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Random Item Response Data Generation Using a Limited-Information Approach: Applications to Assessing Model Complexity. 使用有限信息方法生成随机项目反应数据：评估模型复杂性的应用。
IF 2.9 2区 心理学
Psychometrika Pub Date : 2025-05-21 DOI: 10.1017/psy.2025.10017
Yon Soo Suh, Wes Bonifay, Li Cai
{"title":"Random Item Response Data Generation Using a Limited-Information Approach: Applications to Assessing Model Complexity.","authors":"Yon Soo Suh, Wes Bonifay, Li Cai","doi":"10.1017/psy.2025.10017","DOIUrl":"https://doi.org/10.1017/psy.2025.10017","url":null,"abstract":"","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-51"},"PeriodicalIF":2.9,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144112844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
Exact Exploratory Bi-factor Analysis: A Constraint-Based Optimization Approach. 精确探索性双因素分析：一种基于约束的优化方法。
IF 2.9 2区 心理学
Psychometrika Pub Date : 2025-05-16 DOI: 10.1017/psy.2025.17
Jiawei Qiao, Yunxiao Chen, Zhiliang Ying
{"title":"Exact Exploratory Bi-factor Analysis: A Constraint-Based Optimization Approach.","authors":"Jiawei Qiao, Yunxiao Chen, Zhiliang Ying","doi":"10.1017/psy.2025.17","DOIUrl":"10.1017/psy.2025.17","url":null,"abstract":"<p><p>Bi-factor analysis is a form of confirmatory factor analysis widely used in psychological and educational measurement. The use of a bi-factor model requires specifying an explicit bi-factor structure on the relationship between the observed variables and the group factors. In practice, the bi-factor structure is sometimes unknown, in which case, an exploratory form of bi-factor analysis is needed. Unfortunately, there are few methods for exploratory bi-factor analysis, with the exception of a rotation-based method proposed in Jennrich and Bentler ([2011, Psychometrika 76, pp. 537-549], [2012, Psychometrika 77, pp. 442-454]). However, the rotation method does not yield an exact bi-factor loading structure, even after hard thresholding. In this article, we propose a constraint-based optimization method that learns an exact bi-factor loading structure from data, overcoming the issue with the rotation-based method. The key to the proposed method is a mathematical characterization of the bi-factor loading structure as a set of equality constraints, which allows us to formulate the exploratory bi-factor analysis problem as a constrained optimization problem in a continuous domain and solve the optimization problem with an augmented Lagrangian method. The power of the proposed method is shown via simulation studies and a real data example.</p>","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-16"},"PeriodicalIF":2.9,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144082031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
Identification and Interpretation of the Completely Oblique Rasch Bifactor Model. 完全斜拉希双因子模型的识别与解释。
IF 2.9 2区 心理学
Psychometrika Pub Date : 2025-04-24 DOI: 10.1017/psy.2025.14
Denis Federiakin, Mark R Wilson
{"title":"Identification and Interpretation of the Completely Oblique Rasch Bifactor Model.","authors":"Denis Federiakin, Mark R Wilson","doi":"10.1017/psy.2025.14","DOIUrl":"https://doi.org/10.1017/psy.2025.14","url":null,"abstract":"","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-71"},"PeriodicalIF":2.9,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144029305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
Show Me Some ID: A Universal Identification Program for Structural Equation Models. Show Me Some ID：结构方程模型的通用识别程序。
IF 2.9 2区 心理学
Psychometrika Pub Date : 2025-04-24 DOI: 10.1017/psy.2025.19
Michael D Hunter, Robert M Kirkpatrick, Michael C Neale
{"title":"Show Me Some ID: A Universal Identification Program for Structural Equation Models.","authors":"Michael D Hunter, Robert M Kirkpatrick, Michael C Neale","doi":"10.1017/psy.2025.19","DOIUrl":"10.1017/psy.2025.19","url":null,"abstract":"<p><p>With models and research designs ever increasing in complexity, the foundational question of model identification is more important than ever. The determination of whether or not a model can be fit at all or fit to some particular data set is the essence of model identification. In this article, we pull from previously published work on data-independent model identification applicable to a broad set of structural equation models, and extend it further to include extremely flexible exogenous covariate effects and also to include data-dependent empirical model identification. For illustrative purposes, we apply this model identification solution to several small examples for which the answer is already known, including a real data example from the National Longitudinal Survey of Youth; however, the method applies similarly to models that are far from simple to comprehend. The solution is implemented in the open-source OpenMx package in R.</p>","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-24"},"PeriodicalIF":2.9,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144050601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
