British Journal of Mathematical & Statistical Psychology最新文献_第9页

Bayesian hierarchical response time modelling—A tutorial 贝叶斯分层响应时间建模-教程

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2023-02-22 DOI: 10.1111/bmsp.12302

Christoph Koenig, Benjamin Becker, Esther Ulitzsch

{"title":"Bayesian hierarchical response time modelling—A tutorial","authors":"Christoph Koenig, Benjamin Becker, Esther Ulitzsch","doi":"10.1111/bmsp.12302","DOIUrl":"10.1111/bmsp.12302","url":null,"abstract":"Response time modelling is developing rapidly in the field of psychometrics, and its use is growing in psychology. In most applications, component models for response times are modelled jointly with component models for responses, thereby stabilizing estimation of item response theory model parameters and enabling research on a variety of novel substantive research questions. Bayesian estimation techniques facilitate estimation of response time models. Implementations of these models in standard statistical software, however, are still sparse. In this accessible tutorial, we discuss one of the most common response time models—the lognormal response time model—embedded in the hierarchical framework by van der Linden (2007). We provide detailed guidance on how to specify and estimate this model in a Bayesian hierarchical context. One of the strengths of the presented model is its flexibility, which makes it possible to adapt and extend the model according to researchers' needs and hypotheses on response behaviour. We illustrate this based on three recent model extensions: (a) application to non-cognitive data incorporating the distance-difficulty hypothesis, (b) modelling conditional dependencies between response times and responses, and (c) identifying differences in response behaviour via mixture modelling. This tutorial aims to provide a better understanding of the use and utility of response time models, showcases how these models can easily be adapted and extended, and contributes to a growing need for these models to answer novel substantive research questions in both non-cognitive and cognitive contexts.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":"76 3","pages":"623-645"},"PeriodicalIF":2.6,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bmsp.12302","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10763975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A model-based approach to multivariate principal component regression: Selecting principal components and estimating standard errors for unstandardized regression coefficients 基于模型的多元主成分回归方法:选择主成分和估计非标准化回归系数的标准误差

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2023-02-05 DOI: 10.1111/bmsp.12301

Fei Gu, Mike W.-L. Cheung

引用次数: 0

Heterogeneous heterogeneity by default: Testing categorical moderators in mixed-effects meta-analysis 默认的异质性:混合效应荟萃分析中分类调节因子的检验

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2023-02-02 DOI: 10.1111/bmsp.12299

Josue E. Rodriguez, Donald R. Williams, Paul-Christian Bürkner

{"title":"Heterogeneous heterogeneity by default: Testing categorical moderators in mixed-effects meta-analysis","authors":"Josue E. Rodriguez, Donald R. Williams, Paul-Christian Bürkner","doi":"10.1111/bmsp.12299","DOIUrl":"10.1111/bmsp.12299","url":null,"abstract":"Categorical moderators are often included in mixed-effects meta-analysis to explain heterogeneity in effect sizes. An assumption in tests of categorical moderator effects is that of a constant between-study variance across all levels of the moderator. Although it rarely receives serious thought, there can be statistical ramifications to upholding this assumption. We propose that researchers should instead default to assuming unequal between-study variances when analysing categorical moderators. To achieve this, we suggest using a mixed-effects location-scale model (MELSM) to allow group-specific estimates for the between-study variance. In two extensive simulation studies, we show that in terms of Type I error and statistical power, little is lost by using the MELSM for moderator tests, but there can be serious costs when an equal variance mixed-effects model (MEM) is used. Most notably, in scenarios with balanced sample sizes or equal between-study variance, the Type I error and power rates are nearly identical between the MEM and the MELSM. On the other hand, with imbalanced sample sizes and unequal variances, the Type I error rate under the MEM can be grossly inflated or overly conservative, whereas the MELSM does comparatively well in controlling the Type I error across the majority of cases. A notable exception where the MELSM did not clearly outperform the MEM was in the case of few studies (e.g., 5). With respect to power, the MELSM had similar or higher power than the MEM in conditions where the latter produced non-inflated Type 1 error rates. Together, our results support the idea that assuming unequal between-study variances is preferred as a default strategy when testing categorical moderators.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":"76 2","pages":"402-433"},"PeriodicalIF":2.6,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9255143","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

