Psychometrika最新文献

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

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

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

引用次数: 0

Consistency Theory of General Nonparametric Classification Methods in Cognitive Diagnosis. 认知诊断中一般非参数分类方法的一致性理论。

IF 2.9 2区心理学

Psychometrika Pub Date : 2025-03-17 DOI: 10.1017/psy.2025.9

Chengyu Cui, Yanlong Liu, Gongjun Xu

引用次数: 0

Optimizing Large-Scale Educational Assessment with a "Divide-and-Conquer" Strategy: Fast and Efficient Distributed Bayesian Inference in IRT Models. 用 "分而治之 "策略优化大规模教育评估：快速高效的 IRT 模型分布式贝叶斯推理。

IF 2.9 2区心理学

Psychometrika Pub Date : 2024-12-01 Epub Date: 2024-05-30 DOI: 10.1007/s11336-024-09978-1

Sainan Xu, Jing Lu, Jiwei Zhang, Chun Wang, Gongjun Xu

{"title":"Optimizing Large-Scale Educational Assessment with a \"Divide-and-Conquer\" Strategy: Fast and Efficient Distributed Bayesian Inference in IRT Models.","authors":"Sainan Xu, Jing Lu, Jiwei Zhang, Chun Wang, Gongjun Xu","doi":"10.1007/s11336-024-09978-1","DOIUrl":"10.1007/s11336-024-09978-1","url":null,"abstract":"With the growing attention on large-scale educational testing and assessment, the ability to process substantial volumes of response data becomes crucial. Current estimation methods within item response theory (IRT), despite their high precision, often pose considerable computational burdens with large-scale data, leading to reduced computational speed. This study introduces a novel \"divide- and-conquer\" parallel algorithm built on the Wasserstein posterior approximation concept, aiming to enhance computational speed while maintaining accurate parameter estimation. This algorithm enables drawing parameters from segmented data subsets in parallel, followed by an amalgamation of these parameters via Wasserstein posterior approximation. Theoretical support for the algorithm is established through asymptotic optimality under certain regularity assumptions. Practical validation is demonstrated using real-world data from the Programme for International Student Assessment. Ultimately, this research proposes a transformative approach to managing educational big data, offering a scalable, efficient, and precise alternative that promises to redefine traditional practices in educational assessments.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1119-1147"},"PeriodicalIF":2.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141176735","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

Are Sum Scores a Great Accomplishment of Psychometrics or Intuitive Test Theory? 总分是心理测量学还是直觉测验理论的伟大成就？

IF 2.9 2区心理学

Psychometrika Pub Date : 2024-12-01 Epub Date: 2024-10-22 DOI: 10.1007/s11336-024-10003-8

Robert J Mislevy

引用次数: 0

New Paradigm of Identifiable General-response Cognitive Diagnostic Models: Beyond Categorical Data. 可识别的一般反应认知诊断模型新范例：超越分类数据

IF 2.9 2区心理学

Psychometrika Pub Date : 2024-12-01 Epub Date: 2024-07-05 DOI: 10.1007/s11336-024-09983-4

Seunghyun Lee, Yuqi Gu

{"title":"New Paradigm of Identifiable General-response Cognitive Diagnostic Models: Beyond Categorical Data.","authors":"Seunghyun Lee, Yuqi Gu","doi":"10.1007/s11336-024-09983-4","DOIUrl":"10.1007/s11336-024-09983-4","url":null,"abstract":"Cognitive diagnostic models (CDMs) are a popular family of discrete latent variable models that model students' mastery or deficiency of multiple fine-grained skills. CDMs have been most widely used to model categorical item response data such as binary or polytomous responses. With advances in technology and the emergence of varying test formats in modern educational assessments, new response types, including continuous responses such as response times, and count-valued responses from tests with repetitive tasks or eye-tracking sensors, have also become available. Variants of CDMs have been proposed recently for modeling such responses. However, whether these extended CDMs are identifiable and estimable is entirely unknown. We propose a very general cognitive diagnostic modeling framework for arbitrary types of multivariate responses with minimal assumptions, and establish identifiability in this general setting. Surprisingly, we prove that our general-response CDMs are identifiable under <math><mi>Q</mi></math> -matrix-based conditions similar to those for traditional categorical-response CDMs. Our conclusions set up a new paradigm of identifiable general-response CDMs. We propose an EM algorithm to efficiently estimate a broad class of exponential family-based general-response CDMs. We conduct simulation studies under various response types. The simulation results not only corroborate our identifiability theory, but also demonstrate the superior empirical performance of our estimation algorithms. We illustrate our methodology by applying it to a TIMSS 2019 response time dataset.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1304-1336"},"PeriodicalIF":2.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141535981","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

Ordinal Outcome State-Space Models for Intensive Longitudinal Data. 用于密集纵向数据的序数结果状态空间模型。

IF 2.9 2区心理学

Psychometrika Pub Date : 2024-12-01 Epub Date: 2024-06-11 DOI: 10.1007/s11336-024-09984-3

