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Huynh, Huynh – 1977
Three techniques for estimating Kuder Richardson reliability (KR20) coefficients for incomplete data are contrasted. The methods are: (1) Henderson's Method 1 (analysis of variance, or ANOVA); (2) Henderson's Method 3 (FITCO); and (3) Koch's method of symmetric sums (SYSUM). A Monte Carlo simulation was used to assess the precision of the three…
Descriptors: Analysis of Variance, Comparative Analysis, Mathematical Models, Monte Carlo Methods
Huynh, Cam-Loi – 1989
Parametric measures to estimate J. Cohen's effect size (1966) from a single experiment or for a single study in meta-analysis are investigated. The main objective was to examine the principal statistical properties of this effect size--delta--under variance homogeneity, variance heterogeneity with known variance ratios, and for the Behrens-Fisher…
Descriptors: Analysis of Variance, Effect Size, Equations (Mathematics), Estimation (Mathematics)
Peer reviewed Peer reviewed
Harwell, Michael R.; And Others – Journal of Educational Statistics, 1992
Implications of metanalytic results from Monte Carlo studies of the robustness of the F test in the one- and two-factor fixed effects analysis of variance (ANOVA) models and Monte Carlo results for the B. L. Welch (1947) and Kruskal-Wallis (1952) tests are discussed. (SLD)
Descriptors: Analysis of Variance, Data Interpretation, Mathematical Models, Meta Analysis
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
Braver, Sanford L.; Sheets, Virgil L. – 1990
Numerous designs using analysis of variance (ANOVA) to test ordinal hypotheses were assessed using a Monte Carlo simulation. Each statistic was computed on each of over 10,000 random samples drawn from a variety of population conditions. The number of groups, population variance, and patterns of population means were varied. In the non-null…
Descriptors: Analysis of Variance, Hypothesis Testing, Mathematical Models, Monte Carlo Methods
Noe, Michael J. – 1976
This study compared three approaches to the two-factor experiment with repeated measures on one factor: (1) the conventional mixed model analysis of variance, (2) the Greenhouse-Geisser conservative analysis of variance, and (3) multivariate extensions of analysis of variance. Computer simulated data were used in a total of 96 sets of covariance…
Descriptors: Analysis of Variance, Comparative Analysis, Computer Programs, Correlation
Pohlmann, John T. – 1972
The Monte Carlo method was used, and the factors considered were (1) level of main effects in the population; (2) level of interaction effects in the population; (3) alpha level used in determining whether to pool; and (4) number of degrees of freedom. The results indicated that when the ratio degrees of freedom (axb)/degrees of freedom (within)…
Descriptors: Analysis of Variance, Computer Programs, Factor Analysis, Hypothesis Testing
Corder-Bolz, Charles R. – 1978
A Monte Carlo Study was conducted to evaluate six models commonly used to evaluate change. The results revealed specific problems with each. Analysis of covariance and analysis of variance of residualized gain scores appeared to substantially and consistently overestimate the change effects. Multiple factor analysis of variance models utilizing…
Descriptors: Analysis of Covariance, Analysis of Variance, Comparative Analysis, Hypothesis Testing
Peer reviewed Peer reviewed
Sherman, Charles R. – Psychometrika, 1972
Results provide a first step toward the establishment of guidelines for the experimenter who wishes to use nonmetric multidimensional scaling effectively, especially when an underlying configuration is hypothesized. (Author)
Descriptors: Analysis of Covariance, Analysis of Variance, Evaluation, Goodness of Fit
Chou, Tungshan; Huberty, Carl J. – 1992
The empirical performance of the technique proposed by P. O. Johnson and J. Neyman (1936) (the JN technique) and the modification of R. F. Potthoff (1964) was studied in simulated data settings. The robustness of the two JN techniques was investigated with respect to their ability to control Type I and Type III errors. Factors manipulated in the…
Descriptors: Analysis of Variance, Computer Simulation, Equations (Mathematics), Error Patterns
Peer reviewed Peer reviewed
Huck, Schuyler W.; And Others – Educational and Psychological Measurement, 1981
Believing that examinee-by-item interaction should be conceptualized as true score variability rather than as a result of errors of measurement, Lu proposed a modification of Hoyt's analysis of variance reliability procedure. Via a computer simulation study, it is shown that Lu's approach does not separate interaction from error. (Author/RL)
Descriptors: Analysis of Variance, Comparative Analysis, Computer Programs, Difficulty Level
Peer reviewed Peer reviewed
Farley, John U.; Reddy, Srinivas K. – Multivariate Behavioral Research, 1987
In an experiment manipulating artificial data in a factorial design, model misspecification and varying levels of error in measurement and in model structure are shown to have significant effects on LISREL parameter estimates in a modified peer influence model. (Author/LMO)
Descriptors: Analysis of Variance, Computer Simulation, Error of Measurement, Estimation (Mathematics)
Peer reviewed Peer reviewed
Weinberg, Sharon L.; Menil, Violeta C. – Multivariate Behavioral Research, 1993
The ability of 3-way INDSCAL and ALSCAL models to recover true structure in proximity data based on 2-dimensional configurations varying in number of subjects (15 and 20) and stimuli, amount of error, and monotonic transformation is examined. INDSCAL outperformed metric and nonmetric ALSCAL in all conditions. (SLD)
Descriptors: Analysis of Variance, Comparative Analysis, Computer Simulation, Computer Software Evaluation
Dickinson, Terry L. – 1985
The general linear model was described, and the influence that measurement errors have on model parameters was discussed. In particular, the assumptions of classical true-score theory were used to develop algebraic relationships between the squared multiple correlations coefficient and the regression coefficients in the infallible and fallible…
Descriptors: Analysis of Covariance, Analysis of Variance, Correlation, Error of Measurement
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
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