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Luo, Wen; Kwok, Oi-Man – Multivariate Behavioral Research, 2009
Cross-classified random-effects models (CCREMs) are used for modeling nonhierarchical multilevel data. Misspecifying CCREMs as hierarchical linear models (i.e., treating the cross-classified data as strictly hierarchical by ignoring one of the crossed factors) causes biases in the variance component estimates, which in turn, results in biased…
Descriptors: Models, Bias, Data, Classification
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Meyers, Jason L.; Beretvas, S. Natasha – Multivariate Behavioral Research, 2006
Cross-classified random effects modeling (CCREM) is used to model multilevel data from nonhierarchical contexts. These models are widely discussed but infrequently used in social science research. Because little research exists assessing when it is necessary to use CCREM, 2 studies were conducted. A real data set with a cross-classified structure…
Descriptors: Social Science Research, Computation, Models, Data Analysis
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Huberty, Carl J.; And Others – Multivariate Behavioral Research, 1986
Three methods of transforming unordered categorical response variables are described: (1) analysis using dummy variables; (2) eigenanalysis of frequency patterns scaled relative to within-groups variance; (3) categorical variables analyzed separately with scale values generated so that the grouping variable and the categorical variable are…
Descriptors: Classification, Correlation, Discriminant Analysis, Measurement Techniques
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Bergman, Lars R. – Multivariate Behavioral Research, 1988
When performing a classification study, it is often useful to leave a residue of unclassified entities to be analyzed separately. Using an interactional paradigm, theoretical reasoning for this approach is outlined. A procedure--RESIDAN--for conducting a classification analysis using a residue is described, and empirical data are provided. (TJH)
Descriptors: Classification, Cluster Analysis, Cluster Grouping, Error of Measurement
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Breckenridge, James N. – Multivariate Behavioral Research, 1989
A Monte Carlo study evaluated the effectiveness of three rules of classifying objects into clusters: nearest neighbor classification; nearest centroid assignment; and quadratic discriminant analysis. Results suggest that the nearest neighbor rule is a useful tool for assessing the validity of the clustering procedure of J. H. Ward (1963). (SLD)
Descriptors: Classification, Cluster Analysis, Cluster Grouping, Discriminant Analysis
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Huberty, Carl J.; And Others – Multivariate Behavioral Research, 1987
Three estimates of the probabilities of correct classification in predictive discriminant analysis were computed using mathematical formulas, resubstitution, and external analyses: (1) optimal hit rate; (2) actual hit rate; and (3) expected actual hit rate. Methods were compared using Monte Carlo sampling from two data sets. (Author/GDC)
Descriptors: Classification, Discriminant Analysis, Elementary Education, Estimation (Mathematics)
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Miller, Donald M.; And Others – Multivariate Behavioral Research, 1986
Two techniques compose a new methodology for studying certain classes of qualitative information: the F-sort task for data collection and latent partition analysis for data summarization. A detailed presentation is given of its application to a study of teacher's views of facilitating student learning in the classroom. (Author/LMO)
Descriptors: Classification, Concept Formation, Data Collection, Elementary Secondary Education
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Maij-de Meij, Annette M.; Kelderman, Henk; van der Flier, Henk – Multivariate Behavioral Research, 2005
Based on the literature about self-disclosure, it was hypothesized that different groups of subjects differ in their pattern of self-disclosure with respect to different areas of social interaction. An extended latent-trait latent-class model was proposed to describe these general patterns of self-disclosure. The model was used to analyze the data…
Descriptors: Work Environment, Item Response Theory, Self Disclosure (Individuals), Hypothesis Testing
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Olsson, Ulf – Multivariate Behavioral Research, 1979
The paper discusses the consequences for maximum likelihood factor analysis which may follow if the observed variables are ordinal with only a few scale steps. Results indicate that classification may lead to a substantial lack of fit of the model--an erroneous indication that more factors are needed. (Author/CTM)
Descriptors: Classification, Factor Analysis, Goodness of Fit, Maximum Likelihood Statistics
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Mano, Haim – Multivariate Behavioral Research, 1991
Structure and intensity of naturally occurring and induced affect were studied with 244 university students and 1 employee in 2 studies using 2 methodological paradigms (dimensionality and classification) and 2 everyday contexts (lecture and television advertising). A circular structure of feeling was experienced during the lecture (naturally…
Descriptors: Adults, Advertising, Affective Behavior, Classification