The effect of a nonlinear regression term on the behavior of the standard analysis of covariance (ANCOVA) F test was investigated for balanced and randomized designs through a Monte Carlo study. The results indicate that the use of the standard analysis of covariance model when a quadratic term is present has little effect on Type I error rates…

The performance of analysis of covariance (ANCOVA) and six selected competitors was examined under varying experimental conditions through Monte Carlo simulations. The six alternatives were: (1) Quade's procedure (D. Quade, 1967); (2) Puri and Sen's solution (M. Puri and P. Sen, 1969); (3) Burnett and Barr's rank difference scores (T. Burnett and…

This research is an empirical study, through Monte Carlo simulation, of the effects of violations of the assumptions for the oneway fixed-effects analysis of variance (ANOVA) and analysis of covariance (ANCOVA). Research reaffirms findings of previous studies that suggest that ANOVA and ANCOVA be avoided when group sizes are not equal. (SLD)

Studied, through Monte Carlo simulation, the conditions for which analysis of covariance (ANCOVA) does not maintain adequate Type I error rates and power and evaluated some alternative tests. Discusses differences in ANCOVA robustness for balanced and unbalanced designs. (SLD)

Compared the Type I error rate and the power to detect differences in slopes and additive treatment effects of analysis of covariance (ANCOVA) and randomized block designs through a Monte Carlo simulation. Results show that the more powerful option in almost all simulations for tests of both slope and means was ANCOVA. (SLD)

Used meta analytic methods to summarize results of Monte Carlo studies of test size and power of the F test in the single-factor, fixed-effects analysis of covariance model, updating and extending narrative reviews of this literature. (SLD)

This paper explores the use of K. G. Joreskog's congeneric modeling approach to reliability using censored quantitative variables. In addition, the compound problem of non-normality and attenuation that occurs when estimating censored continuous variables is discussed. (TJH)

This article proposes a method for testing equality of variances which exploits Pitman's idea and the computational power of simulations. Several advantages to this method are illustrated. A Monte Carlo study for several combinations of sample sizes and number of variables is presented. (Author/LMO)

This article shows how a multivariate approximate degrees of freedom procedure based on the Welch-James procedure as simplified by S. Johansen (1980) can be applied to the analysis of repeated measures designs without assuming covariance homogeneity. A Monte Carlo study illustrates the approach. (SLD)

Monte Carlo studies investigated effects of within-group covariance structure on subgroup recovery by several widely used hierarchical clustering methods. In Study 1, subgroup size, within-group correlation, within-group variance, and distance between subgroup centroids were manipulated. All clustering methods were strongly affected by…

The variance of the sample difference and the power of the "F" test for mean differences were studied under group matching on covariates and also under random assignment. Results shed light on systematic assignment procedures advocated to provide more precise estimates of treatment effects than simple random assignment. (TJH)

Provides an overview of standard and modified normal theory and asymptotically distribution-free covariance and correlation structure analysis techniques and details Monte Carlo simulation results on Type I and Type II error control. Demonstrates through the simulation that robustness and nonrobustness of structure analysis techniques vary as a…

The most widely used procedures to harness the power of a concomitant (nuisance) variable are block designs and analysis of covariance (ANCOVA). This study attempted to provide a scientific foundation on which to base decisions on whether to block or covary and how many blocks to be used if blocking is selected. Monte Carlo generated data were…

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)

The empirical performance under null/alternative hypotheses of the likelihood ratio difference test (LRDT); Lagrange Multiplier test (evaluating the impact of model modification with a specific model); and Wald test (using a general model) were compared. The new tests for covariance structure analysis performed as well as did the LRDT. (RLC)