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Wilcox, Rand R. – Educational and Psychological Measurement, 2006
Consider the nonparametric regression model Y = m(X)+ [tau](X)[epsilon], where X and [epsilon] are independent random variables, [epsilon] has a median of zero and variance [sigma][squared], [tau] is some unknown function used to model heteroscedasticity, and m(X) is an unknown function reflecting some conditional measure of location associated…
Descriptors: Nonparametric Statistics, Mathematical Models, Regression (Statistics), Probability
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Rogosa, David – Educational and Psychological Measurement, 1981
The form of the Johnson-Neyman region of significance is shown to be determined by the statistic for testing the null hypothesis that the population within-group regressions are parallel. Results are obtained for both simultaneous and nonsimultaneous regions of significance. (Author)
Descriptors: Hypothesis Testing, Mathematical Models, Predictor Variables, Regression (Statistics)
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Hollingsworth, Holly H. – Educational and Psychological Measurement, 1980
If heterogeneous regression slopes are present in analysis of covariance (ANCOVA), the likelihood of committing a Type I error is greater than what had been prespecified. The power of the ANCOVA test of hypothesis for all possible differences of treatment effects is not maximized. (Author/RL)
Descriptors: Analysis of Covariance, Hypothesis Testing, Mathematical Models, Power (Statistics)
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Kromrey, Jeffrey D.; Foster-Johnson, Lynn – Educational and Psychological Measurement, 1998
Provides a comparison of centered and raw-score analyses in least squares regression. The two methods are demonstrated with constructed data in a Monte Carlo study to be equivalent, yielding identical hypothesis tests associated with the moderation effect and regression equations that are functionally equivalent. (SLD)
Descriptors: Hypothesis Testing, Least Squares Statistics, Monte Carlo Methods, Raw Scores
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James, Lawrence R.; And Others – Educational and Psychological Measurement, 1982
An analytic procedure is presented which casts sequential moderator analysis in the role of a multivariate test of parallelism of regressions. The procedure addresses a test for comparing predictor-criterion relationships for one set of measurements or multiple predictors and repeated measurements on a criterion. (Author/PN)
Descriptors: Correlation, Hypothesis Testing, Measurement Techniques, Multivariate Analysis
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Finch, W. Holmes; French, Brian F. – Educational and Psychological Measurement, 2007
Differential item functioning (DIF) continues to receive attention both in applied and methodological studies. Because DIF can be an indicator of irrelevant variance that can influence test scores, continuing to evaluate and improve the accuracy of detection methods is an essential step in gathering score validity evidence. Methods for detecting…
Descriptors: Item Response Theory, Factor Analysis, Test Bias, Comparative Analysis
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Locascia, Joseph J.; Cordray, David S. – Educational and Psychological Measurement, 1983
This paper demonstrates that gain score analysis and analysis of covariance (ANCOVA) will always yield conflicting results unless the slopes of within-group regression lines of posttest on pretest equal one. In Lord's specific hypothetical example, trait instability invalidates the ANCOVA. An analysis that corrects for this problem is…
Descriptors: Achievement Gains, Analysis of Covariance, Data Analysis, Higher Education
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Wu, Yow-wu B. – Educational and Psychological Measurement, 1984
The present study compares the robustness of two different one way fixed-effects analysis of covariance (ANCOVA) models to investigate whether the model which uses a test statistic incorporating estimates of separate unequal regression slopes is more robust than the conventional model which assumes the slopes are equal. (Author/BW)
Descriptors: Analysis of Covariance, Comparative Analysis, Computer Simulation, Hypothesis Testing
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Charter, Richard A. – Educational and Psychological Measurement, 1982
Practical formulas for several analysis of variance (ANOVA) designs and models are presented which make it possible for readers to compute strength of association measures without the use of complete ANOVA tables. (Author/PN)
Descriptors: Analysis of Variance, Hypothesis Testing, Mathematical Formulas, Mathematical Models