首页 > 最新文献

Multivariate Behavioral Research最新文献

英文 中文
Non-Stationarity in Time-Series Analysis: Modeling Stochastic and Deterministic Trends. 时间序列分析中的非平稳性:随机和确定性趋势建模。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-01-15 DOI: 10.1080/00273171.2024.2436413
Oisín Ryan, Jonas M B Haslbeck, Lourens J Waldorp

Time series analysis is increasingly popular across scientific domains. A key concept in time series analysis is stationarity, the stability of statistical properties of a time series. Understanding stationarity is crucial to addressing frequent issues in time series analysis such as the consequences of failing to model non-stationarity, how to determine the mechanisms generating non-stationarity, and consequently how to model those mechanisms (i.e., by differencing or detrending). However, many empirical researchers have a limited understanding of stationarity, which can lead to the use of incorrect research practices and misleading substantive conclusions. In this paper, we address this problem by answering these questions in an accessible way. To this end, we study how researchers can use detrending and differencing to model trends in time series analysis. We show via simulation the consequences of modeling trends inappropriately, and evaluate the performance of one popular approach to distinguish different trend types in empirical data. We present these results in an accessible way, providing an extensive introduction to key concepts in time series analysis, illustrated throughout with simple examples. Finally, we discuss a number of take-home messages and extensions to standard approaches, which directly address more complex time-series analysis problems encountered by empirical researchers.

时间序列分析在科学领域越来越受欢迎。时间序列分析中的一个关键概念是平稳性,即时间序列统计性质的稳定性。理解平稳性对于解决时间序列分析中经常出现的问题至关重要,例如未能对非平稳性进行建模的后果,如何确定产生非平稳性的机制,以及如何对这些机制进行建模(即,通过差异或去趋势)。然而,许多实证研究人员对平稳性的理解有限,这可能导致使用不正确的研究实践和误导性的实质性结论。在本文中,我们通过以一种易于理解的方式回答这些问题来解决这个问题。为此,我们研究了研究人员如何在时间序列分析中使用趋势性和差异性来建模趋势。我们通过模拟展示了对趋势建模不当的后果,并评估了在经验数据中区分不同趋势类型的一种流行方法的性能。我们以一种易于理解的方式呈现这些结果,对时间序列分析中的关键概念进行了广泛的介绍,并通过简单的示例进行了说明。最后,我们讨论了一些关键信息和标准方法的扩展,这些方法直接解决了实证研究人员遇到的更复杂的时间序列分析问题。
{"title":"Non-Stationarity in Time-Series Analysis: Modeling Stochastic and Deterministic Trends.","authors":"Oisín Ryan, Jonas M B Haslbeck, Lourens J Waldorp","doi":"10.1080/00273171.2024.2436413","DOIUrl":"10.1080/00273171.2024.2436413","url":null,"abstract":"<p><p>Time series analysis is increasingly popular across scientific domains. A key concept in time series analysis is stationarity, the stability of statistical properties of a time series. Understanding stationarity is crucial to addressing frequent issues in time series analysis such as the consequences of failing to model non-stationarity, how to determine the mechanisms generating non-stationarity, and consequently how to model those mechanisms (i.e., by differencing or detrending). However, many empirical researchers have a limited understanding of stationarity, which can lead to the use of incorrect research practices and misleading substantive conclusions. In this paper, we address this problem by answering these questions in an accessible way. To this end, we study how researchers can use detrending and differencing to model trends in time series analysis. We show <i>via</i> simulation the consequences of modeling trends inappropriately, and evaluate the performance of one popular approach to distinguish different trend types in empirical data. We present these results in an accessible way, providing an extensive introduction to key concepts in time series analysis, illustrated throughout with simple examples. Finally, we discuss a number of take-home messages and extensions to standard approaches, which directly address more complex time-series analysis problems encountered by empirical researchers.</p>","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-33"},"PeriodicalIF":5.3,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143016116","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
Causal Estimands and Multiply Robust Estimation of Mediated-Moderation. 中介调节的因果估计与多重稳健估计。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-01-13 DOI: 10.1080/00273171.2024.2444949
Xiao Liu, Mark Eddy, Charles R Martinez

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.

