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Maydeu-Olivares, Alberto; Brown, Anna – Multivariate Behavioral Research, 2010
The comparative format used in ranking and paired comparisons tasks can significantly reduce the impact of uniform response biases typically associated with rating scales. Thurstone's (1927, 1931) model provides a powerful framework for modeling comparative data such as paired comparisons and rankings. Although Thurstonian models are generally…
Descriptors: Item Response Theory, Rating Scales, Models, Comparative Analysis
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Fu, Zhi-Hui; Tao, Jian; Shi, Ning-Zhong; Zhang, Ming; Lin, Nan – Multivariate Behavioral Research, 2011
Multidimensional item response theory (MIRT) models can be applied to longitudinal educational surveys where a group of individuals are administered different tests over time with some common items. However, computational problems typically arise as the dimension of the latent variables increases. This is especially true when the latent variable…
Descriptors: Simulation, Foreign Countries, Longitudinal Studies, Item Response Theory
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Woods, Carol M. – Multivariate Behavioral Research, 2009
Differential item functioning (DIF) occurs when an item on a test or questionnaire has different measurement properties for 1 group of people versus another, irrespective of mean differences on the construct. This study focuses on the use of multiple-indicator multiple-cause (MIMIC) structural equation models for DIF testing, parameterized as item…
Descriptors: Test Bias, Structural Equation Models, Item Response Theory, Testing
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Culpepper, Steven Andrew – Multivariate Behavioral Research, 2009
This study linked nonlinear profile analysis (NPA) of dichotomous responses with an existing family of item response theory models and generalized latent variable models (GLVM). The NPA method offers several benefits over previous internal profile analysis methods: (a) NPA is estimated with maximum likelihood in a GLVM framework rather than…
Descriptors: Profiles, Item Response Theory, Models, Maximum Likelihood Statistics
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Ekehammar, Bo; And Others – Multivariate Behavioral Research, 1975
Descriptors: Anxiety, Comparative Analysis, Factor Analysis, Factor Structure
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van Ginkel, Joost R.; van der Ark, L. Andries; Sijtsma, Klaas – Multivariate Behavioral Research, 2007
The performance of five simple multiple imputation methods for dealing with missing data were compared. In addition, random imputation and multivariate normal imputation were used as lower and upper benchmark, respectively. Test data were simulated and item scores were deleted such that they were either missing completely at random, missing at…
Descriptors: Evaluation Methods, Psychometrics, Item Response Theory, Scores
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Ferrando, Pere J. – Multivariate Behavioral Research, 2002
Analyzed the relations between two continuous response models intended for typical response items: the linear congeneric model and Samejima's continuous response model (CRM). Illustrated the relations described using an empirical example and assessed the relations through a simulation study. (SLD)
Descriptors: Comparative Analysis, Item Response Theory, Models, Simulation
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Thomas, D. Roland – Multivariate Behavioral Research, 1992
The interpretation of discriminant functions as a follow-up to a significant multivariate analysis of variance is discussed. New indices are proposed that aid in identification and interpretation of the subset of response variables that contribute to a significant group discrimination. Their efficacy is compared to several commonly used…
Descriptors: Comparative Analysis, Equations (Mathematics), Mathematical Models, Multivariate Analysis
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Suziedelis, Antanas; And Others – Multivariate Behavioral Research, 1976
A method of typological analysis was applied to computer-generated 96-item questionnaire data for 100 cases, under a variety of conditions to analyze both the item-level and score-level. The results showed a considerable advantage of score-level approach in the number, size, and replicability of clusters recovered. (DEP)
Descriptors: Classification, Cluster Analysis, Cluster Grouping, Comparative Analysis
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Kroonenberg, Pieter M.; Snyder, Conrad W., Jr. – Multivariate Behavioral Research, 1989
Data on problem solving collected within the framework of G. Eckblad's cognitive theory of affect (1981) are analyzed with 3-mode principal component analysis. Eight tasks with 6 judgment scales were completed by 32 13-year-old boys. The effectiveness of the TUCKALS principal components analysis method is discussed. (SLD)
Descriptors: Affective Behavior, Children, Cognitive Processes, Comparative Analysis
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Magnusson, David; Ekehammar, Bo – Multivariate Behavioral Research, 1975
Descriptors: Anxiety, Comparative Analysis, Factor Analysis, Homogeneous Grouping
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De Ayala, R. J.; Hertzog, Melody A. – Multivariate Behavioral Research, 1991
Multidimensional scaling (MDS) and exploratory and confirmatory factor analyses were compared in the assessment of the dimensionality of data sets, using sets generated to be one-dimensional or two-dimensional and differing in degree of interdimensional correlation and number of items defining a dimension. (SLD)
Descriptors: Comparative Analysis, Correlation, Equations (Mathematics), Factor Structure
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Chernyshenko, Oleksandr S.; Stark, Stephen; Chan, Kim-Yin; Drasgow, Fritz; Williams, Bruce – Multivariate Behavioral Research, 2001
Compared the fit of several Item Response Theory (IRT) models to two personality assessment instruments using data from 13,059 individuals responding to one instrument and 1,770 individuals responding to the other. Two- and three-parameter logistic models fit some scales reasonably well, but not others, and the graded response model generally did…
Descriptors: Adults, Comparative Analysis, Goodness of Fit, Item Response Theory
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Knol, Dirk L.; Berger, Martijn P. F. – Multivariate Behavioral Research, 1991
In a simulation study, factor analysis and multidimensional item response theory (IRT) models are compared with respect to estimates of item parameters. For multidimensional data, a common factor analysis on the matrix of tetrachoric correlations performs at least as well as the multidimensional IRT model. (SLD)
Descriptors: Comparative Analysis, Computer Simulation, Equations (Mathematics), Estimation (Mathematics)