Psychometrika最新文献_第2页

A-Optimal Designs for Rasch Poisson Gamma Counts Models. 拉希泊松伽马计数模型的最佳设计。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-07-17 DOI: 10.1017/psy.2026.10136

Heinz Holling, Rainer Schwabe

{"title":"A-Optimal Designs for Rasch Poisson Gamma Counts Models.","authors":"Heinz Holling, Rainer Schwabe","doi":"10.1017/psy.2026.10136","DOIUrl":"10.1017/psy.2026.10136","url":null,"abstract":"In this article, we develop A-optimal designs for a longitudinal Rasch Poisson Gamma counts model (RPGCM). The model is particularly well suited for repeated testing designs in educational and psychological research when the outcomes are count data. Within the RPGCM framework, test scores depend on two types of parameters: respondents' abilities, which are modeled as gamma-distributed random effects, and item difficulties, which are treated as fixed parameters. To account for dependencies among repeated measurements, we incorporate a compound symmetry structure into the RPGCM. Parameter estimation is performed using maximum quasi-likelihood methods. Based on this framework, we derive A-optimal designs that allow for efficient estimation of mean abilities and commonly used functions thereof. In analogy to classical ANOVA models, no constraints are imposed on the mean structure. The resulting A-optimal designs are substantially less restrictive than commonly used D-optimal designs. Moreover, the derived designs are also valid for a more general class of longitudinal Poisson Gamma models with repeated measurements. These models can be applied to analyze growth curves for count data in other disciplines, including epidemiology, medicine, and criminology.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-16"},"PeriodicalIF":2.9,"publicationDate":"2026-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148474201","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

Mean and True Score Convergence in Nonparametric Item Response Theory. 非参数项目反应理论中值与真值的收敛性。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-07-10 DOI: 10.1017/psy.2026.10135

Steffen Grønneberg

引用次数: 0

Single-Item Reliability for Intensive Longitudinal Data: A Three-Level Linear Mixed-Effect Autoregressive Modeling Approach. 密集纵向数据的单项可靠性：一种三水平线性混合效应自回归建模方法。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-07-09 DOI: 10.1017/psy.2026.10133

Tzu-Yao Lin, Francis Tuerlinckx, Sophie Vanbelle

{"title":"Single-Item Reliability for Intensive Longitudinal Data: A Three-Level Linear Mixed-Effect Autoregressive Modeling Approach.","authors":"Tzu-Yao Lin, Francis Tuerlinckx, Sophie Vanbelle","doi":"10.1017/psy.2026.10133","DOIUrl":"10.1017/psy.2026.10133","url":null,"abstract":"With the advancement of technology, intensive longitudinal data (ILD) are increasingly collected to study dynamic, moment-to-moment psychological and physiological processes. Despite their widespread use, the issues of measurement error and reliability are often inadequately addressed, particularly in the presence of serial correlation and complex multilevel structures. This article introduces a three-level linear mixed-effects model that explicitly accounts for nested structures (moments within days within persons) and incorporates two distinct autoregressive processes for moment- and day-level serial correlations, partitioning measurement error from within-person variability. Building on this model, we delineate two approaches to defining reliability: a variance partitioning approach and a correlation approach. This framework yields a multifaceted set of reliability coefficients, enabling researchers to distinguish between-person from within-person reliability, and to assess measurement agreement across different time scales (e.g., moment-to-moment and day-to-day). We further discuss the relationship between the proposed reliability measures and those derived from simpler models. We apply our model to study the reliability of a single negative affect item in an ILD study in which students were measured multiple times a day for several days before and after receiving their exam results.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-32"},"PeriodicalIF":2.9,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148413988","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

Predicting Multilevel Growth Trajectories Using a Random-Effect Diagnostic Classification Model. 使用随机效应诊断分类模型预测多水平增长轨迹。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-07-08 DOI: 10.1017/psy.2026.10126

Kazuhiro Yamaguchi, Haruhiko Mitsunaga, Shun Saso, Yuri Uesaka

{"title":"Predicting Multilevel Growth Trajectories Using a Random-Effect Diagnostic Classification Model.","authors":"Kazuhiro Yamaguchi, Haruhiko Mitsunaga, Shun Saso, Yuri Uesaka","doi":"10.1017/psy.2026.10126","DOIUrl":"https://doi.org/10.1017/psy.2026.10126","url":null,"abstract":"Learning diagnosis is essential for effective education, with formative assessments shown to significantly enhance academic performance. Diagnostic classification models (DCMs) have been developed to assess students' learning status and provide remedial instruction. However, the impact of mastery or non-mastery of specific attributes on long-term learning development remains uncertain. If certain non-mastered attributes hinder the growth of mathematical ability, early intervention becomes essential. In this study, we developed a random-effects DCM for multilevel growth curves (RDC-MGC) model to identify the specific effects of attribute mastery on individual-level mathematics ability growth. The simulation studies showed that the Bayesian estimation procedure provided appropriate parameter recovery and coverage probabilities, whereas ignoring the multilevel structure resulted in biased parameter estimates. The model was applied to arithmetic test data from second- to sixth-grade elementary school students. Diagnosis was conducted in the second grade, and the effects of mastery on mathematics ability growth from the third to sixth grades were assessed. The results showed that attribute mastery in second grade was associated with both the intercept and slope of individual ability growth, suggesting the potential importance of early-stage diagnostic information for understanding later mathematical development. Potential extensions of the proposed RDC-MGC model are also discussed.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-46"},"PeriodicalIF":2.9,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148406863","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

