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Proof of Reliability Convergence to 1 at Rate of Spearman–Brown Formula for Random Test Forms and Irrespective of Item Pool Dimensionality Psychometrika (IF 3.0) Pub Date : 2024-03-12 Jules L. Ellis, Klaas Sijtsma
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Nodal Heterogeneity can Induce Ghost Triadic Effects in Relational Event Models Psychometrika (IF 3.0) Pub Date : 2024-03-06 Rūta Juozaitienė, Ernst C. Wit
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Variational Estimation for Multidimensional Generalized Partial Credit Model Psychometrika (IF 3.0) Pub Date : 2024-03-01 Chengyu Cui, Chun Wang, Gongjun Xu
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Book Review Computational Aspects of Psychometric Methods by Martinková & Hladká Psychometrika (IF 3.0) Pub Date : 2024-02-29 Zhiqing Lin, Huilin Chen
As reported by Martinková, P., & Hladká, A. (Computational Aspects of Psychometric Methods: With R. Boca Raton, CRC Press, FL, 2023) Computational Aspects of Psychometric Methods: With R. Boca Raton, FL: CRC Press.
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DIF Analysis with Unknown Groups and Anchor Items Psychometrika (IF 3.0) Pub Date : 2024-02-21
Abstract Ensuring fairness in instruments like survey questionnaires or educational tests is crucial. One way to address this is by a Differential Item Functioning (DIF) analysis, which examines if different subgroups respond differently to a particular item, controlling for their overall latent construct level. DIF analysis is typically conducted to assess measurement invariance at the item level
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Using External Information for More Precise Inferences in General Regression Models Psychometrika (IF 3.0) Pub Date : 2024-02-20 Martin Jann, Martin Spiess
Empirical research usually takes place in a space of available external information, like results from single studies, meta-analyses, official statistics or subjective (expert) knowledge. The available information ranges from simple means and proportions to known relations between a multitude of variables or estimated distributions. In psychological research, external information derived from the named
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Bayesian Semiparametric Longitudinal Inverse-Probit Mixed Models for Category Learning Psychometrika (IF 3.0) Pub Date : 2024-02-19 Minerva Mukhopadhyay, Jacie R. McHaney, Bharath Chandrasekaran, Abhra Sarkar
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Generalized Structured Component Analysis Accommodating Convex Components: A Knowledge-Based Multivariate Method with Interpretable Composite Indexes Psychometrika (IF 3.0) Pub Date : 2024-02-16 Gyeongcheol Cho, Heungsun Hwang
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A Spectral Method for Identifiable Grade of Membership Analysis with Binary Responses Psychometrika (IF 3.0) Pub Date : 2024-02-15
Abstract Grade of membership (GoM) models are popular individual-level mixture models for multivariate categorical data. GoM allows each subject to have mixed memberships in multiple extreme latent profiles. Therefore, GoM models have a richer modeling capacity than latent class models that restrict each subject to belong to a single profile. The flexibility of GoM comes at the cost of more challenging
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On the Identifiability of 3- and 4-Parameter Item Response Theory Models From the Perspective of Knowledge Space Theory Psychometrika (IF 3.0) Pub Date : 2024-02-13 Stefano Noventa, Sangbeak Ye, Augustin Kelava, Andrea Spoto
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A Multidimensional Model to Facilitate Within Person Comparison of Attributes Psychometrika (IF 3.0) Pub Date : 2024-02-08 Mark L. Davison, Seungwon Chung, Nidhi Kohli, Ernest C. Davenport
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Measures of Agreement with Multiple Raters: Fréchet Variances and Inference Psychometrika (IF 3.0) Pub Date : 2024-01-08 Jonas Moss
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Restricted Latent Class Models for Nominal Response Data: Identifiability and Estimation Psychometrika (IF 3.0) Pub Date : 2023-12-19 Ying Liu, Steven Andrew Culpepper
