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Yu-Wei Chang, Nan-Jung Hsu, Rung-Ching Tsai
The multiple-group categorical factor analysis (FA) model and the graded response model (GRM) are commonly used to examine polytomous items for differential item functioning to detect possible measurement bias in educational testing. In this study, the multiple-group categorical factor analysis model (MC-FA) and multiple-group normal-ogive GRM models are unified under the common framework of discretization of a normal variant. We rigorously justify a set of identified parameters and determine possible identifiability constraints necessary to make the parameters just-identified and estimable in the common framework of MC-FA...
February 17, 2017: Psychometrika
Ji Yeh Choi, Heungsun Hwang, Marieke E Timmerman
Parallel factor analysis (PARAFAC) is a useful multivariate method for decomposing three-way data that consist of three different types of entities simultaneously. This method estimates trilinear components, each of which is a low-dimensional representation of a set of entities, often called a mode, to explain the maximum variance of the data. Functional PARAFAC permits the entities in different modes to be smooth functions or curves, varying over a continuum, rather than a collection of unconnected responses...
February 14, 2017: Psychometrika
J Fernando Vera, Rodrigo Macías
One of the main problems in cluster analysis is that of determining the number of groups in the data. In general, the approach taken depends on the cluster method used. For K-means, some of the most widely employed criteria are formulated in terms of the decomposition of the total point scatter, regarding a two-mode data set of N points in p dimensions, which are optimally arranged into K classes. This paper addresses the formulation of criteria to determine the number of clusters, in the general situation in which the available information for clustering is a one-mode [Formula: see text] dissimilarity matrix describing the objects...
February 13, 2017: Psychometrika
Alberto Maydeu-Olivares
When a statistically significant mean difference is found, the magnitude of the difference is judged qualitatively using an effect size such as Cohen's d. In contrast, in a structural equation model (SEM), the result of the statistical test of model fit is often disregarded if significant, and inferences are drawn using "close" models retained based on point estimates of sample statistics (goodness-of-fit indices). However, when a SEM cannot be retained using a test of exact fit, all substantive inferences drawn from it are suspect...
February 7, 2017: Psychometrika
Wim J van der Linden, Michelle D Barrett
No abstract text is available yet for this article.
January 23, 2017: Psychometrika
Steffen Nestler, Katharina Geukes, Roos Hutteman, Mitja D Back
The social relations model (SRM) is commonly used in the analysis of interpersonal judgments and behaviors that arise in groups. The SRM was developed only for use with cross-sectional data. Here, we introduce an extension of the SRM to longitudinal data. The social relations growth model represents a person's repeated SRM judgments of another person as a function of time. We show how the model's parameters can be estimated using restricted maximum likelihood, and how the effects of covariates on interindividual and interdyad variability in growth can be computed...
December 6, 2016: Psychometrika
Yunxiao Chen, Xiaoou Li, Jingchen Liu, Zhiliang Ying
Diagnostic classification models are confirmatory in the sense that the relationship between the latent attributes and responses to items is specified or parameterized. Such models are readily interpretable with each component of the model usually having a practical meaning. However, parameterized diagnostic classification models are sometimes too simple to capture all the data patterns, resulting in significant model lack of fit. In this paper, we attempt to obtain a compromise between interpretability and goodness of fit by regularizing a latent class model...
November 30, 2016: Psychometrika
Tom Frans Wilderjans, Eva Vande Gaer, Henk A L Kiers, Iven Van Mechelen, Eva Ceulemans
In the behavioral sciences, many research questions pertain to a regression problem in that one wants to predict a criterion on the basis of a number of predictors. Although in many cases, ordinary least squares regression will suffice, sometimes the prediction problem is more challenging, for three reasons: first, multiple highly collinear predictors can be available, making it difficult to grasp their mutual relations as well as their relations to the criterion. In that case, it may be very useful to reduce the predictors to a few summary variables, on which one regresses the criterion and which at the same time yields insight into the predictor structure...
November 30, 2016: Psychometrika
Tobias Koch, Martin Schultze, Jana Holtmann, Christian Geiser, Michael Eid
A new multiple indicator multilevel latent state-trait (LST) model for the analysis of multitrait-multimethod-multioccasion (MTMM-MO) data is proposed. The LST-COM model combines current CFA-MTMM modeling approaches of interchangeable and structurally different methods and LST modeling approaches. The model enables researchers to specify construct and method factors on the level of time-stable (trait) as well as time-variable (occasion-specific) latent variables and analyze the convergent and discriminant validity among different rater groups across time...