在研究不同亚组间的效应异质性(即调节)时,研究者往往对异质性的中介机制感兴趣,即介导的调节。为了评估中介性调节,传统方法通常需要参数模型来定义中介性调节,当参数模型可能被错误指定和当因果解释感兴趣时,这有局限性。对于中介的因果解释,因果中介分析越来越受欢迎,但对中介的调节分析还不发达。在本研究中,我们扩展了因果中介文献,并提出了一种新的中介调节分析方法。使用潜在结果框架,我们获得了分解总调节的两个因果估计:(i)归因于调解人的中介调节和(ii)归因于调解人的剩余调节。我们还开发了一种用于中介调节分析的多重稳健估计方法,该方法可以将机器学习方法纳入因果估计的推断中。我们通过仿真对该方法进行了评估。我们通过评估预防干预对青少年行为结果影响的性别差异的中介机制来说明所提出的中介调节分析。
{"title":"Causal Estimands and Multiply Robust Estimation of Mediated-Moderation.","authors":"Xiao Liu, Mark Eddy, Charles R Martinez","doi":"10.1080/00273171.2024.2444949","DOIUrl":"https://doi.org/10.1080/00273171.2024.2444949","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-27"},"PeriodicalIF":5.3,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973231","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
MIIVefa: An R Package for a New Type of Exploratory Factor Anaylysis Using Model-Implied Instrumental Variables. MIIVefa:一个使用模型隐含工具变量的新型探索性因子分析的R包。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-12-27 DOI: 10.1080/00273171.2024.2436418
Lan Luo, Kathleen M Gates, Kenneth A Bollen

We present the R package MIIVefa, designed to implement the MIIV-EFA algorithm. This algorithm explores and identifies the underlying factor structure within a set of variables. The resulting model is not a typical exploratory factor analysis (EFA) model because some loadings are fixed to zero and it allows users to include hypothesized correlated errors such as might occur with longitudinal data. As such, it resembles a confirmatory factor analysis (CFA) model. But, unlike CFA, the MIIV-EFA algorithm determines the number of factors and the items that load on these factors directly from the data. We provide both simulation and empirical examples to illustrate the application of MIIVefa and discuss its benefits and limitations.

我们提出了R包MIIVefa,旨在实现MIIV-EFA算法。该算法在一组变量中探索并识别潜在的因素结构。所得到的模型不是典型的探索性因子分析(EFA)模型,因为一些载荷被固定为零,它允许用户包括假设的相关误差,例如纵向数据可能发生的误差。因此,它类似于验证性因素分析(CFA)模型。但是,与CFA不同的是,MIIV-EFA算法直接从数据中确定因素的数量和加载在这些因素上的项目。我们提供了模拟和经验例子来说明MIIVefa的应用,并讨论了它的优点和局限性。
{"title":"MIIVefa: An R Package for a New Type of Exploratory Factor Anaylysis Using Model-Implied Instrumental Variables.","authors":"Lan Luo, Kathleen M Gates, Kenneth A Bollen","doi":"10.1080/00273171.2024.2436418","DOIUrl":"https://doi.org/10.1080/00273171.2024.2436418","url":null,"abstract":"<p><p>We present the R package MIIVefa, designed to implement the MIIV-EFA algorithm. This algorithm explores and identifies the underlying factor structure within a set of variables. The resulting model is not a typical exploratory factor analysis (EFA) model because some loadings are fixed to zero and it allows users to include hypothesized correlated errors such as might occur with longitudinal data. As such, it resembles a confirmatory factor analysis (CFA) model. But, unlike CFA, the MIIV-EFA algorithm determines the number of factors and the items that load on these factors directly from the data. We provide both simulation and empirical examples to illustrate the application of MIIVefa and discuss its benefits and limitations.</p>","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-9"},"PeriodicalIF":5.3,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142900361","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 Latent Structure of Responses and Response Times from Multidimensional Personality Measurement with Ordinal Rating Scales. 基于顺序评定量表的多维人格测量反应的潜在结构和反应时间。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-12-23 DOI: 10.1080/00273171.2024.2436406
Inhan Kang

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.

