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Reynolds, Thomas J.; Jackosfsky, Ellen F. – Educational and Psychological Measurement, 1981
The purpose of this paper is to outline the role of orthogonal rotation in canonical analysis, including the evaluative measures that need be reported and scrutinized upon application. (Author)
Descriptors: Attitude Measures, Multivariate Analysis, Orthogonal Rotation, Transformations (Mathematics)
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Meir, Elchanan I.; Gati, Itamar – Educational and Psychological Measurement, 1981
In many personality and interest inventories, a score profile, rather than a single score, is attributed to each subject. Six applicable criteria are suggested for use in examining the adequacy of items in such inventories. These criteria relate to the items' response distributions, internal consistency, and discriminative value. (Author/BW)
Descriptors: Evaluation Criteria, Interest Inventories, Item Analysis, Personality Measures
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Conger, Anthony J. – Educational and Psychological Measurement, 1980
Reliability maximizing weights are related to theoretically specified true score scaling weights to show a constant relationship that is invariant under separate linear tranformations on each variable in the system. Test theoretic relations should be derived for the most general model available and not for unnecessarily constrained models.…
Descriptors: Mathematical Formulas, Scaling, Test Reliability, Test Theory
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Reynolds, Thomas J. – Educational and Psychological Measurement, 1981
Cliff's Index "c" derived from an item dominance matrix is utilized in a clustering approach, termed extracting Reliable Guttman Orders (ERGO), to isolate Guttman-type item hierarchies. A comparison of factor analysis to the ERGO is made on social distance data involving multiple ethnic groups. (Author/BW)
Descriptors: Cluster Analysis, Difficulty Level, Factor Analysis, Item Analysis
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Gati, Itamar – Educational and Psychological Measurement, 1981
This paper examines the properties of the Item Efficiency Index proposed by Neill and Jackson (1976; EJ 137 077) for minimum redundancy item analysis. (Author/BW)
Descriptors: Correlation, Factor Structure, Item Analysis, Mathematical Models
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Fleming, James S. – Educational and Psychological Measurement, 1981
The perfunctory use of factor scores in conjunction with regression analysis is inappropriate for many purposes. It is suggested that factoring methods are most suitable for independent variable sets when some consideration has been given to the nature of the domain, which is implied by the predictors. (Author/BW)
Descriptors: Factor Analysis, Multiple Regression Analysis, Predictor Variables, Research Problems
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Hsu, Louis M. – Educational and Psychological Measurement, 1980
In two treatment-repeated measurements designs, the ratio between the unbiased variance of the differences and twice the variance of the errors of measurement can be used to test for interaction of subjects and treatments. The use of this statistic is illustrated. (Author/CP)
Descriptors: Analysis of Variance, Aptitude Tests, Error of Measurement, Mathematical Formulas
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Harris, Chester W. – Educational and Psychological Measurement, 1980
Brennan's B statistic is a generalized upper-lower discrimination index which was first published in 1972: Peirce earlier introduced a statistic on the relation between a predictor and an outcome which has the same structure as Brennan's B. (Author/CP)
Descriptors: Discriminant Analysis, Item Analysis, Mathematical Formulas, Predictive Measurement
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Greener, Jack M.; Osburn, H. G. – Educational and Psychological Measurement, 1980
Corrections for restriction in range due to explicit selection assume linearity of regression and homoscedastic array variances. A Monte Carlo study was conducted to examine the effects of some common forms of violation of these assumptions. (Author/CP)
Descriptors: Correlation, Error of Measurement, Predictor Variables, Statistical Bias
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Willson, Victor L. – Educational and Psychological Measurement, 1980
Guilford's average interrater correlation coefficient is shown to be related to the Friedman Rank Sum statistic. Under the null hypothesis of zero correlation, the resultant distribution is known and the hypothesis can be tested. Large sample and tied score cases are also considered. An example from Guilford (1954) is presented. (Author)
Descriptors: Correlation, Hypothesis Testing, Mathematical Formulas, Reliability
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Vegelius, Jan – Educational and Psychological Measurement, 1980
One argument against the G index is that, unlike phi, it is not a correlation coefficient; yet, G conforms to the Kendall and E-coefficient definitions. The G index is also equal to the Pearson product moment correlation coefficient obtained from double scoring. (Author/CP)
Descriptors: Correlation, Mathematical Formulas, Test Reliability
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Silverstein, A. B. – Educational and Psychological Measurement, 1980
An alternative derivation was given of Gaylord's formulas showing the relationships among the average item intercorrelation, the average item-test correlation, and test reliability. Certain parallels were also noted in analysis of variance and principal component analysis. (Author)
Descriptors: Analysis of Variance, Item Analysis, Mathematical Formulas, Test Reliability
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Gustafsson, Jan-Eric – Educational and Psychological Measurement, 1980
The statistically correct conditional maximum likelihood (CML) estimation method has not been used because of numerical problems. A solution is presented which allows a rapid computation of the CML esitmates also for long tests. CML has decisive advantages in the construction of statistical tests of goodness of fit. (Author/CP)
Descriptors: Goodness of Fit, Item Analysis, Latent Trait Theory, Mathematical Formulas