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Exploratory Procedure for Component-Based Structural Equation Modeling for Simple Structure by Simultaneous Rotation Psychometrika (IF 3.0) Pub Date : 2023-12-12 Naoto Yamashita
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A Note on Improving Variational Estimation for Multidimensional Item Response Theory Psychometrika (IF 3.0) Pub Date : 2023-11-18 Chenchen Ma, Jing Ouyang, Chun Wang, Gongjun Xu
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What Can We Learn from a Semiparametric Factor Analysis of Item Responses and Response Time? An Illustration with the PISA 2015 Data Psychometrika (IF 3.0) Pub Date : 2023-11-16 Yang Liu, Weimeng Wang
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A Latent Hidden Markov Model for Process Data Psychometrika (IF 3.0) Pub Date : 2023-11-07 Xueying Tang
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Adjusted Residuals for Evaluating Conditional Independence in IRT Models for Multistage Adaptive Testing Psychometrika (IF 3.0) Pub Date : 2023-11-06 Peter W. van Rijn, Usama S. Ali, Hyo Jeong Shin, Sean-Hwane Joo
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Designing Optimal, Data-Driven Policies from Multisite Randomized Trials Psychometrika (IF 3.0) Pub Date : 2023-10-24 Youmi Suk, Chan Park
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Maximum Augmented Empirical Likelihood Estimation of Categorical Marginal Models for Large Sparse Contingency Tables Psychometrika (IF 3.0) Pub Date : 2023-09-26 L. Andries van der Ark, Wicher P. Bergsma, Letty Koopman
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How Social Networks Influence Human Behavior: An Integrated Latent Space Approach for Differential Social Influence Psychometrika (IF 3.0) Pub Date : 2023-09-23 Jina Park, Ick Hoon Jin, Minjeong Jeon
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A General Theorem and Proof for the Identification of Composed CFA Models Psychometrika (IF 3.0) Pub Date : 2023-09-19 R. Maximilian Bee, Tobias Koch, Michael Eid
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Estimating and Using Block Information in the Thurstonian IRT Model Psychometrika (IF 3.0) Pub Date : 2023-08-28 Susanne Frick
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Joint Latent Space Model for Social Networks with Multivariate Attributes Psychometrika (IF 3.0) Pub Date : 2023-08-24 Selena Wang, Subhadeep Paul, Paul De Boeck
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DIF Statistical Inference Without Knowing Anchoring Items Psychometrika (IF 3.0) Pub Date : 2023-08-07 Yunxiao Chen, Chengcheng Li, Jing Ouyang, Gongjun Xu
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A two-step estimator for multilevel latent class analysis with covariates Psychometrika (IF 3.0) Pub Date : 2023-08-06 Roberto Di Mari, Zsuzsa Bakk, Jennifer Oser, Jouni Kuha
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The Dirichlet Dual Response Model: An Item Response Model for Continuous Bounded Interval Responses Psychometrika (IF 3.0) Pub Date : 2023-07-20 Matthias Kloft, Raphael Hartmann, Andreas Voss, Daniel W. Heck
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Item-Specific Factors in IRTree Models: When They Matter and When They Don’t Psychometrika (IF 3.0) Pub Date : 2023-06-16 Thorsten Meiser, Fabiola Reiber
Lyu et al. (Psychometrika, 2023) demonstrated that item-specific factors can cause spurious effects on the structural parameters of IRTree models for multiple nested response processes per item. Here, we discuss some boundary conditions and argue that person selection effects on item parameters are not unique to item-specific factors and that the effects presented by Lyu et al. (Psychometrika, 2023)
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Exploring the Effects of Item-Specific Factors in Sequential and IRTree Models Psychometrika (IF 3.0) Pub Date : 2023-06-16 Weicong Lyu, Daniel M. Bolt, Samuel Westby
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Rejoinder to Commentaries on Lyu, Bolt and Westby’s “Exploring the Effects of Item Specific Factors in Sequential and IRTree Models” Psychometrika (IF 3.0) Pub Date : 2023-06-16 Weicong Lyu, Daniel M. Bolt
