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Wang, Lijuan; Grimm, Kevin J. – Multivariate Behavioral Research, 2012
Reliabilities of the two most widely used intraindividual variability indicators, "ISD[superscript 2]" and "ISD", are derived analytically. Both are functions of the sizes of the first and second moments of true intraindividual variability, the size of the measurement error variance, and the number of assessments within a burst. For comparison,…
Descriptors: Reliability, Statistical Analysis, Measurement, Models
Steinley, Douglas; Brusco, Michael J. – Multivariate Behavioral Research, 2008
A variance-to-range ratio variable weighting procedure is proposed. We show how this weighting method is theoretically grounded in the inherent variability found in data exhibiting cluster structure. In addition, a variable selection procedure is proposed to operate in conjunction with the variable weighting technique. The performances of these…
Descriptors: Test Items, Simulation, Multivariate Analysis, Data Analysis
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

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

Fava, Joseph L.; Velicer, Wayne F. – Multivariate Behavioral Research, 1992
Effects of overextracting factors and components within and between maximum likelihood factor analysis and principal components analysis were examined through computer simulation of a range of factor and component patterns. Results demonstrate similarity of component and factor scores during overextraction. Overall, results indicate that…
Descriptors: Computer Simulation, Correlation, Factor Analysis, Mathematical Models