Mixture-modelling-based Bayesian MH-RM algorithm for the multidimensional 4PLM 基于混合建模的多维4PLM贝叶斯MH-RM算法

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2023-02-02 DOI: 10.1111/bmsp.12300

Shaoyang Guo, Yanlei Chen, Chanjin Zheng, Guiyu Li

引用次数: 0

Multilevel SEM with random slopes in discrete data using the pairwise maximum likelihood 使用成对最大似然的离散数据中具有随机斜率的多层扫描电镜

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2023-01-12 DOI: 10.1111/bmsp.12294

Maria T. Barendse, Yves Rosseel

{"title":"Multilevel SEM with random slopes in discrete data using the pairwise maximum likelihood","authors":"Maria T. Barendse, Yves Rosseel","doi":"10.1111/bmsp.12294","DOIUrl":"10.1111/bmsp.12294","url":null,"abstract":"Pairwise maximum likelihood (PML) estimation is a promising method for multilevel models with discrete responses. Multilevel models take into account that units within a cluster tend to be more alike than units from different clusters. The pairwise likelihood is then obtained as the product of bivariate likelihoods for all within-cluster pairs of units and items. In this study, we investigate the PML estimation method with computationally intensive multilevel random intercept and random slope structural equation models (SEM) in discrete data. In pursuing this, we first reconsidered the general ‘wide format’ (WF) approach for SEM models and then extend the WF approach with random slopes. In a small simulation study we the determine accuracy and efficiency of the PML estimation method by varying the sample size (250, 500, 1000, 2000), response scales (two-point, four-point), and data-generating model (mediation model with three random slopes, factor model with one and two random slopes). Overall, results show that the PML estimation method is capable of estimating computationally intensive random intercept and random slopes multilevel models in the SEM framework with discrete data and many (six or more) latent variables with satisfactory accuracy and efficiency. However, the condition with 250 clusters combined with a two-point response scale shows more bias.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":"76 2","pages":"327-352"},"PeriodicalIF":2.6,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bmsp.12294","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9308773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Penalized optimal scaling for ordinal variables with an application to international classification of functioning core sets 惩罚最优尺度的有序变量与应用的国际分类功能核心集

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2023-01-10 DOI: 10.1111/bmsp.12297

Aisouda Hoshiyar, Henk A. L. Kiers, Jan Gertheiss

{"title":"Penalized optimal scaling for ordinal variables with an application to international classification of functioning core sets","authors":"Aisouda Hoshiyar, Henk A. L. Kiers, Jan Gertheiss","doi":"10.1111/bmsp.12297","DOIUrl":"10.1111/bmsp.12297","url":null,"abstract":"Ordinal data occur frequently in the social sciences. When applying principal component analysis (PCA), however, those data are often treated as numeric, implying linear relationships between the variables at hand; alternatively, non-linear PCA is applied where the obtained quantifications are sometimes hard to interpret. Non-linear PCA for categorical data, also called optimal scoring/scaling, constructs new variables by assigning numerical values to categories such that the proportion of variance in those new variables that is explained by a predefined number of principal components (PCs) is maximized. We propose a penalized version of non-linear PCA for ordinal variables that is a smoothed intermediate between standard PCA on category labels and non-linear PCA as used so far. The new approach is by no means limited to monotonic effects and offers both better interpretability of the non-linear transformation of the category labels and better performance on validation data than unpenalized non-linear PCA and/or standard linear PCA. In particular, an application of penalized optimal scaling to ordinal data as given with the International Classification of Functioning, Disability and Health (ICF) is provided.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":"76 2","pages":"353-371"},"PeriodicalIF":2.6,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bmsp.12297","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9254457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Extending exploratory diagnostic classification models: Inferring the effect of covariates 扩展探索性诊断分类模型:推断协变量的影响

