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Hwang, Heungsun – Psychometrika, 2009

Generalized structured component analysis (GSCA) has been proposed as a component-based approach to structural equation modeling. In practice, GSCA may suffer from multi-collinearity, i.e., high correlations among exogenous variables. GSCA has yet no remedy for this problem. Thus, a regularized extension of GSCA is proposed that integrates a ridge…

Descriptors: Monte Carlo Methods, Structural Equation Models, Least Squares Statistics, Computation

van den Heuvel-Panhuizen, Marja; Robitzsch, Alexander; Treffers, Adri; Koller, Olaf – Psychometrika, 2009

This article discusses large-scale assessment of change in student achievement and takes the study by Hickendorff, Heiser, Van Putten, and Verhelst (2009) as an example. This study compared the achievement of students in the Netherlands in 1997 and 2004 on written division problems. Based on this comparison, they claim that there is a performance…

Descriptors: Academic Achievement, Measures (Individuals), Program Effectiveness, Foreign Countries

Chang, Hua-Hua; Ying, Zhiliang – Psychometrika, 2008

It has been widely reported that in computerized adaptive testing some examinees may get much lower scores than they would normally if an alternative paper-and-pencil version were given. The main purpose of this investigation is to quantitatively reveal the cause for the underestimation phenomenon. The logistic models, including the 1PL, 2PL, and…

Descriptors: Adaptive Testing, Computer Assisted Testing, Computation, Test Items

Wilderjans, Tom; Ceulemans, Eva; Van Mechelen, Iven – Psychometrika, 2008

Often problems result in the collection of coupled data, which consist of different N-way N-mode data blocks that have one or more modes in common. To reveal the structure underlying such data, an integrated modeling strategy, with a single set of parameters for the common mode(s), that is estimated based on the information in all data blocks, may…

Descriptors: Test Items, Simulation, Item Response Theory, Models

Steinley, Douglas; Brusco, Michael J. – Psychometrika, 2008

Eight different variable selection techniques for model-based and non-model-based clustering are evaluated across a wide range of cluster structures. It is shown that several methods have difficulties when non-informative variables (i.e., random noise) are included in the model. Furthermore, the distribution of the random noise greatly impacts the…

Descriptors: Models, Comparative Analysis, Multivariate Analysis, Evaluation Methods

Takane, Yoshio; Jung, Sunho – Psychometrika, 2008

Methods of incorporating a ridge type of regularization into partial redundancy analysis (PRA), constrained redundancy analysis (CRA), and partial and constrained redundancy analysis (PCRA) were discussed. The usefulness of ridge estimation in reducing mean square error (MSE) has been recognized in multiple regression analysis for some time,…

Descriptors: Predictor Variables, Multiple Regression Analysis, Least Squares Statistics, Data Analysis

Ceulemans, Eva; Van Mechelen, Iven; Leenen, Iwin – Psychometrika, 2007

Hierarchical classes models are quasi-order retaining Boolean decomposition models for N-way N-mode binary data. To fit these models to data, rationally started alternating least squares (or, equivalently, alternating least absolute deviations) algorithms have been proposed. Extensive simulation studies showed that these algorithms succeed quite…

Descriptors: Least Squares Statistics, Data Analysis, Mathematics, Item Response Theory

Bollen, Kenneth A.; Maydeu-Olivares, Albert – Psychometrika, 2007

This paper presents a new polychoric instrumental variable (PIV) estimator to use in structural equation models (SEMs) with categorical observed variables. The PIV estimator is a generalization of Bollen's (Psychometrika 61:109-121, 1996) 2SLS/IV estimator for continuous variables to categorical endogenous variables. We derive the PIV estimator…

Descriptors: Structural Equation Models, Simulation, Robustness (Statistics), Computation

Hoshino, Takahiro – Psychometrika, 2007

Due to the difficulty in achieving a random assignment, a quasi-experimental or observational study design is frequently used in the behavioral and social sciences. If a nonrandom assignment depends on the covariates, multiple group structural equation modeling, that includes the regression function of the dependent variables on the covariates…

Descriptors: Structural Equation Models, Simulation, Observation, Behavioral Science Research

Brusco, Michael J.; Steinley, Douglas – Psychometrika, 2007

Perhaps the most common criterion for partitioning a data set is the minimization of the within-cluster sums of squared deviation from cluster centroids. Although optimal solution procedures for within-cluster sums of squares (WCSS) partitioning are computationally feasible for small data sets, heuristic procedures are required for most practical…

Descriptors: Heuristics, Behavioral Sciences, Mathematics, Item Response Theory

Millsap, Roger E. – Psychometrika, 2007

Borsboom (Psychometrika, 71:425-440, 2006) noted that recent work on measurement invariance (MI) and predictive invariance (PI) has had little impact on the practice of measurement in psychology. To understand this contention, the definitions of MI and PI are reviewed, followed by results on the consistency between the two forms of invariance in…

Descriptors: Measurement Techniques, Regression (Statistics), Factor Analysis, Prediction

Van Mechelen, Iven; Lombardi, Luigi; Ceulemans, Eva – Psychometrika, 2007

Hierarchical classes (HICLAS) models constitute a distinct family of structural models for N-way N-mode data. All members of the family include N simultaneous and linked classifications of the elements of the N modes implied by the data; those classifications are organized in terms of hierarchical, if-then-type relations. Moreover, the models are…

Descriptors: Structural Equation Models, Data Analysis, Classification, Visual Stimuli

Kim, Jee-Seon; Frees, Edward W. – Psychometrika, 2007

When there exist omitted effects, measurement error, and/or simultaneity in multilevel models, explanatory variables may be correlated with random components, and standard estimation methods do not provide consistent estimates of model parameters. This paper introduces estimators that are consistent under such conditions. By employing generalized…

Descriptors: Simulation, Measurement, Error of Measurement, Computation

Haberman, Shelby J.; Holland, Paul W.; Sinharay, Sandip – Psychometrika, 2007

Bounds are established for log odds ratios (log cross-product ratios) involving pairs of items for item response models. First, expressions for bounds on log odds ratios are provided for one-dimensional item response models in general. Then, explicit bounds are obtained for the Rasch model and the two-parameter logistic (2PL) model. Results are…

Descriptors: Goodness of Fit, Item Response Theory, Research Methodology, Measurement Techniques

Hwang, Heungsun; Montreal, Hec; Dillon, William R.; Takane, Yoshio – Psychometrika, 2006

An extension of multiple correspondence analysis is proposed that takes into account cluster-level heterogeneity in respondents' preferences/choices. The method involves combining multiple correspondence analysis and k-means in a unified framework. The former is used for uncovering a low-dimensional space of multivariate categorical variables…

Descriptors: Heterogeneous Grouping, Multivariate Analysis, Models