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Fang, Hua; Brooks, Gordon P.; Rizzo, Maria L.; Espy, Kimberly Andrews; Barcikowski, Robert S. – Journal of Experimental Education, 2009
Because the power properties of traditional repeated measures and hierarchical multivariate linear models have not been clearly determined in the balanced design for longitudinal studies in the literature, the authors present a power comparison study of traditional repeated measures and hierarchical multivariate linear models under 3…
Descriptors: Longitudinal Studies, Models, Measurement, Multivariate Analysis

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

Barcikowski, Robert S.; Stevens, James P. – Multivariate Behavioral Research, 1976
This article is a rejoinder to TM 502 249. Each of Thorndike's comments are examined. A possible solution to the large number of subjects necessary for stable weights and variate-variable correlations using ridge regression procedures is suggested. (RC)
Descriptors: Correlation, Measurement Techniques, Monte Carlo Methods, Multivariate Analysis
Barcikowski, Robert S.; Elliott, Ronald S. – 1991
The contribution of individual variables to overall multivariate significance in a multivariate analysis of variance (MANOVA) is investigated using a combination of canonical discriminant analysis and Roy-Bose simultaneous confidence intervals. Difficulties with this procedure are discussed, and its advantages are illustrated using examples based…
Descriptors: Comparative Analysis, Correlation, Discriminant Analysis, Mathematical Models

Elliott, Ronald S.; Barcikowski, Robert S. – Mid-Western Educational Researcher, 1994
In multivariate analysis of variance studies with small numbers of subjects (15 or less) per treatment level, probability values reported by the commercial statistical packages SAS and SPSS are conservative for F approximations based on Pillai's trace and liberal for F approximations based on the Hotelling-Lawley trace. Discusses results in terms…
Descriptors: Monte Carlo Methods, Multivariate Analysis, Power (Statistics), Probability
Barcikowski, Robert S.; Robey, Randall R. – 1985
This paper provides researchers with a method of determining sample size for a given power level in the preparation of a single group exploratory repeated measure analysis. The rationale for determining sample size which takes into consideration the powers and assumptions of both the adjusted univariate and multivariate repeated measures tests is…
Descriptors: Analysis of Variance, Effect Size, Hypothesis Testing, Multivariate Analysis
Elliott, Ronald S.; Barcikowski, Robert S. – 1993
This Monte Carlo study examines whether, given various numbers of variables, treatments, and sample sizes, in a one-way multivariate analysis of variance, Type I error rates of the test approximations provided by the BMDP program, the Statistical Analysis System (SAS), and the Statistical Package for the Social Sciences (SPSS) for Roy's largest…
Descriptors: Analysis of Variance, Computer Simulation, Estimation (Mathematics), Monte Carlo Methods
Robey, Randall R.; Barcikowski, Robert S. – 1987
The mixed model analysis of variance assumes a mathematical property known as sphericity. Several preliminary tests have been proposed to detect departures from the sphericity assumption. The logic of the preliminary testing procedure is to conduct the mixed model analysis of variance if the preliminary test suggests that the sphericity assumption…
Descriptors: Analysis of Variance, Error of Measurement, Hypothesis Testing, Mathematical Models
Robey, Randall R.; Barcikowski, Robert S. – 1986
This paper reports the results of a Monte Carlo investigation of Type I errors in the single group repeated measures design where multiple measures are collected from each observational unit at each measurement occasion. The Type I error of three multivariate tests were examined. These were the doubly multivariate F test, the multivariate mixed…
Descriptors: Analysis of Variance, Behavioral Science Research, Comparative Analysis, Hypothesis Testing