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Restricted Latent Class Models for Nominal Response Data: Identifiability and Estimation 名义响应数据的限制潜类模型：可识别性与估计
IF 3 2区 心理学
Psychometrika Pub Date : 2023-12-19 DOI: 10.1007/s11336-023-09940-7
Ying Liu, Steven Andrew Culpepper
{"title":"Restricted Latent Class Models for Nominal Response Data: Identifiability and Estimation","authors":"Ying Liu, Steven Andrew Culpepper","doi":"10.1007/s11336-023-09940-7","DOIUrl":"https://doi.org/10.1007/s11336-023-09940-7","url":null,"abstract":"<p>Restricted latent class models (RLCMs) provide an important framework for diagnosing and classifying respondents on a collection of multivariate binary responses. Recent research made significant advances in theory for establishing identifiability conditions for RLCMs with binary and polytomous response data. Multiclass data, which are unordered nominal response data, are also widely collected in the social sciences and psychometrics via forced-choice inventories and multiple choice tests. We establish new identifiability conditions for parameters of RLCMs for multiclass data and discuss the implications for substantive applications. The new identifiability conditions are applicable to a wealth of RLCMs for polytomous and nominal response data. We propose a Bayesian framework for inferring model parameters, assess parameter recovery in a Monte Carlo simulation study, and present an application of the model to a real dataset.</p>","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":"452 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138745505","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 Procedure for Component-Based Structural Equation Modeling for Simple Structure by Simultaneous Rotation 通过同步旋转对简单结构进行基于成分的结构方程建模的探索程序
IF 3 2区 心理学
Psychometrika Pub Date : 2023-12-12 DOI: 10.1007/s11336-023-09942-5
Naoto Yamashita
{"title":"Exploratory Procedure for Component-Based Structural Equation Modeling for Simple Structure by Simultaneous Rotation","authors":"Naoto Yamashita","doi":"10.1007/s11336-023-09942-5","DOIUrl":"https://doi.org/10.1007/s11336-023-09942-5","url":null,"abstract":"<p>Generalized structured component analysis (GSCA) is a structural equation modeling (SEM) procedure that constructs components by weighted sums of observed variables and confirmatorily examines their regressional relationship. The research proposes an exploratory version of GSCA, called exploratory GSCA (EGSCA). EGSCA is analogous to exploratory SEM (ESEM) developed as an exploratory factor-based SEM procedure, which seeks the relationships between the observed variables and the components by orthogonal rotation of the parameter matrices. The indeterminacy of orthogonal rotation in GSCA is first shown as a theoretical support of the proposed method. The whole EGSCA procedure is then presented, together with a new rotational algorithm specialized to EGSCA, which aims at simultaneous simplification of all parameter matrices. Two numerical simulation studies revealed that EGSCA with the following rotation successfully recovered the true values of the parameter matrices and was superior to the existing GSCA procedure. EGSCA was applied to two real datasets, and the model suggested by the EGSCA’s result was shown to be better than the model proposed by previous research, which demonstrates the effectiveness of EGSCA in model exploration.</p>","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":"27 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138573302","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
Erratum to: A Modeling Framework to Examine Psychological Processes Underlying Ordinal Responses and Response Times of Psychometric Data. 检验心理测量数据的有序反应和反应时间背后的心理过程的建模框架的勘误。
IF 3 2区 心理学
Psychometrika Pub Date : 2023-12-01 DOI: 10.1007/s11336-023-09925-6
Inhan Kang, Dylan Molenaar, Roger Ratcliff
{"title":"Erratum to: A Modeling Framework to Examine Psychological Processes Underlying Ordinal Responses and Response Times of Psychometric Data.","authors":"Inhan Kang, Dylan Molenaar, Roger Ratcliff","doi":"10.1007/s11336-023-09925-6","DOIUrl":"10.1007/s11336-023-09925-6","url":null,"abstract":"","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1592"},"PeriodicalIF":3.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9676770","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
Erratum to: Rejoinder to Commentaries on Lyu, Bolt and Westby's "Exploring the Effects of Item Specific Factors in Sequential and IRTree Models". 对Lyu, Bolt和Westby的“探索项目特定因素在序列和IRTree模型中的影响”的评论的回复。
IF 3 2区 心理学
Psychometrika Pub Date : 2023-12-01 DOI: 10.1007/s11336-023-09928-3
Weicong Lyu, Daniel M Bolt
{"title":"Erratum to: Rejoinder to Commentaries on Lyu, Bolt and Westby's \"Exploring the Effects of Item Specific Factors in Sequential and IRTree Models\".","authors":"Weicong Lyu, Daniel M Bolt","doi":"10.1007/s11336-023-09928-3","DOIUrl":"10.1007/s11336-023-09928-3","url":null,"abstract":"","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1591"},"PeriodicalIF":3.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10283822","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
Designing Optimal, Data-Driven Policies from Multisite Randomized Trials. 从多站点随机试验中设计最优、数据驱动的策略。
IF 2.9 2区 心理学
Psychometrika Pub Date : 2023-12-01 Epub Date: 2023-10-24 DOI: 10.1007/s11336-023-09937-2
Youmi Suk, Chan Park
