Psychometrika最新文献_第10页

Rotating Factors to Simplify Their Structural Paths. 旋转因子以简化其结构路径。

IF 2.9 2区心理学

Psychometrika Pub Date : 2023-09-01 Epub Date: 2022-07-22 DOI: 10.1007/s11336-022-09877-3

Guangjian Zhang, Minami Hattori, Lauren A Trichtinger

{"title":"Rotating Factors to Simplify Their Structural Paths.","authors":"Guangjian Zhang, Minami Hattori, Lauren A Trichtinger","doi":"10.1007/s11336-022-09877-3","DOIUrl":"10.1007/s11336-022-09877-3","url":null,"abstract":"Applications of structural equation modeling (SEM) may encounter issues like inadmissible parameter estimates, nonconvergence, or unsatisfactory model fit. We propose a new factor rotation method that reparameterizes the factor correlation matrix in exploratory factor analysis (EFA) such that factors can be either exogenous or endogenous. The proposed method is an oblique rotation method for EFA, but it allows directional structural paths among factors. We thus referred it to as FSP (factor structural paths) rotation. In particular, we can use FSP rotation to \"translate\" an SEM model to incorporate theoretical expectations on both factor loadings and structural parameters. We illustrate FSP rotation with an empirical example and explore its statistical properties with simulated data. The results include that (1) EFA with FSP rotation tends to fit data better and encounters fewer Heywood cases than SEM does when there are cross-loadings and many small nonzero loadings, (2) FSP rotated parameter estimates are satisfactory for small models, and (3) FSP rotated parameter estimates are more satisfactory for large models when the structural parameter matrices are sparse.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":"88 3","pages":"865-887"},"PeriodicalIF":2.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10277477","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

Book Review. 书评。

IF 3 2区心理学

Psychometrika Pub Date : 2023-07-06 DOI: 10.1007/s11336-023-09923-8

Youn Seon Lim

引用次数: 0

Book Review 书评

IF 3 2区心理学

Psychometrika Pub Date : 2023-07-01 DOI: 10.1007/s11336-023-09927-4

Ji Seung Yang, Yang Liu, Sungyeun Kim

引用次数: 0

A Test to Distinguish Monotone Homogeneity from Monotone Multifactor Models. 区分单调同质性与单调多因素模型的测试。

IF 2.9 2区心理学

Psychometrika Pub Date : 2023-06-01 Epub Date: 2023-03-18 DOI: 10.1007/s11336-023-09905-w

Jules L Ellis, Klaas Sijtsma

{"title":"A Test to Distinguish Monotone Homogeneity from Monotone Multifactor Models.","authors":"Jules L Ellis, Klaas Sijtsma","doi":"10.1007/s11336-023-09905-w","DOIUrl":"10.1007/s11336-023-09905-w","url":null,"abstract":"The goodness-of-fit of the unidimensional monotone latent variable model can be assessed using the empirical conditions of nonnegative correlations (Mokken in A theory and procedure of scale-analysis, Mouton, The Hague, 1971), manifest monotonicity (Junker in Ann Stat 21:1359-1378, 1993), multivariate total positivity of order 2 (Bartolucci and Forcina in Ann Stat 28:1206-1218, 2000), and nonnegative partial correlations (Ellis in Psychometrika 79:303-316, 2014). We show that multidimensional monotone factor models with independent factors also imply these empirical conditions; therefore, the conditions are insensitive to multidimensionality. Conditional association (Rosenbaum in Psychometrika 49(3):425-435, 1984) can detect multidimensionality, but tests of it (De Gooijer and Yuan in Comput Stat Data Anal 55:34-44, 2011) are usually not feasible for realistic numbers of items. The only existing feasible test procedures that can reveal multidimensionality are Rosenbaum's (Psychometrika 49(3):425-435, 1984) Case 2 and Case 5, which test the covariance of two items or two subtests conditionally on the unweighted sum of the other items. We improve this procedure by conditioning on a weighted sum of the other items. The weights are estimated in a training sample from a linear regression analysis. Simulations show that the Type I error rate is under control and that, for large samples, the power is higher if one dimension is more important than the other or if there is a third dimension. In small samples and with two equally important dimensions, using the unweighted sum yields greater power.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":"88 2","pages":"387-412"},"PeriodicalIF":2.9,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10188426/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9580487","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

Bayesian Inference for an Unknown Number of Attributes in Restricted Latent Class Models. 限制潜类模型中未知属性数量的贝叶斯推断。

IF 2.9 2区心理学

Psychometrika Pub Date : 2023-06-01 Epub Date: 2023-01-22 DOI: 10.1007/s11336-022-09900-7

