Psychometrika最新文献_第5页

Learning Bayesian Networks: A Copula Approach for Mixed-Type Data 学习贝叶斯网络：混合类型数据的 Copula 方法

IF 3 2区心理学

Psychometrika Pub Date : 2024-04-12 DOI: 10.1007/s11336-024-09969-2

Federico Castelletti

{"title":"Learning Bayesian Networks: A Copula Approach for Mixed-Type Data","authors":"Federico Castelletti","doi":"10.1007/s11336-024-09969-2","DOIUrl":"https://doi.org/10.1007/s11336-024-09969-2","url":null,"abstract":"Estimating dependence relationships between variables is a crucial issue in many applied domains and in particular psychology. When several variables are entertained, these can be organized into a network which encodes their set of conditional dependence relations. Typically however, the underlying network structure is completely unknown or can be partially drawn only; accordingly it should be learned from the available data, a process known as structure learning. In addition, data arising from social and psychological studies are often of different types, as they can include categorical, discrete and continuous measurements. In this paper, we develop a novel Bayesian methodology for structure learning of directed networks which applies to mixed data, i.e., possibly containing continuous, discrete, ordinal and binary variables simultaneously. Whenever available, our method can easily incorporate known dependence structures among variables represented by paths or edge directions that can be postulated in advance based on the specific problem under consideration. We evaluate the proposed method through extensive simulation studies, with appreciable performances in comparison with current state-of-the-art alternative methods. Finally, we apply our methodology to well-being data from a social survey promoted by the United Nations, and mental health data collected from a cohort of medical students. R code implementing the proposed methodology is available at https://github.com/FedeCastelletti/bayes_networks_mixed_data.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":"2014 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140569609","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

Polytomous Effectiveness Indicators in Complex Problem-Solving Tasks and Their Applications in Developing Measurement Model 复杂问题解决任务中的多项式有效性指标及其在开发测量模型中的应用

IF 3 2区心理学

Psychometrika Pub Date : 2024-04-09 DOI: 10.1007/s11336-024-09963-8

Pujue Wang, Hongyun Liu

{"title":"Polytomous Effectiveness Indicators in Complex Problem-Solving Tasks and Their Applications in Developing Measurement Model","authors":"Pujue Wang, Hongyun Liu","doi":"10.1007/s11336-024-09963-8","DOIUrl":"https://doi.org/10.1007/s11336-024-09963-8","url":null,"abstract":"Recent years have witnessed the emergence of measurement models for analyzing action sequences in computer-based problem-solving interactive tasks. The cutting-edge psychometrics process models require pre-specification of the effectiveness of state transitions often simplifying them into dichotomous indicators. However, the dichotomous effectiveness becomes impractical when dealing with complex tasks that involve multiple optimal paths and numerous state transitions. Building on the concept of problem-solving, we introduce polytomous indicators to assess the effectiveness of problem states (d_{s}) and state-to-state transitions ({mathrm {Delta }d}_{mathrm {srightarrow s'}}). The three-step evaluation method for these two types of indicators is proposed and illustrated across two real problem-solving tasks. We further present a novel psychometrics process model, the sequential response model with polytomous effectiveness indicators (SRM-PEI), which is tailored to encompass a broader range of problem-solving tasks. Monte Carlo simulations indicated that SRM-PEI performed well in the estimation of latent ability and transition tendency parameters across different conditions. Empirical studies conducted on two real tasks supported the better fit of SRM-PEI over previous models such as SRM and SRMM, providing rational and interpretable estimates of latent abilities and transition tendencies through effectiveness indicators. The paper concludes by outlining potential avenues for the further application and enhancement of polytomous effectiveness indicators and SRM-PEI.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":"81 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140569211","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

Examining Differential Item Functioning from a Multidimensional IRT Perspective 从多维 IRT 角度研究差异项目功能

IF 3 2区心理学

Psychometrika Pub Date : 2024-04-05 DOI: 10.1007/s11336-024-09965-6

Terry A. Ackerman, Ye Ma

{"title":"Examining Differential Item Functioning from a Multidimensional IRT Perspective","authors":"Terry A. Ackerman, Ye Ma","doi":"10.1007/s11336-024-09965-6","DOIUrl":"https://doi.org/10.1007/s11336-024-09965-6","url":null,"abstract":"Differential item functioning (DIF) is a standard analysis for every testing company. Research has demonstrated that DIF can result when test items measure different ability composites, and the groups being examined for DIF exhibit distinct underlying ability distributions on those composite abilities. In this article, we examine DIF from a two-dimensional multidimensional item response theory (MIRT) perspective. We begin by delving into the compensatory MIRT model, illustrating and how items and the composites they measure can be graphically represented. Additionally, we discuss how estimated item parameters can vary based on the underlying latent ability distributions of the examinees. Analytical research highlighting the consequences of ignoring dimensionally and applying unidimensional IRT models, where the two-dimensional latent space is mapped onto a unidimensional, is reviewed. Next, we investigate three different approaches to understanding DIF from a MIRT standpoint: 1. Analytically Uniform and Nonuniform DIF: When two groups of interest have different two-dimensional ability distributions, a unidimensional model is estimated. 2. Accounting for complete latent ability space: We emphasize the importance of considering the entire latent ability space when using DIF conditional approaches, which leads to the mitigation of DIF effects. 3. Scenario-Based DIF: Even when underlying two-dimensional distributions are identical for two groups, differing problem-solving approaches can still lead to DIF. Modern software programs facilitate routine DIF procedures for comparing response data from two identified groups of interest. The real challenge is to identify why DIF could occur with flagged items. Thus, as a closing challenge, we present four items (Appendix A) from a standardized test and invite readers to identify which group was favored by a DIF analysis.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":"50 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140569127","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

