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Tenko Raykov; Ahmed Haddadi; Christine DiStefano; Mohammed Alqabbaa – Educational and Psychological Measurement, 2025
This note is concerned with the study of temporal development in several indices reflecting clustering effects in multilevel designs that are frequently utilized in educational and behavioral research. A latent variable method-based approach is outlined, which can be used to point and interval estimate the growth or decline in important functions…
Descriptors: Multivariate Analysis, Hierarchical Linear Modeling, Educational Research, Statistical Inference
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Powers, Stephen; Jones, Patricia B. – Educational and Psychological Measurement, 1986
This paper describes two BASIC computer programs that calculate Hotelling's T-square either for one sample or for two samples. Output of the progams includes the Mahalanobis distance D-square, the F ratio associated with T-square, and its probability level. (Author)
Descriptors: Computer Software, Microcomputers, Multivariate Analysis
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Janson, Harald; Olsson, Ulf – Educational and Psychological Measurement, 2001
Proposes a generalization of Cohen's kappa coefficient (J. Cohen, 1960) to address the problem of accounting for overall chance-corrected interobserver agreement among the multivariate ratings of several judges. The statistic's metric is conventional and in the univariate case it is equivalent to existing extensions of the kappa coefficient to…
Descriptors: Interrater Reliability, Judges, Multivariate Analysis
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Alliger, George M.; Alexander, Ralph A. – Educational and Psychological Measurement, 1984
When selection occurs on the basis of two or more predictors, multivariate restriction of range can reduce various parameters of a validation study. A Statistical Analysis System (SAS) and a Fortran IV program are described that allow for correction of criterion standard deviation(s) and zero-order validities. (Author)
Descriptors: Computer Software, Multivariate Analysis, Predictive Validity, Selection
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Thompson, Bruce; Frankiewicz, Ronald G. – Educational and Psychological Measurement, 1979
An overview of several statistics useful in interpreting variates constructed by using canonical correlation analysis is presented. A computer program which calculates coefficients not typically provided by computer packages is discussed. An illustrative example of the output is provided. (Author/JKS)
Descriptors: Computer Programs, Correlation, Multivariate Analysis, Program Descriptions
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De Corte, Wilfried – Educational and Psychological Measurement, 2000
Shows how a theorem proven by H. Brogden (1951, 1959) can be used to estimate the allocation average (a predictor based classification of a test battery) assuming that the predictor intercorrelations and validities are known and that the predictor variables have a joint multivariate normal distribution. (SLD)
Descriptors: Classification, Correlation, Estimation (Mathematics), Multivariate Analysis
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Janson, Harald; Olsson, Ulf – Educational and Psychological Measurement, 2004
This article addresses the problem of accounting overall multivariate chance-corrected interobserver agreement when targets have been rated by different sets of judges (not necessarily equal in number). The proposed approach builds on Janson and Olsson's multivariate generalization of Cohen's kappa but incorporates weighting for number of judges…
Descriptors: Interrater Reliability, Multivariate Analysis, Evaluation Methods, Measurement Techniques
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Rupp, Andre A.; Zumbo, Bruno D. – Educational and Psychological Measurement, 2004
Based on seminal work by Lord and Hambleton, Swaminathan, and Rogers, this article is an analytical, graphical, and conceptual reminder that item response theory (IRT) parameter invariance only holds for perfect model fit in multiple populations or across multiple conditions and is thus an ideal state. In practice, one attempts to quantify the…
Descriptors: Correlation, Item Response Theory, Statistical Analysis, Evaluation Methods
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Kristjansson, Elizabeth; Aylesworth, Richard; Mcdowell, Ian; Zumbo, Bruno D. – Educational and Psychological Measurement, 2005
Item bias is a major threat to measurement validity. Methods for detecting differential item functioning (DIF) are now commonly used to identify potentially biased items. DIF detection methods for dichotomous items are well developed, but those for ordinal items are less well developed. In this article, the authors compare four methods for…
Descriptors: Discriminant Analysis, Test Bias, Multivariate Analysis, Regression (Statistics)