Yinghan Chen, Steven Andrew Culpepper, Yuguo Chen

{"title":"Bayesian Inference for an Unknown Number of Attributes in Restricted Latent Class Models.","authors":"Yinghan Chen, Steven Andrew Culpepper, Yuguo Chen","doi":"10.1007/s11336-022-09900-7","DOIUrl":"10.1007/s11336-022-09900-7","url":null,"abstract":"The specification of the [Formula: see text] matrix in cognitive diagnosis models is important for correct classification of attribute profiles. Researchers have proposed many methods for estimation and validation of the data-driven [Formula: see text] matrices. However, inference of the number of attributes in the general restricted latent class model remains an open question. We propose a Bayesian framework for general restricted latent class models and use the spike-and-slab prior to avoid the computation issues caused by the varying dimensions of model parameters associated with the number of attributes, K. We develop an efficient Metropolis-within-Gibbs algorithm to estimate K and the corresponding [Formula: see text] matrix simultaneously. The proposed algorithm uses the stick-breaking construction to mimic an Indian buffet process and employs a novel Metropolis-Hastings transition step to encourage exploring the sample space associated with different values of K. We evaluate the performance of the proposed method through a simulation study under different model specifications and apply the method to a real data set related to a fluid intelligence matrix reasoning test.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":"88 2","pages":"613-635"},"PeriodicalIF":2.9,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9641539","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

Advantages of Using Unweighted Approximation Error Measures for Model Fit Assessment. 在模型拟合度评估中使用非加权近似误差测量的优势。

IF 2.9 2区心理学

Psychometrika Pub Date : 2023-06-01 Epub Date: 2023-04-18 DOI: 10.1007/s11336-023-09909-6

Dirk Lubbe

{"title":"Advantages of Using Unweighted Approximation Error Measures for Model Fit Assessment.","authors":"Dirk Lubbe","doi":"10.1007/s11336-023-09909-6","DOIUrl":"10.1007/s11336-023-09909-6","url":null,"abstract":"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 error, the complex weighting function involved in its calculation makes indices derived from it challenging to interpret. Moreover, noncentrality-parameter-based fit indices yield systematically different values, depending on the indicators' level of measurement. For instance, RMSEA and CFI yield more favorable fit indices for models with categorical as compared to metric variables under otherwise identical conditions. In the present article, approaches for obtaining an approximation discrepancy estimate that is independent from any specific weighting function are considered. From these unweighted approximation error estimates, fit indices analogous to RMSEA and CFI are calculated and their finite sample properties are investigated using simulation studies. The results illustrate that the new fit indices consistently estimate their true value which, in contrast to other fit indices, is the same value for metric and categorical variables. Advantages with respect to interpretability are discussed and cutoff criteria for the new indices are considered.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":"88 2","pages":"413-433"},"PeriodicalIF":2.9,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10188575/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9593162","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

Identifiability of Hidden Markov Models for Learning Trajectories in Cognitive Diagnosis. 认知诊断中学习轨迹的隐马尔可夫模型的可识别性。

IF 2.9 2区心理学

Psychometrika Pub Date : 2023-06-01 Epub Date: 2023-02-16 DOI: 10.1007/s11336-023-09904-x

Ying Liu, Steven Andrew Culpepper, Yuguo Chen

引用次数: 0

A Tensor-EM Method for Large-Scale Latent Class Analysis with Binary Responses. 用于二元响应大规模潜类分析的张量-EM 方法

IF 2.9 2区心理学

Psychometrika Pub Date : 2023-06-01 Epub Date: 2022-10-01 DOI: 10.1007/s11336-022-09887-1

Zhenghao Zeng, Yuqi Gu, Gongjun Xu

{"title":"A Tensor-EM Method for Large-Scale Latent Class Analysis with Binary Responses.","authors":"Zhenghao Zeng, Yuqi Gu, Gongjun Xu","doi":"10.1007/s11336-022-09887-1","DOIUrl":"10.1007/s11336-022-09887-1","url":null,"abstract":"Latent class models are powerful statistical modeling tools widely used in psychological, behavioral, and social sciences. In the modern era of data science, researchers often have access to response data collected from large-scale surveys or assessments, featuring many items (large J) and many subjects (large N). This is in contrary to the traditional regime with fixed J and large N. To analyze such large-scale data, it is important to develop methods that are both computationally efficient and theoretically valid. In terms of computation, the conventional EM algorithm for latent class models tends to have a slow algorithmic convergence rate for large-scale data and may converge to some local optima instead of the maximum likelihood estimator (MLE). Motivated by this, we introduce the tensor decomposition perspective into latent class analysis with binary responses. Methodologically, we propose to use a moment-based tensor power method in the first step and then use the obtained estimates as initialization for the EM algorithm in the second step. Theoretically, we establish the clustering consistency of the MLE in assigning subjects into latent classes when N and J both go to infinity. Simulation studies suggest that the proposed tensor-EM pipeline enjoys both good accuracy and computational efficiency for large-scale data with binary responses. We also apply the proposed method to an educational assessment dataset as an illustration.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":"88 2","pages":"580-612"},"PeriodicalIF":2.9,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9579478","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

