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Hartig, Johannes; Holzel, Britta; Moosbrugger, Helfried – Multivariate Behavioral Research, 2007
Numerous studies have shown increasing item reliabilities as an effect of the item position in personality scales. Traditionally, these context effects are analyzed based on item-total correlations. This approach neglects that trends in item reliabilities can be caused either by an increase in true score variance or by a decrease in error…
Descriptors: True Scores, Error of Measurement, Structural Equation Models, Simulation
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Conger, Anthony J. – Multivariate Behavioral Research, 1974
Two indices of profile reliability are shown to be equivalent in terms of the individual independent canonical composites; however, because of different weighting procedures, they yield different overall indices of profile reliability. A common formula is provided from which both indices can be derived. (Author)
Descriptors: Analysis of Variance, Correlation, Matrices, Measurement Techniques
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Werts, C. E.; And Others – Multivariate Behavioral Research, 1980
This paper demonstrates how the problem of calibrating measures can be formulated in terms of confirmatory factor analysis. The relationships between traditional approaches and a confirmatory factor approach are specified. (Author/CTM)
Descriptors: Achievement Tests, Equated Scores, Factor Analysis, Intermediate Grades
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Millsap, Roger E.; Everson, Howard – Multivariate Behavioral Research, 1991
Use of confirmatory factor analysis (CFA) with nonzero latent means in testing six different measurement models from classical test theory is discussed. Implications of the six models for observed mean and covariance structures are described, and three examples of the use of CFA in testing the models are presented. (SLD)
Descriptors: Comparative Analysis, Equations (Mathematics), Goodness of Fit, Mathematical Models
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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