{"title":"Designing Optimal, Data-Driven Policies from Multisite Randomized Trials.","authors":"Youmi Suk, Chan Park","doi":"10.1007/s11336-023-09937-2","DOIUrl":"10.1007/s11336-023-09937-2","url":null,"abstract":"<p><p>Optimal treatment regimes (OTRs) have been widely employed in computer science and personalized medicine to provide data-driven, optimal recommendations to individuals. However, previous research on OTRs has primarily focused on settings that are independent and identically distributed, with little attention given to the unique characteristics of educational settings, where students are nested within schools and there are hierarchical dependencies. The goal of this study is to propose a framework for designing OTRs from multisite randomized trials, a commonly used experimental design in education and psychology to evaluate educational programs. We investigate modifications to popular OTR methods, specifically Q-learning and weighting methods, in order to improve their performance in multisite randomized trials. A total of 12 modifications, 6 for Q-learning and 6 for weighting, are proposed by utilizing different multilevel models, moderators, and augmentations. Simulation studies reveal that all Q-learning modifications improve performance in multisite randomized trials and the modifications that incorporate random treatment effects show the most promise in handling cluster-level moderators. Among weighting methods, the modification that incorporates cluster dummies into moderator variables and augmentation terms performs best across simulation conditions. The proposed modifications are demonstrated through an application to estimate an OTR of conditional cash transfer programs using a multisite randomized trial in Colombia to maximize educational attainment.</p>","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1171-1196"},"PeriodicalIF":2.9,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49694034","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
Diagnosing and Handling Common Violations of Missing at Random. 诊断和处理随机失踪的常见违规行为。
IF 2.9 2区 心理学
Psychometrika Pub Date : 2023-12-01 Epub Date: 2023-01-04 DOI: 10.1007/s11336-022-09896-0
Feng Ji, Sophia Rabe-Hesketh, Anders Skrondal
{"title":"Diagnosing and Handling Common Violations of Missing at Random.","authors":"Feng Ji, Sophia Rabe-Hesketh, Anders Skrondal","doi":"10.1007/s11336-022-09896-0","DOIUrl":"10.1007/s11336-022-09896-0","url":null,"abstract":"<p><p>Ignorable likelihood (IL) approaches are often used to handle missing data when estimating a multivariate model, such as a structural equation model. In this case, the likelihood is based on all available data, and no model is specified for the missing data mechanism. Inference proceeds via maximum likelihood or Bayesian methods, including multiple imputation without auxiliary variables. Such IL approaches are valid under a missing at random (MAR) assumption. Rabe-Hesketh and Skrondal (Ignoring non-ignorable missingness. Presidential Address at the International Meeting of the Psychometric Society, Beijing, China, 2015; Psychometrika, 2023) consider a violation of MAR where a variable A can affect missingness of another variable B also when A is not observed. They show that this case can be handled by discarding more data before proceeding with IL approaches. This data-deletion approach is similar to the sequential estimation of Mohan et al. (in: Advances in neural information processing systems, 2013) based on their ordered factorization theorem but is preferable for parametric models. Which kind of data-deletion or ordered factorization to employ depends on the nature of the MAR violation. In this article, we therefore propose two diagnostic tests, a likelihood-ratio test for a heteroscedastic regression model and a kernel conditional independence test. We also develop a test-based estimator that first uses diagnostic tests to determine which MAR violation appears to be present and then proceeds with the corresponding data-deletion estimator. Simulations show that the test-based estimator outperforms IL when the missing data problem is severe and performs similarly otherwise.</p>","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1123-1143"},"PeriodicalIF":2.9,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10656344/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10847354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
Three Psychometric-Model-Based Option-Scored Multiple Choice Item Design Principles that Enhance Instruction by Improving Quiz Diagnostic Classification of Knowledge Attributes. 三个基于心理测量模型的选项计分选择题设计原则:通过改进知识属性测验诊断分类来加强教学。
IF 2.9 2区 心理学
Psychometrika Pub Date : 2023-12-01 Epub Date: 2022-12-13 DOI: 10.1007/s11336-022-09885-3
William Stout, Robert Henson, Lou DiBello
{"title":"Three Psychometric-Model-Based Option-Scored Multiple Choice Item Design Principles that Enhance Instruction by Improving Quiz Diagnostic Classification of Knowledge Attributes.","authors":"William Stout, Robert Henson, Lou DiBello","doi":"10.1007/s11336-022-09885-3","DOIUrl":"10.1007/s11336-022-09885-3","url":null,"abstract":"<p><p>Three IRT diagnostic-classification-modeling (DCM)-based multiple choice (MC) item design principles are stated that improve classroom quiz student diagnostic classification. Using proven-optimal maximum likelihood-based student classification, example items demonstrate that adherence to these item design principles increases attribute (skills and especially misconceptions) correct classification rates (CCRs). Simple formulas compute these needed item CCRs. By use of these psychometrically driven item design principles, hopefully enough attributes can be accurately diagnosed by necessarily short MC-item-based quizzes to be widely instructionally useful. These results should then stimulate increased use of well-designed MC item quizzes that target accurately diagnosing skills/misconceptions, thereby enhancing classroom learning.</p>","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1299-1333"},"PeriodicalIF":2.9,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10338507","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
