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Gignac, Gilles E.; Watkins, Marley W. – Multivariate Behavioral Research, 2013
Previous confirmatory factor analytic research that has examined the factor structure of the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) has endorsed either higher order models or oblique factor models that tend to amalgamate both general factor and index factor sources of systematic variance. An alternative model that has not yet…
Descriptors: Intelligence Tests, Test Reliability, Factor Structure, Models
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Estabrook, Ryne; Neale, Michael – Multivariate Behavioral Research, 2013
Factor score estimation is a controversial topic in psychometrics, and the estimation of factor scores from exploratory factor models has historically received a great deal of attention. However, both confirmatory factor models and the existence of missing data have generally been ignored in this debate. This article presents a simulation study…
Descriptors: Factor Analysis, Scores, Computation, Regression (Statistics)
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Gagne, Phill; Hancock, Gregory R. – Multivariate Behavioral Research, 2006
Sample size recommendations in confirmatory factor analysis (CFA) have recently shifted away from observations per variable or per parameter toward consideration of model quality. Extending research by Marsh, Hau, Balla, and Grayson (1998), simulations were conducted to determine the extent to which CFA model convergence and parameter estimation…
Descriptors: Sample Size, Factor Analysis, Computation, Models
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Barcikowski, Robert S.; Stevens, James P. – Multivariate Behavioral Research, 1975
Results showed that the canonical correlations are very stable upon replication. The results also indicated that there is no solid evidence for concluding that components are superior to the coefficients, at least not in terms of being more reliable. (Author/BJG)
Descriptors: Correlation, Factor Analysis, Matrices, Monte Carlo Methods
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Nesselroade, John R.; Cable, Dana G. – Multivariate Behavioral Research, 1974
Using the personality trait versus state distinction as a substantive context, the fit of the factor analytic model to difference score data is investigated and found to be quite good. Methodological issues related to properties of difference scores and their implications for personality research are briefly discussed. (Author)
Descriptors: Anxiety, Factor Analysis, Measurement Techniques, Personality
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Paunonen, Sampo V. – Multivariate Behavioral Research, 1987
Study determines that solutions derived by multiple group analysis and item-total correlation analysis were generally most interpretable from a psychological perspective. It was concluded that their application to test construction is preferred over Procrustean or confirmatory maximum likelihood approaches. (RB)
Descriptors: Correlation, Data Analysis, Factor Analysis, Psychological Testing
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Silverstein, A. B.; Fisher, Gary – Multivariate Behavioral Research, 1974
Descriptors: Cluster Analysis, Factor Analysis, Factor Structure, Males
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Rindskopf, David; Rose, Tedd – Multivariate Behavioral Research, 1988
Confirmatory factor analysis was applied to test second- and higher-order factor models in the areas of structure of abilities, allometry, and the separation of specific and error variance estimates. The estimation of validity and reliability, second-order models within factor analysis models, and the concept of discriminability were also studied.…
Descriptors: Discriminant Analysis, Error of Measurement, Estimation (Mathematics), Factor Analysis
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Dudzinski, M. L.; And Others – Multivariate Behavioral Research, 1975
Descriptors: Comparative Analysis, Correlation, Factor Analysis, Homogeneous Grouping
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Bernstein, Ira H.; And Others – Multivariate Behavioral Research, 1986
A three subscale inventory designed by Fenigstein, Scheier, and Buss to measure self-consciousness was administered to 297 college students. Fenigstein et al.'s representation was found to fit the data in its original form. Items on the subscales differ nearly as much statistically as they do substantively. (Author/LMO)
Descriptors: College Students, Correlation, Factor Analysis, Factor Structure
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Overall, John E.; Pfefferbaum, Betty – Multivariate Behavioral Research, 1982
Five experienced child and adolescent psychiatrists provided ratings of symptoms and behaviors of typical patients belonging to 18 different diagnostic groups. Ratings were then factor analyzed and revealed consistency of description in 14 of the 18 groups. (Author/JKS)
Descriptors: Adolescents, Behavior Problems, Child Psychology, Clinical Diagnosis
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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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Nelson, Edward A.; Uhl, Norman P. – Multivariate Behavioral Research, 1974
Descriptors: Attitude Change, Attitude Measures, Black Colleges, College Freshmen
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Ofir, Chezy; And Others – Multivariate Behavioral Research, 1987
Three frequently used response formats are compared via analysis of covariance structures. The cumulative results based on four data sets provided evidence inconsistent with previous research suggesting that these formats are interchangeable. The semantic-differential format is most preferred while in most cases the Stapel format is least…
Descriptors: Analysis of Covariance, Factor Analysis, Hypothesis Testing, Mathematical Models
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Strauman, Timothy J.; Wetzler, Scott – Multivariate Behavioral Research, 1992
Scale-level factor analyses are reported for 2 self-report measures of psychopathology, the Symptom Checklist-90-R (SCL-90) and the Millon Clinical Multiaxial Inventory (MCMI), using 130 psychiatric inpatients and outpatients. Used separately, the measures offer limited interpretability of scale profiles. Their combined use permits differentiation…
Descriptors: Adults, Anxiety, Clinical Diagnosis, Comparative Testing