Teague R Henry, Lindley R Slipetz, Ami Falk, Jiaxing Qiu, Meng Chen

{"title":"Ordinal Outcome State-Space Models for Intensive Longitudinal Data.","authors":"Teague R Henry, Lindley R Slipetz, Ami Falk, Jiaxing Qiu, Meng Chen","doi":"10.1007/s11336-024-09984-3","DOIUrl":"10.1007/s11336-024-09984-3","url":null,"abstract":"Intensive longitudinal (IL) data are increasingly prevalent in psychological science, coinciding with technological advancements that make it simple to deploy study designs such as daily diary and ecological momentary assessments. IL data are characterized by a rapid rate of data collection (1+ collections per day), over a period of time, allowing for the capture of the dynamics that underlie psychological and behavioral processes. One powerful framework for analyzing IL data is state-space modeling, where observed variables are considered measurements for underlying states (i.e., latent variables) that change together over time. However, state-space modeling has typically relied on continuous measurements, whereas psychological data often come in the form of ordinal measurements such as Likert scale items. In this manuscript, we develop a general estimation approach for state-space models with ordinal measurements, specifically focusing on a graded response model for Likert scale items. We evaluate the performance of our model and estimator against that of the commonly used \"linear approximation\" model, which treats ordinal measurements as though they are continuous. We find that our model resulted in unbiased estimates of the state dynamics, while the linear approximation resulted in strongly biased estimates of the state dynamics. Finally, we develop an approximate standard error, termed slice standard errors and show that these approximate standard errors are more liberal than true standard errors (i.e., smaller) at a consistent bias.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1203-1229"},"PeriodicalIF":2.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11582181/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141302095","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 Note on Ising Network Analysis with Missing Data. 关于缺失数据的 Ising 网络分析的说明。

IF 2.9 2区心理学

Psychometrika Pub Date : 2024-12-01 Epub Date: 2024-07-06 DOI: 10.1007/s11336-024-09985-2

Siliang Zhang, Yunxiao Chen

{"title":"A Note on Ising Network Analysis with Missing Data.","authors":"Siliang Zhang, Yunxiao Chen","doi":"10.1007/s11336-024-09985-2","DOIUrl":"10.1007/s11336-024-09985-2","url":null,"abstract":"The Ising model has become a popular psychometric model for analyzing item response data. The statistical inference of the Ising model is typically carried out via a pseudo-likelihood, as the standard likelihood approach suffers from a high computational cost when there are many variables (i.e., items). Unfortunately, the presence of missing values can hinder the use of pseudo-likelihood, and a listwise deletion approach for missing data treatment may introduce a substantial bias into the estimation and sometimes yield misleading interpretations. This paper proposes a conditional Bayesian framework for Ising network analysis with missing data, which integrates a pseudo-likelihood approach with iterative data imputation. An asymptotic theory is established for the method. Furthermore, a computationally efficient Pólya-Gamma data augmentation procedure is proposed to streamline the sampling of model parameters. The method's performance is shown through simulations and a real-world application to data on major depressive and generalized anxiety disorders from the National Epidemiological Survey on Alcohol and Related Conditions (NESARC).","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1186-1202"},"PeriodicalIF":2.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11582142/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141545557","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

Practical Implications of Sum Scores Being Psychometrics' Greatest Accomplishment. 总分是心理测量学最大成就的实际意义。

IF 2.9 2区心理学

Psychometrika Pub Date : 2024-12-01 Epub Date: 2024-07-20 DOI: 10.1007/s11336-024-09988-z

Daniel McNeish

{"title":"Practical Implications of Sum Scores Being Psychometrics' Greatest Accomplishment.","authors":"Daniel McNeish","doi":"10.1007/s11336-024-09988-z","DOIUrl":"10.1007/s11336-024-09988-z","url":null,"abstract":"This paper reflects on some practical implications of the excellent treatment of sum scoring and classical test theory (CTT) by Sijtsma et al. (Psychometrika 89(1):84-117, 2024). I have no major disagreements about the content they present and found it to be an informative clarification of the properties and possible extensions of CTT. In this paper, I focus on whether sum scores-despite their mathematical justification-are positioned to improve psychometric practice in empirical studies in psychology, education, and adjacent areas. First, I summarize recent reviews of psychometric practice in empirical studies, subsequent calls for greater psychometric transparency and validity, and how sum scores may or may not be positioned to adhere to such calls. Second, I consider limitations of sum scores for prediction, especially in the presence of common features like ordinal or Likert response scales, multidimensional constructs, and moderated or heterogeneous associations. Third, I review previous research outlining potential limitations of using sum scores as outcomes in subsequent analyses where rank ordering is not always sufficient to successfully characterize group differences or change over time. Fourth, I cover potential challenges for providing validity evidence for whether sum scores represent a single construct, particularly if one wishes to maintain minimal CTT assumptions. I conclude with thoughts about whether sum scores-even if mathematically justified-are positioned to improve psychometric practice in empirical studies.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1148-1169"},"PeriodicalIF":2.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141731649","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