Regularized Joint Maximum Likelihood Estimation for Exploratory Multidimensional Item Response Theory Models. 探索性多维项目反应理论模型的正则化联合最大似然估计。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-07-07 DOI: 10.1017/psy.2026.10132

Pengyuan Travis Yang, Wilco Emons, Katrijn Van Deun

引用次数: 0

Capturing Heterogeneity in Levels, Variability, and Couplings across Persons and Time with a Hierarchical Time-Varying Coefficient Formulation of the Multivariate Normal. 利用多元正态的分层时变系数公式捕捉人与时间之间的水平、可变性和耦合的异质性。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-07-07 DOI: 10.1017/psy.2026.10125

Esther Ulitzsch, Steffen Nestler, Sverre Urnes Johnson, Therese Ruud Snuggerud, Oliver Lüdtke

{"title":"Capturing Heterogeneity in Levels, Variability, and Couplings across Persons and Time with a Hierarchical Time-Varying Coefficient Formulation of the Multivariate Normal.","authors":"Esther Ulitzsch, Steffen Nestler, Sverre Urnes Johnson, Therese Ruud Snuggerud, Oliver Lüdtke","doi":"10.1017/psy.2026.10125","DOIUrl":"https://doi.org/10.1017/psy.2026.10125","url":null,"abstract":"Time-varying coefficient modeling (TVCM), which represents regression coefficients as smooth functions of continuous time, provides a flexible framework for uncovering complex patterns of change in levels and associations in intensive longitudinal data. However, conventional TVCM remains limited to investigating directional effects across individuals. By introducing a TVCM formulation of the multivariate normal distribution, the present study extends TVCM to explore change in undirected associations (couplings) and variability, thereby broadening its utility for psychological research. We discuss three versions of this approach: an aggregate-level model and two hierarchical versions capturing interindividual differences in unfolding change, either via person-specific intercepts accounting for onset differences or through fully person-specific coefficient functions smoothed via partial pooling. To illustrate the proposed developments, we apply them to six weeks of intensive longitudinal data from 16 anxiety patients undergoing therapy and examine unfolding changes in the level and volatility of nervousness and threat monitoring, their coupling, as well as between-person heterogeneity in each of these. We further show how inspecting first-order derivatives of the coefficient functions supports identifying periods of stability and change. Finally, we discuss extensions incorporating person-level characteristics to explain heterogeneity in patterns of change and predict outcomes.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-26"},"PeriodicalIF":2.9,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148399776","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 Mixed Multidimensional Scaling for Auditory Processing. 听觉加工的贝叶斯混合多维标度。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-07-02 DOI: 10.1017/psy.2026.10131

Giovanni Rebaudo, Fernando Llanos, Bharath Chandrasekaran, Abhra Sarkar

引用次数: 0

Testing linear hypotheses in repeated measures generalized linear models using external information. 利用外部信息检验重复测量广义线性模型中的线性假设。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-06-25 DOI: 10.1017/psy.2026.10130

Martin Jann, Martin Spiess

引用次数: 0

When Do Unifactorial Items Increase the Reliability? 单因子项目何时能提高可靠性？

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-06-16 DOI: 10.1017/psy.2026.10129

Jules L Ellis, Klaas Sijtsma

引用次数: 0

Longitudinal Designs for Diagnostic Models: Identification and Estimation. 诊断模型的纵向设计：鉴定和估计。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-06-15 DOI: 10.1017/psy.2026.10128

Trung Tran Quoc Le, Steven Andrew Culpepper, Jeff Douglas

{"title":"Longitudinal Designs for Diagnostic Models: Identification and Estimation.","authors":"Trung Tran Quoc Le, Steven Andrew Culpepper, Jeff Douglas","doi":"10.1017/psy.2026.10128","DOIUrl":"10.1017/psy.2026.10128","url":null,"abstract":"Recent studies on cognitive diagnostic models (CDMs) have extended the framework to longitudinal data. Various methods combined CDMs and hidden Markov models (HMMs) to assess changes in attributes over time and to evaluate the effects of interventions and covariates. A requirement for model fitting, inference, and interpretation is that models are identifiable. In this article, we derive identifiability conditions for experimental design used in education research. Specifically, we consider three designs: pretest/posttest single-group design, counterbalancing, and multiple-group longitudinal design. Drawing on existing HMM research, we examine the extent to which these designs satisfy common identifiability assumptions and propose new constraints for the counterbalancing and multiple-group longitudinal designs. These two setups are recommended in situations where item parameters differ over time or when there is heterogeneity in transition patterns across groups. We introduce a general HMM model for the multiple-group longitudinal design and a Gibbs sampling algorithm to estimate the parameters. We assess parameter recovery through a Monte Carlo simulation study and apply the model to a dataset from a study which evaluates the effectiveness of two interventions relative to a control condition. The results demonstrate the flexibility of the model and its potential to offer new insights into learning processes.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-30"},"PeriodicalIF":2.9,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148254429","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