Maximum Augmented Empirical Likelihood Estimation of Categorical Marginal Models for Large Sparse Contingency Tables. 大型稀疏列联表范畴边际模型的最大增广经验似然估计。
IF 2.9 2区 心理学
Psychometrika Pub Date : 2023-12-01 Epub Date: 2023-09-26 DOI: 10.1007/s11336-023-09932-7
L Andries van der Ark, Wicher P Bergsma, Letty Koopman
{"title":"Maximum Augmented Empirical Likelihood Estimation of Categorical Marginal Models for Large Sparse Contingency Tables.","authors":"L Andries van der Ark, Wicher P Bergsma, Letty Koopman","doi":"10.1007/s11336-023-09932-7","DOIUrl":"10.1007/s11336-023-09932-7","url":null,"abstract":"<p><p>Categorical marginal models (CMMs) are flexible tools for modelling dependent or clustered categorical data, when the dependencies themselves are not of interest. A major limitation of maximum likelihood (ML) estimation of CMMs is that the size of the contingency table increases exponentially with the number of variables, so even for a moderate number of variables, say between 10 and 20, ML estimation can become computationally infeasible. An alternative method, which retains the optimal asymptotic efficiency of ML, is maximum empirical likelihood (MEL) estimation. However, we show that MEL tends to break down for large, sparse contingency tables. As a solution, we propose a new method, which we call maximum augmented empirical likelihood (MAEL) estimation and which involves augmentation of the empirical likelihood support with a number of well-chosen cells. Simulation results show good finite sample performance for very large contingency tables.</p>","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1228-1248"},"PeriodicalIF":2.9,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10656332/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41159685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
A two-step estimator for multilevel latent class analysis with covariates. 含协变量的多水平潜在类分析的两步估计。
IF 2.9 2区 心理学
Psychometrika Pub Date : 2023-12-01 Epub Date: 2023-08-06 DOI: 10.1007/s11336-023-09929-2
Roberto Di Mari, Zsuzsa Bakk, Jennifer Oser, Jouni Kuha
{"title":"A two-step estimator for multilevel latent class analysis with covariates.","authors":"Roberto Di Mari, Zsuzsa Bakk, Jennifer Oser, Jouni Kuha","doi":"10.1007/s11336-023-09929-2","DOIUrl":"10.1007/s11336-023-09929-2","url":null,"abstract":"<p><p>We propose a two-step estimator for multilevel latent class analysis (LCA) with covariates. The measurement model for observed items is estimated in its first step, and in the second step covariates are added in the model, keeping the measurement model parameters fixed. We discuss model identification, and derive an Expectation Maximization algorithm for efficient implementation of the estimator. By means of an extensive simulation study we show that (1) this approach performs similarly to existing stepwise estimators for multilevel LCA but with much reduced computing time, and (2) it yields approximately unbiased parameter estimates with a negligible loss of efficiency compared to the one-step estimator. The proposal is illustrated with a cross-national analysis of predictors of citizenship norms.</p>","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1144-1170"},"PeriodicalIF":2.9,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10656341/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9943422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
How Social Networks Influence Human Behavior: An Integrated Latent Space Approach for Differential Social Influence. 社会网络如何影响人类行为：差异社会影响的综合潜在空间方法。
IF 2.9 2区 心理学
Psychometrika Pub Date : 2023-12-01 Epub Date: 2023-09-23 DOI: 10.1007/s11336-023-09934-5
Jina Park, Ick Hoon Jin, Minjeong Jeon
{"title":"How Social Networks Influence Human Behavior: An Integrated Latent Space Approach for Differential Social Influence.","authors":"Jina Park, Ick Hoon Jin, Minjeong Jeon","doi":"10.1007/s11336-023-09934-5","DOIUrl":"10.1007/s11336-023-09934-5","url":null,"abstract":"<p><p>How social networks influence human behavior has been an interesting topic in applied research. Existing methods often utilized scale-level behavioral data (e.g., total number of positive responses) to estimate the influence of a social network on human behavior. This study proposes a novel approach to studying social influence that utilizes item-level behavioral measures. Under the latent space modeling framework, we integrate the two latent spaces for respondents' social network data and item-level behavior measures into a single space we call 'interaction map'. The interaction map visualizes the association between the latent homophily among respondents and their item-level behaviors, revealing differential social influence effects across item-level behaviors. We also measure overall social influence by assessing the impact of the interaction map. We evaluate the properties of the proposed approach via extensive simulation studies and demonstrate the proposed approach with a real data in the context of studying how students' friendship network influences their participation in school activities.</p>","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1529-1555"},"PeriodicalIF":2.9,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41152396","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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