Exploratory General-response Cognitive Diagnostic Models with Higher-order Structures. 探索性高阶结构一般反应认知诊断模型。
IF 2.9 2区 心理学
Psychometrika Pub Date : 2025-04-16 DOI: 10.1017/psy.2025.15
Jia Liu, Seunghyun Lee, Yuqi Gu
{"title":"Exploratory General-response Cognitive Diagnostic Models with Higher-order Structures.","authors":"Jia Liu, Seunghyun Lee, Yuqi Gu","doi":"10.1017/psy.2025.15","DOIUrl":"https://doi.org/10.1017/psy.2025.15","url":null,"abstract":"","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-42"},"PeriodicalIF":2.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144058676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
Comparing Functional Trend and Learning among Groups in Intensive Binary Longitudinal Eye-Tracking Data using By-Variable Smooth Functions of GAMM. 基于GAMM的逐变平滑函数比较密集二值纵向眼动追踪数据的组间功能趋势和学习。
IF 2.9 2区 心理学
Psychometrika Pub Date : 2025-04-14 DOI: 10.1007/psy.2024.27
Sun-Joo Cho, Sarah Brown-Schmidt, Sharice Clough, Melissa C Duff
{"title":"Comparing Functional Trend and Learning among Groups in Intensive Binary Longitudinal Eye-Tracking Data using By-Variable Smooth Functions of GAMM.","authors":"Sun-Joo Cho, Sarah Brown-Schmidt, Sharice Clough, Melissa C Duff","doi":"10.1007/psy.2024.27","DOIUrl":"https://doi.org/10.1007/psy.2024.27","url":null,"abstract":"<p><p>This paper presents a model specification for group comparisons regarding a functional trend over time within a trial and learning across a series of trials in intensive binary longitudinal eye-tracking data. The functional trend and learning effects are modeled using by-variable smooth functions. This model specification is formulated as a generalized additive mixed model, which allowed for the use of the freely available mgcv package (Wood in Package 'mgcv.' https://cran.r-project.org/web/packages/mgcv/mgcv.pdf, 2023) in R. The model specification was applied to intensive binary longitudinal eye-tracking data, where the questions of interest concern differences between individuals with and without brain injury in their real-time language comprehension and how this affects their learning over time. The results of the simulation study show that the model parameters are recovered well and the by-variable smooth functions are adequately predicted in the same condition as those found in the application.</p>","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-30"},"PeriodicalIF":2.9,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144050131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
Simultaneous Object and Category Score Estimation in Joint Correspondence Analysis. 在联合对应分析中同时估算对象和类别得分。
IF 2.9 2区 心理学
Psychometrika Pub Date : 2025-04-07 DOI: 10.1017/psy.2025.12
Naomichi Makino
{"title":"Simultaneous Object and Category Score Estimation in Joint Correspondence Analysis.","authors":"Naomichi Makino","doi":"10.1017/psy.2025.12","DOIUrl":"10.1017/psy.2025.12","url":null,"abstract":"<p><p>Joint correspondence analysis (JCA) is a statistical method for obtaining a low-dimensional representation of multivariate categorical data. It was developed as an alternative to multiple correspondence analysis (MCA). Typically, the solution is visualized through a map that projects the data onto a reduced space. A joint map, which shows both object and category scores in the same space, helps users explore inter- and intra-relationships in objects and categories. However, unlike MCA, current JCA estimation methods do not allow the joint representation of objects and categories on the map, which limits the interpretability of JCA results. To overcome this limitation, we propose a simultaneous object and category score estimation method for JCA while addressing the underestimated variance problem that is inherent in MCA. In the proposed method, JCA parameters are estimated by minimizing the discrepancy between the observed categorical data and the JCA data model, rather than relying on the JCA covariance model used in existing estimation methods. Previous research has shown that JCA is comparable to exploratory factor analysis. We also address the factor-analytic interpretation of JCA solutions in addition to geometric interpretation. Two real data analysis examples are also presented to demonstrate the geometric and factor-analytic interpretations of the JCA solutions.</p>","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-16"},"PeriodicalIF":2.9,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
