drawn about the underlying emotion dynamics. In a simulation study we evaluate the estimation accuracy, assessed in terms of bias and variance, of different moderated autoregressive models in the presence of noise in the covariate. We show that estimation accuracy decreases when the amount of noise in the covariate increases. We also show that this bias is magnified by a larger effect of the covariate, a slower switching frequency of the covariate, a discrete rather than a continuous covariate, and constant rather than occasional noise in the covariate. We also show that the bias that results from a noisy covariate does not decrease when the number of observations increases. We end with a few recommendations for applying moderated autoregressive models based on our simulation.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-21"},"PeriodicalIF":5.3,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142830449","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

A Gentle Introduction and Application of Feature-Based Clustering with Psychological Time Series.

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-12-11 DOI: 10.1080/00273171.2024.2432918

Jannis Kreienkamp, Maximilian Agostini, Rei Monden, Kai Epstude, Peter de Jonge, Laura F Bringmann

{"title":"A Gentle Introduction and Application of Feature-Based Clustering with Psychological Time Series.","authors":"Jannis Kreienkamp, Maximilian Agostini, Rei Monden, Kai Epstude, Peter de Jonge, Laura F Bringmann","doi":"10.1080/00273171.2024.2432918","DOIUrl":"10.1080/00273171.2024.2432918","url":null,"abstract":"Psychological researchers and practitioners collect increasingly complex time series data aimed at identifying differences between the developments of participants or patients. Past research has proposed a number of dynamic measures that describe meaningful developmental patterns for psychological data (e.g., instability, inertia, linear trend). Yet, commonly used clustering approaches are often not able to include these meaningful measures (e.g., due to model assumptions). We propose feature-based time series clustering as a flexible, transparent, and well-grounded approach that clusters participants based on the dynamic measures directly using common clustering algorithms. We introduce the approach and illustrate the utility of the method with real-world empirical data that highlight common ESM challenges of multivariate conceptualizations, structural missingness, and non-stationary trends. We use the data to showcase the main steps of input selection, feature extraction, feature reduction, feature clustering, and cluster evaluation. We also provide practical algorithm overviews and readily available code for data preparation, analysis, and interpretation.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-31"},"PeriodicalIF":5.3,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808443","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

Using Projective IRT to Evaluate the Effects of Multidimensionality on Unidimensional IRT Model Parameters.

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-12-09 DOI: 10.1080/00273171.2024.2430630

Steven P Reise, Jared M Block, Maxwell Mansolf, Mark G Haviland, Benjamin D Schalet, Rachel Kimerling

{"title":"Using Projective IRT to Evaluate the Effects of Multidimensionality on Unidimensional IRT Model Parameters.","authors":"Steven P Reise, Jared M Block, Maxwell Mansolf, Mark G Haviland, Benjamin D Schalet, Rachel Kimerling","doi":"10.1080/00273171.2024.2430630","DOIUrl":"https://doi.org/10.1080/00273171.2024.2430630","url":null,"abstract":"The application of unidimensional IRT models requires item response data to be unidimensional. Often, however, item response data contain a dominant dimension, as well as one or more nuisance dimensions caused by content clusters. Applying a unidimensional IRT model to multidimensional data causes violations of local independence, which can vitiate IRT applications. To evaluate and, possibly, remedy the problems caused by forcing unidimensional models onto multidimensional data, we consider the creation of a projected unidimensional IRT model, where the multidimensionality caused by nuisance dimensions is controlled for by integrating them out from the model. Specifically, when item response data have a bifactor structure, one can create a unidimensional model based on projecting to the general factor. Importantly, the projected unidimensional IRT model can be used as a benchmark for comparison to a unidimensional model to judge the practical consequences of multidimensionality. Limitations of the proposed approach are detailed.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-17"},"PeriodicalIF":5.3,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803121","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 Importance of Considering Concurrent Effects in Random-Intercept Cross-Lagged Panel Modelling: Example Analysis of Bullying and Internalising Problems. 论随机截距交叉滞后面板模型中考虑并发效应的重要性：欺凌和内化问题的实例分析。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-11-26 DOI: 10.1080/00273171.2024.2428222