Longitudinal Modeling of Age-Dependent Latent Traits with Generalized Additive Latent and Mixed Models. 用广义加性潜模型和混合模型建立年龄相关潜特征的纵向模型

IF 2.9 2区心理学

Psychometrika Pub Date : 2023-06-01 Epub Date: 2023-03-28 DOI: 10.1007/s11336-023-09910-z

Øystein Sørensen, Anders M Fjell, Kristine B Walhovd

{"title":"Longitudinal Modeling of Age-Dependent Latent Traits with Generalized Additive Latent and Mixed Models.","authors":"Øystein Sørensen, Anders M Fjell, Kristine B Walhovd","doi":"10.1007/s11336-023-09910-z","DOIUrl":"10.1007/s11336-023-09910-z","url":null,"abstract":"We present generalized additive latent and mixed models (GALAMMs) for analysis of clustered data with responses and latent variables depending smoothly on observed variables. A scalable maximum likelihood estimation algorithm is proposed, utilizing the Laplace approximation, sparse matrix computation, and automatic differentiation. Mixed response types, heteroscedasticity, and crossed random effects are naturally incorporated into the framework. The models developed were motivated by applications in cognitive neuroscience, and two case studies are presented. First, we show how GALAMMs can jointly model the complex lifespan trajectories of episodic memory, working memory, and speed/executive function, measured by the California Verbal Learning Test (CVLT), digit span tests, and Stroop tests, respectively. Next, we study the effect of socioeconomic status on brain structure, using data on education and income together with hippocampal volumes estimated by magnetic resonance imaging. By combining semiparametric estimation with latent variable modeling, GALAMMs allow a more realistic representation of how brain and cognition vary across the lifespan, while simultaneously estimating latent traits from measured items. Simulation experiments suggest that model estimates are accurate even with moderate sample sizes.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":"88 2","pages":"456-486"},"PeriodicalIF":2.9,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10188428/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10299581","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

Rotation to Sparse Loadings Using L p Losses and Related Inference Problems. 使用[公式：见正文]损失和相关推理问题对稀疏载荷进行旋转。

IF 2.9 2区心理学

Psychometrika Pub Date : 2023-06-01 Epub Date: 2023-03-31 DOI: 10.1007/s11336-023-09911-y

Xinyi Liu, Gabriel Wallin, Yunxiao Chen, Irini Moustaki

{"title":"<ArticleTitle xmlns:ns0=\"http://www.w3.org/1998/Math/MathML\">Rotation to Sparse Loadings Using <ns0:math>\u0000 <ns0:msup>\u0000 <ns0:mi>L</ns0:mi>\u0000 <ns0:mi>p</ns0:mi>\u0000 </ns0:msup>\u0000</ns0:math> Losses and Related Inference Problems.","authors":"Xinyi Liu, Gabriel Wallin, Yunxiao Chen, Irini Moustaki","doi":"10.1007/s11336-023-09911-y","DOIUrl":"10.1007/s11336-023-09911-y","url":null,"abstract":"Researchers have widely used exploratory factor analysis (EFA) to learn the latent structure underlying multivariate data. Rotation and regularised estimation are two classes of methods in EFA that they often use to find interpretable loading matrices. In this paper, we propose a new family of oblique rotations based on component-wise <math>\u0000 <msup>\u0000 <mi>L</mi>\u0000 <mi>p</mi>\u0000 </msup>\u0000</math> loss functions <math>\u0000 <mrow>\u0000 <mo>(</mo>\u0000 <mrow>\u0000 <mn>0</mn>\u0000 <mo><</mo>\u0000 <mi>p</mi>\u0000 <mo>≤</mo>\u0000 <mn>1</mn>\u0000 </mrow>\u0000 <mo>)</mo>\u0000 </mrow>\u0000</math> that is closely related to an <math>\u0000 <msup>\u0000 <mi>L</mi>\u0000 <mi>p</mi>\u0000 </msup>\u0000</math> regularised estimator. We develop model selection and post-selection inference procedures based on the proposed rotation method. When the true loading matrix is sparse, the proposed method tends to outperform traditional rotation and regularised estimation methods in terms of statistical accuracy and computational cost. Since the proposed loss functions are nonsmooth, we develop an iteratively reweighted gradient projection algorithm for solving the optimisation problem. We also develop theoretical results that establish the statistical consistency of the estimation, model selection, and post-selection inference. We evaluate the proposed method and compare it with regularised estimation and traditional rotation methods via simulation studies. We further illustrate it using an application to the Big Five personality assessment.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":"88 2","pages":"527-553"},"PeriodicalIF":2.9,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10188427/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9587347","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