Bayesian Identification and Estimation of Growth Mixture Models. 增长混合模型的贝叶斯识别和估计。
IF 2.9 2区 心理学
Psychometrika Pub Date : 2025-04-07 DOI: 10.1017/psy.2025.11
Xingyao Xiao, Sophia Rabe-Hesketh, Anders Skrondal
{"title":"Bayesian Identification and Estimation of Growth Mixture Models.","authors":"Xingyao Xiao, Sophia Rabe-Hesketh, Anders Skrondal","doi":"10.1017/psy.2025.11","DOIUrl":"https://doi.org/10.1017/psy.2025.11","url":null,"abstract":"","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-35"},"PeriodicalIF":2.9,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
Testing of Reverse Causality Using Semi-Supervised Machine Learning. 使用半监督机器学习测试反向因果关系。
IF 2.9 2区 心理学
Psychometrika Pub Date : 2025-04-07 DOI: 10.1017/psy.2025.13
Nan Zhang, Heng Xu, Manuel J Vaulont, Zhen Zhang
{"title":"Testing of Reverse Causality Using Semi-Supervised Machine Learning.","authors":"Nan Zhang, Heng Xu, Manuel J Vaulont, Zhen Zhang","doi":"10.1017/psy.2025.13","DOIUrl":"10.1017/psy.2025.13","url":null,"abstract":"<p><p>Two potential obstacles stand between the observation of a statistical correlation and the design (and deployment) of an effective intervention, <i>omitted variable bias</i> and <i>reverse causality</i>. Whereas the former has received ample attention, comparably scant focus has been devoted to the latter in the methodological literature. Many existing methods for reverse causality testing commence by postulating a structural model that may suffer from widely recognized issues such as the difficulty of properly setting temporal lags, which are critical to model validity. In this article, we draw upon advances in machine learning, specifically the recently established link between causal direction and the effectiveness of semi-supervised learning algorithms, to develop a novel method for reverse causality testing that circumvents many of the assumptions required by traditional methods. Mathematical analysis and simulation studies were carried out to demonstrate the effectiveness of our method. We also performed tests over a real-world dataset to show how our method may be used to identify causal relationships in practice.</p>","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-25"},"PeriodicalIF":2.9,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
Logistic Multidimensional Data Analysis for Ordinal Response Variables Using a Cumulative Link Function. 基于累积链接函数的有序响应变量的Logistic多维数据分析。
IF 2.9 2区 心理学
Psychometrika Pub Date : 2025-03-27 DOI: 10.1017/psy.2025.10
Mark de Rooij, Ligaya Breemer, Dion Woestenburg, Frank Busing
{"title":"Logistic Multidimensional Data Analysis for Ordinal Response Variables Using a Cumulative Link Function.","authors":"Mark de Rooij, Ligaya Breemer, Dion Woestenburg, Frank Busing","doi":"10.1017/psy.2025.10","DOIUrl":"https://doi.org/10.1017/psy.2025.10","url":null,"abstract":"<p><p>We present a multidimensional data analysis framework for the analysis of ordinal response variables. Underlying the ordinal variables, we assume a continuous latent variable, leading to cumulative logit models. The framework includes unsupervised methods, when no predictor variables are available, and supervised methods, when predictor variables are available. We distinguish between dominance variables and proximity variables, where dominance variables are analyzed using inner product models, whereas the proximity variables are analyzed using distance models. An expectation-majorization-minimization algorithm is derived for estimation of the parameters of the models. We illustrate our methodology with three empirical data sets highlighting the advantages of the proposed framework. A simulation study is conducted to evaluate the performance of the algorithm.</p>","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-37"},"PeriodicalIF":2.9,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144026859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
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