在本文中,我们提出了潜在变量模型,共同解释多维人格测量中的反应和反应时间(RTs)。我们通过模型比较解决了关于RT分布潜在结构的两个关键研究问题。首先,我们将RT分解为决策时间和非决策时间,通过纳入RT分布中不可约的最小位移,就像在认知决策模型中所做的那样。其次,我们研究决策时间的速度因子是否应该是多维的,具有与人格特质相同的潜在结构,或者如果一个单维的速度因子就足够了。对四个不同数据集的综合模型比较表明,考虑RT分布变化的个体参数和一维速度因子的联合模型最能解释有序响应和RT。后验预测检查进一步证实了这些发现。此外,仿真研究验证了所提出模型的参数恢复,并支持了实证结果。最重要的是,未能考虑到RT分布中不可约的最小位移会导致其他模型成分的系统性偏差,并严重低估响应与RT之间的非线性关系。
{"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":"<p><p>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.</p>","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 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-12-15 DOI: 10.1080/00273171.2024.2436420
Anja F Ernst, Eva Ceulemans, Laura F Bringmann, Janne Adolf

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.

目前,对情绪的研究经常使用密集的纵向数据来评估日常情绪体验的波动。由此产生的数据通常采用调节自回归模型进行分析,以捕捉情境事件对情绪动态的影响。然而,这些模型通常忽略了背景事件测量中存在的噪声(如测量误差)。如果忽略这些协变量中存在的噪声,可能会导致参数估计偏差,并对潜在的情绪动态得出错误的结论。在一项模拟研究中,我们从偏差和方差的角度评估了存在协变量噪声时不同缓和自回归模型的估计精度。我们发现,当协变量中的噪声增加时,估计精度会降低。我们还表明,协变量的影响越大、协变量的切换频率越慢、协变量是离散的而不是连续的、协变量中的噪声是恒定的而不是偶尔出现的,这种偏差就越大。我们还表明,当观测数据数量增加时,噪声协变量导致的偏差并不会减少。最后,我们根据模拟结果提出了一些应用节制自回归模型的建议。
{"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":"<p><p>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.</p>","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. 基于心理时间序列的特征聚类及其应用。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS 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

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.

心理学研究人员和从业人员收集越来越复杂的时间序列数据,旨在识别参与者或患者发展之间的差异。过去的研究提出了一些动态测量方法来描述有意义的心理数据发展模式(如不稳定性、惯性、线性趋势)。然而,常用的聚类方法通常不能包括这些有意义的度量(例如,由于模型假设)。我们提出基于特征的时间序列聚类是一种灵活、透明和有充分基础的方法,它直接使用常见的聚类算法基于动态度量对参与者进行聚类。我们介绍了该方法,并用现实世界的经验数据说明了该方法的实用性,这些数据突出了多变量概念化、结构缺失和非平稳趋势等常见的ESM挑战。我们使用这些数据来展示输入选择、特征提取、特征约简、特征聚类和聚类评估的主要步骤。我们还提供实用的算法概述和现成的数据准备、分析和解释代码。
{"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":"<p><p>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.</p>","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. 利用射影IRT评价多维度对一维IRT模型参数的影响。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS 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

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.

