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Hwang, Heungsun; Dillon, William R. – Multivariate Behavioral Research, 2010
A 2-way clustering approach to multiple correspondence analysis is proposed to account for cluster-level heterogeneity of both respondents and variable categories in multivariate categorical data. Specifically, in the proposed method, multiple correspondence analysis is combined with k-means in a unified framework in which "k"-means is…
Descriptors: Data Analysis, Multivariate Analysis, Classification, Monte Carlo Methods
Ruscio, John; Kaczetow, Walter – Multivariate Behavioral Research, 2009
Interest in modeling the structure of latent variables is gaining momentum, and many simulation studies suggest that taxometric analysis can validly assess the relative fit of categorical and dimensional models. The generation and parallel analysis of categorical and dimensional comparison data sets reduces the subjectivity required to interpret…
Descriptors: Classification, Models, Comparative Analysis, Statistical Analysis
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

Rodgers, Joseph Lee – Multivariate Behavioral Research, 1999
Defines a sampling taxonomy that shows the differences between and relationships among the bootstrap, the jackknife, and the randomization test. Demonstrates the usefulness of the taxonomy for teaching the goals and purposes of resampling schemes and presents univariate and multivariate examples. (SLD)
Descriptors: Classification, Models, Sampling
Bauer, Daniel J.; Sterba, Sonya K.; Hallfors, Denise Dion – Multivariate Behavioral Research, 2008
Individually randomized treatments are often administered within a group setting. As a consequence, outcomes for treated individuals may be correlated due to provider effects, common experiences within the group, and/or informal processes of socialization. In contrast, it is often reasonable to regard outcomes for control participants as…
Descriptors: Youth Programs, High Risk Students, Behavior Disorders, Outcomes of Treatment

Milligan, Glenn W. – Multivariate Behavioral Research, 1989
Simulated test data (N=864 artificial data sets) with four different error conditions were used to study the recovery characteristics of the beta-flexible clustering method. Conditions under which the beta-flexible method provides good recovery are discussed. (SLD)
Descriptors: Classification, Cluster Analysis, Cluster Grouping, Simulation

van Buuren, Stef; de Leeuw, Jan – Multivariate Behavioral Research, 1992
Application of equality constraints on the categories of a variable is a simple and useful extension of multiple correspondence analysis. Equality is an easy way to incorporate prior knowledge. A procedure to deal with unequal category numbers and with subsets of variables is outlined and illustrated. (SLD)
Descriptors: Classification, Knowledge Level, Mathematical Models, Multivariate Analysis

Burton, Michael L. – Multivariate Behavioral Research, 1975
Three dissimilarity measures for the unconstrained sorting task are investigated. All three are metrics, but differ in the kind of compensation which they make for differences in the sizes of cells within sortings. Empirical tests of the measures are done with sorting data for occupations names and the names of behaviors, using multidimensional…
Descriptors: Classification, Cluster Analysis, Correlation, Matrices

Carroll, Robert M.; Field, John – Multivariate Behavioral Research, 1974
Descriptors: Classification, Goodness of Fit, Profiles, Scores

Henschke, Claudia I.; And Others – Multivariate Behavioral Research, 1974
The technique presented accomodates multiple populations and a mixture of variable types. It is based on the linear discriminant function and the sequential concept. Also presented is an example in which the technique is used to obtain the classification rule to predict the career choices of individuals based upon a mixture of variable types.…
Descriptors: Classification, Measurement Techniques, Predictor Variables, Probability
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

Rigdon, Edward E. – Multivariate Behavioral Research, 1995
This article presents a straightforward classification system that is a necessary and sufficient condition for identification of the structural component of structural equation models of the block-recursive type with no more than two equations per block. Limitations of other identification techniques are discussed. (SLD)
Descriptors: Classification, Equations (Mathematics), Estimation (Mathematics), Identification

Millsap, Roger E.; Meredith, William – Multivariate Behavioral Research, 1991
Mathematical relationships between three-mode component analysis and stationary component analysis are explored. Theorems are presented giving constraints that must be satisfied for equivalency between component representations provided by the methods. In general, the two approaches give mathematically distinct representations. (SLD)
Descriptors: Classification, Equations (Mathematics), Longitudinal Studies, Mathematical Models

Maydeu-Olivares, Albert; Morera, Osvaldo; D'Zurilla, Thomas J. – Multivariate Behavioral Research, 1999
Using item response theory, discusses the difficulties faced in evaluating measurement invariance when a psychological construct is assessed through a test or inventory composed of categorical items. Illustrates the usefulness of fitplots in assessing measurement invariance in inventory data. (SLD)
Descriptors: Classification, Item Response Theory, Psychological Testing, Test Interpretation

Huberty, Carl J.; Curry, Allen R. – Multivariate Behavioral Research, 1978
Classification is a procedure through which individuals are classified as being members of a particular group based on a variety of independent variables. Two methods of makin such classifications are discussed; the quadratic method is seen to be superior to the linear under certain constraints. (JKS)
Descriptors: Analysis of Covariance, Classification, Discriminant Analysis, Groups