November 30, 2016: Psychometrika
(no author information available yet)
No abstract text is available yet for this article.
December 2016: Psychometrika
Willem Heiser, Lawrence Hubert
A review is provided for the creation of the Psychometric Society in 1935, and the establishment of its journal, Psychometrika, in 1936. This document is part of the 80th anniversary celebration for Psychometrika's founding, held during the annual meeting of the Psychometric Society in July of 2016 in Asheville, NC.
December 2016: Psychometrika
Willem Heiser, Lawrence Hubert
No abstract text is available yet for this article.
December 2016: Psychometrika
Hao Wu, Michael W Browne
No abstract text is available yet for this article.
December 2016: Psychometrika
Chia-Yi Chiu, Hans-Friedrich Köhn, Yi Zheng, Robert Henson
Joint maximum likelihood estimation (JMLE) is developed for diagnostic classification models (DCMs). JMLE has been barely used in Psychometrics because JMLE parameter estimators typically lack statistical consistency. The JMLE procedure presented here resolves the consistency issue by incorporating an external, statistically consistent estimator of examinees' proficiency class membership into the joint likelihood function, which subsequently allows for the construction of item parameter estimators that also have the consistency property...
December 2016: Psychometrika
Peter M Bentler
Classical test theory reliability coefficients are said to be population specific. Reliability generalization, a meta-analysis method, is the main procedure for evaluating the stability of reliability coefficients across populations. A new approach is developed to evaluate the degree of invariance of reliability coefficients to population characteristics. Factor or common variance of a reliability measure is partitioned into parts that are, and are not, influenced by control variables, resulting in a partition of reliability into a covariate-dependent and a covariate-free part...
December 2016: Psychometrika
Myrsini Katsikatsou, Irini Moustaki
Correlated multivariate ordinal data can be analysed with structural equation models. Parameter estimation has been tackled in the literature using limited-information methods including three-stage least squares and pseudo-likelihood estimation methods such as pairwise maximum likelihood estimation. In this paper, two likelihood ratio test statistics and their asymptotic distributions are derived for testing overall goodness-of-fit and nested models, respectively, under the estimation framework of pairwise maximum likelihood estimation...
December 2016: Psychometrika
Hye Won Suk, Heungsun Hwang
An extension of Generalized Structured Component Analysis (GSCA), called Functional GSCA, is proposed to analyze functional data that are considered to arise from an underlying smooth curve varying over time or other continua. GSCA has been geared for the analysis of multivariate data. Accordingly, it cannot deal with functional data that often involve different measurement occasions across participants and a large number of measurement occasions that exceed the number of participants. Functional GSCA addresses these issues by integrating GSCA with spline basis function expansions that represent infinite-dimensional curves onto a finite-dimensional space...
December 2016: Psychometrika
Jianan Sun, Yunxiao Chen, Jingchen Liu, Zhiliang Ying, Tao Xin
We develop a latent variable selection method for multidimensional item response theory models. The proposed method identifies latent traits probed by items of a multidimensional test. Its basic strategy is to impose an [Formula: see text] penalty term to the log-likelihood. The computation is carried out by the expectation-maximization algorithm combined with the coordinate descent algorithm. Simulation studies show that the resulting estimator provides an effective way in correctly identifying the latent structures...
December 2016: Psychometrika
Michael D Hunter
Generalized orthogonal linear derivative (GOLD) estimates were proposed to correct a problem of correlated estimation errors in generalized local linear approximation (GLLA). This paper shows that GOLD estimates are related to GLLA estimates by the Gram-Schmidt orthogonalization process. Analytical work suggests that GLLA estimates are derivatives of an approximating polynomial and GOLD estimates are linear combinations of these derivatives. A series of simulation studies then further investigates and tests the analytical properties derived...
December 2016: Psychometrika
Hao Wu
In this note, we prove that the 3 parameter logistic model with fixed-effect abilities is identified only up to a linear transformation of the ability scale under mild regularity conditions, contrary to the claims in Theorem 2 of San Martín et al. (Psychometrika, 80(2):450-467, 2015a).
December 2016: Psychometrika
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