一维IRT模型的应用要求项目反应数据是一维的。然而,项目响应数据通常包含一个主要维度,以及一个或多个由内容集群引起的麻烦维度。将一维IRT模型应用于多维数据会导致违反本地独立性,从而破坏IRT应用程序。为了评估并可能补救将一维模型强制应用于多维数据所造成的问题,我们考虑创建一个投影的一维IRT模型,其中通过将有害维度从模型中集成出来来控制由它们引起的多维度。具体来说,当项目反应数据具有双因素结构时,可以基于对一般因素的投影来创建一维模型。重要的是,投影的一维IRT模型可以作为与一维模型比较的基准,以判断多维的实际后果。本文详细介绍了该方法的局限性。
{"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":"<p><p>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.</p>","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 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-11-26 DOI: 10.1080/00273171.2024.2428222
Lydia G Speyer, Xinxin Zhu, Yi Yang, Denis Ribeaud, Manuel Eisner

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.

随机截距交叉滞后面板模型(RI-CLPMs)越来越多地用于研究一个时间点的一个变量如何影响随后时间点的另一个变量的问题。由于此类研究设计中隐含了事件的时间顺序,因此对 RI-CLPM 的解释主要集中在纵向交叉滞后路径上,而忽略了并发关联,仅将其建模为残差协方差。然而,这可能会导致有偏差的交叉滞后效应。尤其是当在同一时间点收集的数据指的是不同的参考时间范围,从而为同时测量的构念创建了一个事件的时间序列时,这种情况可能会更加严重。为了研究这个问题,我们利用纵向 z-proso 研究的数据进行了一系列实证分析,其中建模或不建模时间点内的定向关联可能会影响从 RI-CLPMs 得出的推论。结果突出表明,不考虑方向性并发效应可能会导致有偏差的交叉滞后效应。因此,在选择模型分析变量随时间变化的方向性关联时,必须仔细考虑潜在的方向性并发效应。如果无法明确确定并发效应的时间序列,建议测试多个模型,并根据所有模型效应的稳健性得出结论。
{"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":"<p><p>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.</p>","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 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-11-18 DOI: 10.1080/00273171.2024.2424514
Alessandro Varacca

In this paper, we propose a Bayesian causal mediation approach to the analysis of experimental data when both the outcome and the mediator are measured through structured questionnaires based on Likert-scaled inquiries. Our estimation strategy builds upon the error-in-variables literature and, specifically, it leverages Item Response Theory to explicitly model the observed surrogate mediator and outcome measures. We employ their elicited latent counterparts in a simple g-computation algorithm, where we exploit the fundamental identifying assumptions of causal mediation analysis to impute all the relevant counterfactuals and estimate the causal parameters of interest. We finally devise a sensitivity analysis procedure to test the robustness of the proposed methods to the restrictive requirement of mediator's conditional ignorability. We demonstrate the functioning of our proposed methodology through an empirical application using survey data from an online experiment on food purchasing intentions and the effect of different labeling regimes.

在本文中,我们提出了一种贝叶斯因果中介方法来分析实验数据,即通过基于李克特量表调查的结构化问卷来测量结果和中介。我们的估算策略建立在变量误差文献的基础上,具体来说,它利用项目反应理论(Item Response Theory)对观察到的中介变量和结果变量进行明确建模。我们在一个简单的 g 计算算法中使用了所激发的潜在对应变量,利用因果中介分析的基本识别假设来估算所有相关的反事实,并估算相关的因果参数。最后,我们设计了一个敏感性分析程序,以检验所提出的方法对中介人条件无知这一限制性要求的稳健性。我们通过一个关于食品购买意向和不同标签制度影响的在线实验调查数据的实证应用,证明了我们提出的方法的功能。
{"title":"Latently Mediating: A Bayesian Take on Causal Mediation Analysis with Structured Survey Data.","authors":"Alessandro Varacca","doi":"10.1080/00273171.2024.2424514","DOIUrl":"https://doi.org/10.1080/00273171.2024.2424514","url":null,"abstract":"<p><p>In this paper, we propose a Bayesian causal mediation approach to the analysis of experimental data when both the outcome and the mediator are measured through structured questionnaires based on Likert-scaled inquiries. Our estimation strategy builds upon the error-in-variables literature and, specifically, it leverages Item Response Theory to explicitly model the observed surrogate mediator and outcome measures. We employ their elicited latent counterparts in a simple g-computation algorithm, where we exploit the fundamental identifying assumptions of causal mediation analysis to impute all the relevant counterfactuals and estimate the causal parameters of interest. We finally devise a sensitivity analysis procedure to test the robustness of the proposed methods to the restrictive requirement of mediator's conditional ignorability. We demonstrate the functioning of our proposed methodology through an empirical application using survey data from an online experiment on food purchasing intentions and the effect of different labeling regimes.</p>","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-23"},"PeriodicalIF":5.3,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142649633","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
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 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-11-01 Epub Date: 2023-02-10 DOI: 10.1080/00273171.2023.2170963
L Lichtenberg, I Visser, M E J Raijmakers