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2023-01-05 DOI: 10.1111/bmsp.12298

Hulya Duygu Yigit, Steven Andrew Culpepper

{"title":"Extending exploratory diagnostic classification models: Inferring the effect of covariates","authors":"Hulya Duygu Yigit, Steven Andrew Culpepper","doi":"10.1111/bmsp.12298","DOIUrl":"10.1111/bmsp.12298","url":null,"abstract":"Diagnostic models provide a statistical framework for designing formative assessments by classifying student knowledge profiles according to a collection of fine-grained attributes. The context and ecosystem in which students learn may play an important role in skill mastery, and it is therefore important to develop methods for incorporating student covariates into diagnostic models. Including covariates may provide researchers and practitioners with the ability to evaluate novel interventions or understand the role of background knowledge in attribute mastery. Existing research is designed to include covariates in confirmatory diagnostic models, which are also known as restricted latent class models. We propose new methods for including covariates in exploratory RLCMs that jointly infer the latent structure and evaluate the role of covariates on performance and skill mastery. We present a novel Bayesian formulation and report a Markov chain Monte Carlo algorithm using a Metropolis-within-Gibbs algorithm for approximating the model parameter posterior distribution. We report Monte Carlo simulation evidence regarding the accuracy of our new methods and present results from an application that examines the role of student background knowledge on the mastery of a probability data set.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":"76 2","pages":"372-401"},"PeriodicalIF":2.6,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bmsp.12298","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9609479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effect sizes in ANCOVA and difference-in-differences designs ANCOVA和差中差设计的效应量

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2023-01-02 DOI: 10.1111/bmsp.12296

Larry V. Hedges, Elizabeth Tipton, Rrita Zejnullahi, Karina G. Diaz

引用次数: 3

Which method delivers greater signal-to-noise ratio: Structural equation modelling or regression analysis with weighted composites? 哪一种方法的信噪比更大:结构方程模型还是加权复合材料回归分析?

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2022-12-02 DOI: 10.1111/bmsp.12293

Ke-Hai Yuan, Yongfei Fang

{"title":"Which method delivers greater signal-to-noise ratio: Structural equation modelling or regression analysis with weighted composites?","authors":"Ke-Hai Yuan, Yongfei Fang","doi":"10.1111/bmsp.12293","DOIUrl":"10.1111/bmsp.12293","url":null,"abstract":"Observational data typically contain measurement errors. Covariance-based structural equation modelling (CB-SEM) is capable of modelling measurement errors and yields consistent parameter estimates. In contrast, methods of regression analysis using weighted composites as well as a partial least squares approach to SEM facilitate the prediction and diagnosis of individuals/participants. But regression analysis with weighted composites has been known to yield attenuated regression coefficients when predictors contain errors. Contrary to the common belief that CB-SEM is the preferred method for the analysis of observational data, this article shows that regression analysis via weighted composites yields parameter estimates with much smaller standard errors, and thus corresponds to greater values of the signal-to-noise ratio (SNR). In particular, the SNR for the regression coefficient via the least squares (LS) method with equally weighted composites is mathematically greater than that by CB-SEM if the items for each factor are parallel, even when the SEM model is correctly specified and estimated by an efficient method. Analytical, numerical and empirical results also show that LS regression using weighted composites performs as well as or better than the normal maximum likelihood method for CB-SEM under many conditions even when the population distribution is multivariate normal. Results also show that the LS regression coefficients become more efficient when considering the sampling errors in the weights of composites than those that are conditional on weights.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":"76 3","pages":"646-678"},"PeriodicalIF":2.6,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41180529","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: 6

Editorial acknowledgement 编辑确认

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2022-11-18 DOI: 10.1111/bmsp.12295

引用次数: 0