Reducing Attenuation Bias in Regression Analyses Involving Rating Scale Data via Psychometric Modeling 通过心理测量建模减少评级量表数据回归分析中的衰减偏差

IF 3 2区心理学

Psychometrika Pub Date : 2024-04-04 DOI: 10.1007/s11336-024-09967-4

Cees A. W. Glas, Terrence D. Jorgensen, Debby ten Hove

引用次数: 0

Sociocognitive and Argumentation Perspectives on Psychometric Modeling in Educational Assessment 从社会认知和论证角度看教育评估中的心理测量建模

IF 3 2区心理学

Psychometrika Pub Date : 2024-04-03 DOI: 10.1007/s11336-024-09966-5

Robert J. Mislevy

引用次数: 0

DIF Analysis with Unknown Groups and Anchor Items. 使用未知组和锚项进行 DIF 分析。

IF 2.9 2区心理学

Psychometrika Pub Date : 2024-03-01 Epub Date: 2024-02-21 DOI: 10.1007/s11336-024-09948-7

Gabriel Wallin, Yunxiao Chen, Irini Moustaki

{"title":"DIF Analysis with Unknown Groups and Anchor Items.","authors":"Gabriel Wallin, Yunxiao Chen, Irini Moustaki","doi":"10.1007/s11336-024-09948-7","DOIUrl":"10.1007/s11336-024-09948-7","url":null,"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. Traditional DIF analysis methods require knowing the comparison groups (reference and focal groups) and anchor items (a subset of DIF-free items). Such prior knowledge may not always be available, and psychometric methods have been proposed for DIF analysis when one piece of information is unknown. More specifically, when the comparison groups are unknown while anchor items are known, latent DIF analysis methods have been proposed that estimate the unknown groups by latent classes. When anchor items are unknown while comparison groups are known, methods have also been proposed, typically under a sparsity assumption - the number of DIF items is not too large. However, DIF analysis when both pieces of information are unknown has not received much attention. This paper proposes a general statistical framework under this setting. In the proposed framework, we model the unknown groups by latent classes and introduce item-specific DIF parameters to capture the DIF effects. Assuming the number of DIF items is relatively small, an <math><msub><mi>L</mi> <mn>1</mn></msub> </math> -regularised estimator is proposed to simultaneously identify the latent classes and the DIF items. A computationally efficient Expectation-Maximisation (EM) algorithm is developed to solve the non-smooth optimisation problem for the regularised estimator. The performance of the proposed method is evaluated by simulation studies and an application to item response data from a real-world educational test.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"267-295"},"PeriodicalIF":2.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11062998/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139934189","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 Multidimensional Model to Facilitate Within Person Comparison of Attributes. 促进人内属性比较的多维模型。

IF 2.9 2区心理学

Psychometrika Pub Date : 2024-03-01 Epub Date: 2024-02-08 DOI: 10.1007/s11336-023-09946-1

Mark L Davison, Seungwon Chung, Nidhi Kohli, Ernest C Davenport

引用次数: 0

Book Review Computational Aspects of Psychometric Methods by Martinková & Hladká. 书评 Martinková & Hladká 著《心理测量方法的计算方面》。

IF 3 2区心理学

Psychometrika Pub Date : 2024-03-01 DOI: 10.1007/s11336-024-09954-9

Zhiqing Lin, Huilin Chen

引用次数: 0

Psychometric Society Meeting of the Members University of Maryland College Park, Maryland July 28, 2023. 马里兰大学学院帕克分校心理测量协会会员会议，马里兰州，2023 年 7 月 28 日。

IF 2.9 2区心理学

Psychometrika Pub Date : 2024-03-01 DOI: 10.1007/s11336-023-09943-4

引用次数: 0

A Note on Improving Variational Estimation for Multidimensional Item Response Theory. 改进多维项目反应理论的变分估计。

IF 2.9 2区心理学

Psychometrika Pub Date : 2024-03-01 Epub Date: 2023-11-18 DOI: 10.1007/s11336-023-09939-0

Chenchen Ma, Jing Ouyang, Chun Wang, Gongjun Xu

{"title":"A Note on Improving Variational Estimation for Multidimensional Item Response Theory.","authors":"Chenchen Ma, Jing Ouyang, Chun Wang, Gongjun Xu","doi":"10.1007/s11336-023-09939-0","DOIUrl":"10.1007/s11336-023-09939-0","url":null,"abstract":"Survey instruments and assessments are frequently used in many domains of social science. When the constructs that these assessments try to measure become multifaceted, multidimensional item response theory (MIRT) provides a unified framework and convenient statistical tool for item analysis, calibration, and scoring. However, the computational challenge of estimating MIRT models prohibits its wide use because many of the extant methods can hardly provide results in a realistic time frame when the number of dimensions, sample size, and test length are large. Instead, variational estimation methods, such as Gaussian variational expectation-maximization (GVEM) algorithm, have been recently proposed to solve the estimation challenge by providing a fast and accurate solution. However, results have shown that variational estimation methods may produce some bias on discrimination parameters during confirmatory model estimation, and this note proposes an importance-weighted version of GVEM (i.e., IW-GVEM) to correct for such bias under MIRT models. We also use the adaptive moment estimation method to update the learning rate for gradient descent automatically. Our simulations show that IW-GVEM can effectively correct bias with modest increase of computation time, compared with GVEM. The proposed method may also shed light on improving the variational estimation for other psychometrics models.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"172-204"},"PeriodicalIF":2.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136400354","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