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A Latent Space Diffusion Item Response Theory Model to Explore Conditional Dependence between Responses and Response Times Psychometrika (IF 3.0) Pub Date : 2023-06-14 Inhan Kang, Minjeong Jeon, Ivailo Partchev
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Fitting and Testing Log-Linear Subpopulation Models with Known Support Psychometrika (IF 3.0) Pub Date : 2023-06-14 David J. Hessen
In this paper, the support of the joint probability distribution of categorical variables in the total population is treated as unknown. From a general total population model with unknown support, a general subpopulation model with its support equal to the set of all observed score patterns is derived. In maximum likelihood estimation of the parameters of any such subpopulation model, the evaluation
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Measuring Agreement Using Guessing Models and Knowledge Coefficients Psychometrika (IF 3.0) Pub Date : 2023-06-08 Jonas Moss
Several measures of agreement, such as the Perreault–Leigh coefficient, the \(\textsc {AC}_{1}\), and the recent coefficient of van Oest, are based on explicit models of how judges make their ratings. To handle such measures of agreement under a common umbrella, we propose a class of models called guessing models, which contains most models of how judges make their ratings. Every guessing model have
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Multinomial Logistic Factor Regression for Multi-source Functional Block-wise Missing Data Psychometrika (IF 3.0) Pub Date : 2023-06-02 Xiuli Du, Xiaohu Jiang, Jinguan Lin
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Factor Tree Copula Models for Item Response Data Psychometrika (IF 3.0) Pub Date : 2023-06-01 Sayed H. Kadhem, Aristidis K. Nikoloulopoulos
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Random Effects Multinomial Processing Tree Models: A Maximum Likelihood Approach Psychometrika (IF 3.0) Pub Date : 2023-05-29 Steffen Nestler, Edgar Erdfelder
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Comparing Bayesian Variable Selection to Lasso Approaches for Applications in Psychology Psychometrika (IF 3.0) Pub Date : 2023-05-23 Sierra A. Bainter, Thomas G. McCauley, Mahmoud M. Fahmy, Zachary T. Goodman, Lauren B. Kupis, J. Sunil Rao
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A Modeling Framework to Examine Psychological Processes Underlying Ordinal Responses and Response Times of Psychometric Data Psychometrika (IF 3.0) Pub Date : 2023-05-12 Inhan Kang, Dylan Molenaar, Roger Ratcliff
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Commentary: Explore Conditional Dependencies in Item Response Tree Data Psychometrika (IF 3.0) Pub Date : 2023-04-27 Minjeong Jeon
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Advantages of Using Unweighted Approximation Error Measures for Model Fit Assessment Psychometrika (IF 3.0) Pub Date : 2023-04-18 Dirk Lubbe
Fit indices are highly frequently used for assessing the goodness of fit of latent variable models. Most prominent fit indices, such as the root-mean-square error of approximation (RMSEA) or the comparative fit index (CFI), are based on a noncentrality parameter estimate derived from the model fit statistic. While a noncentrality parameter estimate is well suited for quantifying the amount of systematic
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Rotation to Sparse Loadings Using $$L^p$$ Losses and Related Inference Problems Psychometrika (IF 3.0) Pub Date : 2023-03-31 Xinyi Liu, Gabriel Wallin, Yunxiao Chen, Irini Moustaki
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Incorporating Functional Response Time Effects into a Signal Detection Theory Model Psychometrika (IF 3.0) Pub Date : 2023-03-29 Sun-Joo Cho, Sarah Brown-Schmidt, Paul De Boeck, Matthew Naveiras, Si On Yoon, Aaron Benjamin
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Longitudinal Modeling of Age-Dependent Latent Traits with Generalized Additive Latent and Mixed Models Psychometrika (IF 3.0) Pub Date : 2023-03-28 Øystein Sørensen, Anders M. Fjell, Kristine B. Walhovd
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A Test to Distinguish Monotone Homogeneity from Monotone Multifactor Models Psychometrika (IF 3.0) Pub Date : 2023-03-18 Jules L. Ellis, Klaas Sijtsma
