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Steiger, James H.; Browne, Michael W. – Psychometrika, 1984
A general procedure is provided for comparing correlation coefficients between optimal linear composites. It allows computationally efficient significance tests on independent or dependent multiple correlations, partial correlations, and canonical correlations, with or without the assumption of multivariate normality. Evidence from Monte Carlo…
Descriptors: Correlation, Hypothesis Testing, Monte Carlo Methods, Statistical Distributions
Oshima, T. C.; Raju, Nambury S.; Nanda, Alice O. – Journal of Educational Measurement, 2006
A new item parameter replication method is proposed for assessing the statistical significance of the noncompensatory differential item functioning (NCDIF) index associated with the differential functioning of items and tests framework. In this new method, a cutoff score for each item is determined by obtaining a (1-alpha ) percentile rank score…
Descriptors: Evaluation Methods, Statistical Distributions, Statistical Significance, Test Bias

Rasmussen, Jeffrey Lee; Dunlap, William P. – Educational and Psychological Measurement, 1991
Results of a Monte Carlo study with 4 populations (3,072 conditions) indicate that when distributions depart markedly from normality, nonparametric analysis and parametric analysis of transformed data show superior power to parametric analysis of raw data. Under conditions studied, parametric analysis of transformed data is more powerful than…
Descriptors: Comparative Analysis, Computer Simulation, Monte Carlo Methods, Power (Statistics)
Olejnik, Stephen; Algina, James – 1987
The purpose of this study was to develop a single procedure for comparing population variances which could be used for distribution forms. Bootstrap methodology was used to estimate the variability of the sample variance statistic when the population distribution was normal, platykurtic and leptokurtic. The data for the study were generated and…
Descriptors: Comparative Analysis, Estimation (Mathematics), Measurement Techniques, Monte Carlo Methods

Zimmerman, Donald W. – Journal of Experimental Education, 1987
A program obtained random samples from known populations, some of which violated the homogeneity assumption. Student t tests and Mann-Whitney U Tests were performed on the sample value. Where the t test led to incorrect decisions, the use of Mann-Whitney U test in its place led to poorer results. (JAZ)
Descriptors: Computer Software, Error of Measurement, Monte Carlo Methods, Nonparametric Statistics