This study is the first to investigate how 3-year-olds learn simple rules from feedback using the Toddler Card Sorting Task (TCST). To account for intra- and inter- individual differences in the learning process, latent Markov models were fitted to the time series of accuracy responses using maximum likelihood techniques (Visser et al., 2002). In a first, exploratory study (N = 110, 3- to 5-years olds) a considerable group of 3-year olds applied a hypothesis testing learning strategy. A second study confirmed these results with a preregistered study (3-years olds, N = 60). Under supportive learning conditions, a majority of 3-year- olds was capable of hypothesis testing. Furthermore, older children and those with bigger working memory capacities were more likely to use hypothesis testing, even though the latter group perseverated more than younger children or those with smaller working memory capacities. 3-year-olds are more advanced feedback-learners than assumed.

本研究首次利用幼儿卡片分类任务(TCST)研究 3 岁幼儿如何从反馈中学习简单规则。为了考虑学习过程中个体内部和个体之间的差异,我们使用最大似然法(Visser 等人,2002 年)对准确性反应的时间序列拟合了潜在马尔可夫模型。在第一项探索性研究(N = 110,3-5 岁儿童)中,相当一部分 3 岁儿童采用了假设检验学习策略。第二项研究通过一项预先登记的研究(3 岁儿童,N = 60)证实了这些结果。在支持性学习条件下,大多数 3 岁幼儿都能进行假设检验。此外,年龄较大和工作记忆能力较强的儿童更有可能进行假设检验,尽管他们比年龄较小或工作记忆能力较弱的儿童更容易坚持不懈。3 岁儿童的反馈学习能力比假定的更强。
{"title":"Latent Markov Models to Test the Strategy Use of 3-Year-Olds in a Rule-Based Feedback-Learning Task.","authors":"L Lichtenberg, I Visser, M E J Raijmakers","doi":"10.1080/00273171.2023.2170963","DOIUrl":"10.1080/00273171.2023.2170963","url":null,"abstract":"<p><p>This study is the first to investigate how 3-year-olds learn simple rules from feedback using the Toddler Card Sorting Task (TCST). To account for intra- and inter- individual differences in the learning process, latent Markov models were fitted to the time series of accuracy responses using maximum likelihood techniques (Visser et al., 2002). In a first, exploratory study (N = 110, 3- to 5-years olds) a considerable group of 3-year olds applied a hypothesis testing learning strategy. A second study confirmed these results with a preregistered study (3-years olds, N = 60). Under supportive learning conditions, a majority of 3-year- olds was capable of hypothesis testing. Furthermore, older children and those with bigger working memory capacities were more likely to use hypothesis testing, even though the latter group perseverated more than younger children or those with smaller working memory capacities. 3-year-olds are more advanced feedback-learners than assumed.</p>","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1123-1136"},"PeriodicalIF":5.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10675513","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
期刊
Multivariate Behavioral Research
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1