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Detecting Changes in Correlation Networks with Application to Functional Connectivity of fMRI Data Psychometrika (IF 3.0) Pub Date : 2023-03-09 Changryong Baek, Benjamin Leinwand, Kristen A. Lindquist, Seok-Oh Jeong, Joseph Hopfinger, Katheleen M. Gates, Vladas Pipiras
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Blind Subgrouping of Task-based fMRI Psychometrika (IF 3.0) Pub Date : 2023-03-09 Zachary F. Fisher, Jonathan Parsons, Kathleen M. Gates, Joseph B. Hopfinger
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Dynamical Non-compensatory Multidimensional IRT Model Using Variational Approximation Psychometrika (IF 3.0) Pub Date : 2023-03-06 Hiroshi Tamano, Daichi Mochihashi
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Identifiability of Hidden Markov Models for Learning Trajectories in Cognitive Diagnosis Psychometrika (IF 3.0) Pub Date : 2023-02-16 Ying Liu, Steven Andrew Culpepper, Yuguo Chen
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Dynamic Response Strategies: Accounting for Response Process Heterogeneity in IRTree Decision Nodes Psychometrika (IF 3.0) Pub Date : 2023-02-06 Viola Merhof, Thorsten Meiser
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Accurate Confidence and Bayesian Interval Estimation for Non-centrality Parameters and Effect Size Indices Psychometrika (IF 3.0) Pub Date : 2023-02-01 Kaidi Kang, Megan T. Jones, Kristan Armstrong, Suzanne Avery, Maureen McHugo, Stephan Heckers, Simon Vandekar
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Partial Identification of Latent Correlations with Ordinal Data Psychometrika (IF 3.0) Pub Date : 2023-01-31 Jonas Moss, Steffen Grønneberg
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Bayesian Inference for an Unknown Number of Attributes in Restricted Latent Class Models Psychometrika (IF 3.0) Pub Date : 2023-01-22 Yinghan Chen, Steven Andrew Culpepper, Yuguo Chen
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A Note on the Connection Between Trek Rules and Separable Nonlinear Least Squares in Linear Structural Equation Models Psychometrika (IF 3.0) Pub Date : 2022-12-25 Maximilian S. Ernst, Aaron Peikert, Andreas M. Brandmaier, Yves Rosseel
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Ignoring Non-ignorable Missingness Psychometrika (IF 3.0) Pub Date : 2022-12-20 Sophia Rabe-Hesketh, Anders Skrondal
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Three Psychometric-Model-Based Option-Scored Multiple Choice Item Design Principles that Enhance Instruction by Improving Quiz Diagnostic Classification of Knowledge Attributes Psychometrika (IF 3.0) Pub Date : 2022-12-13 William Stout, Robert Henson, Lou DiBello
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
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Bi-factor and Second-Order Copula Models for Item Response Data Psychometrika (IF 3.0) Pub Date : 2022-11-21 Sayed H. Kadhem, Aristidis K. Nikoloulopoulos
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A Bayesian Approach Towards Missing Covariate Data in Multilevel Latent Regression Models Psychometrika (IF 3.0) Pub Date : 2022-11-23 Christian Aßmann, Jean-Christoph Gaasch, Doris Stingl
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Psychometric Society Meeting of the Members University of Bologna, Bologna, Italy, July 15, 2022. Psychometrika (IF 3.0) Pub Date : 2023-03-01
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A Tensor-EM Method for Large-Scale Latent Class Analysis with Binary Responses Psychometrika (IF 3.0) Pub Date : 2022-10-01 Zhenghao Zeng, Yuqi Gu, Gongjun Xu
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Generic Identifiability of the DINA Model and Blessing of Latent Dependence Psychometrika (IF 3.0) Pub Date : 2022-09-27 Yuqi Gu
Cognitive diagnostic models are a powerful family of fine-grained discrete latent variable models in psychometrics. Within this family, the DINA model is a fundamental and parsimonious one that has received significant attention. Similar to other complex latent variable models, identifiability is an important issue for CDMs, including the DINA model. Gu and Xu (Psychometrika 84(2):468–483, 2019) established