Lydia G Speyer, Xinxin Zhu, Yi Yang, Denis Ribeaud, Manuel Eisner

{"title":"On the Importance of Considering Concurrent Effects in Random-Intercept Cross-Lagged Panel Modelling: Example Analysis of Bullying and Internalising Problems.","authors":"Lydia G Speyer, Xinxin Zhu, Yi Yang, Denis Ribeaud, Manuel Eisner","doi":"10.1080/00273171.2024.2428222","DOIUrl":"https://doi.org/10.1080/00273171.2024.2428222","url":null,"abstract":"Random-intercept cross-lagged panel models (RI-CLPMs) are increasingly used to investigate research questions focusing on how one variable at one time point affects another variable at the subsequent time point. Due to the implied temporal sequence of events in such research designs, interpretations of RI-CLPMs primarily focus on longitudinal cross-lagged paths while disregarding concurrent associations and modeling these only as residual covariances. However, this may cause biased cross-lagged effects. This may be especially so when data collected at the same time point refers to different reference timeframes, creating a temporal sequence of events for constructs measured concurrently. To examine this issue, we conducted a series of empirical analyses in which the impact of modeling or not modeling of directional within-time point associations may impact inferences drawn from RI-CLPMs using data from the longitudinal z-proso study. Results highlight that not considering directional concurrent effects may lead to biased cross-lagged effects. Thus, it is essential to carefully consider potential directional concurrent effects when choosing models to analyze directional associations between variables over time. If temporal sequences of concurrent effects cannot be clearly established, testing multiple models and drawing conclusions based on the robustness of effects across all models is recommended.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-17"},"PeriodicalIF":5.3,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142717612","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

Latently Mediating: A Bayesian Take on Causal Mediation Analysis with Structured Survey Data. 潜中介：利用结构化调查数据的贝叶斯因果中介分析。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-11-18 DOI: 10.1080/00273171.2024.2424514

Alessandro Varacca

引用次数: 0

Latent Markov Models to Test the Strategy Use of 3-Year-Olds in a Rule-Based Feedback-Learning Task. 用潜在马尔可夫模型测试 3 岁幼儿在基于规则的反馈学习任务中的策略使用情况

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-11-01 Epub Date: 2023-02-10 DOI: 10.1080/00273171.2023.2170963

L Lichtenberg, I Visser, M E J Raijmakers

引用次数: 0

From Behavioral Genetics to Idiographic Science: Methodological Developments and Applications Inspired by the Work of Peter C. M. Molenaar. 从行为遗传学到图像学：从行为遗传学到成语科学：受彼得-莫伦纳尔（Peter C. M. Molenaar）著作启发的方法论发展与应用》。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-11-01 Epub Date: 2024-08-30 DOI: 10.1080/00273171.2024.2394054

Sy-Miin Chow, Ellen L Hamaker, Nilam Ram

引用次数: 0

Homogeneity Assumptions in the Analysis of Dynamic Processes. 动态过程分析中的同质性假设。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-11-01 Epub Date: 2023-07-10 DOI: 10.1080/00273171.2023.2225172

Siwei Liu, Kathleen M Gates, Emilio Ferrer

{"title":"Homogeneity Assumptions in the Analysis of Dynamic Processes.","authors":"Siwei Liu, Kathleen M Gates, Emilio Ferrer","doi":"10.1080/00273171.2023.2225172","DOIUrl":"10.1080/00273171.2023.2225172","url":null,"abstract":"With the increased use of time series data in human research, ranging from ecological momentary assessments to data passively obtained, researchers can explore dynamic processes more than ever before. An important question researchers must ask themselves is, do I think all individuals have similar processes? If not, how different, and in what ways? Dr. Peter Molenaar's work set the foundation to answer these questions by providing insight into individual-level analysis for processes that are assumed to differ across individuals in at least some aspects. Currently, such assumptions do not have a clear taxonomy regarding the degree of homogeneity in the patterns of relations among variables and the corresponding parameter values. This paper provides the language with which researchers can discuss assumptions inherent in their analyses. We define strict homogeneity as the assumption that all individuals have an identical pattern of relations as well as parameter values; pattern homogeneity assumes the same pattern of relations but parameter values can differ; weak homogeneity assumes there are some (but not all) generalizable aspects of the process; and no homogeneity explicitly assumes no population-level similarities in dynamic processes across individuals. We demonstrate these assumptions with an empirical data set of daily emotions in couples.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1166-1176"},"PeriodicalIF":5.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9820682","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

Touchstones of Equivalence and the Houdini Transformation. 等价试金石和胡迪尼变换。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-11-01 Epub Date: 2023-05-16 DOI: 10.1080/00273171.2023.2205390

Michael J Rovine, Paul A McDermott

引用次数: 0