Common ratio-based approaches for analyzing gene expression microarray data do not provide a framework for handling replication, although replication is clearly desirable for these noisy data. In contrast, replication fits naturally into analysis of variance (ANOVA) methods. We use ANOVA to analyze data from a microarray experiment to compare gene expression in drug-treated and control cells li...

the aim of present research was to investigate psychometric properties (reliability ,validity and factor analysis) of children depression inventory (kovacs,1981) in boy and girl students of junior high schools in ahvaz. 399 boy and girl students were randomly selected by multi-stage sampling. the results of factor analysis extracted six factors. among those, the greatest proportion of variance ...

Factorial models and analysis of variance have been a central pillar of statistical thinking and practice for at least 70 years, so the opportunity to revisit these topics to look for signs of new life is welcome. Given its lengthy history, Anova is an unpromising area for new development, but recent advances in computational techniques have altered the outlook regarding spatial factors, and co...

Analysis of variance (ANOVA) is a statistical method that is widely used in the psychosomatic literature to analyze the results of randomized trials, yet ANOVA does not provide an estimate for the difference between groups, the key variable of interest in a randomized trial. Although the use of ANOVA is frequently justified on the grounds that a trial incorporates more than two groups, the hypo...

This article presents a Bayesian hypothesis test for analysis of variance (ANOVA) designs. The test is an application of standard Bayesian methods for variable selection in regression models. We illustrate the effect of various g-priors on the ANOVA hypothesis test. The Bayesian test for ANOVA designs is useful for empirical researchers and for students; both groups will get a more acute apprec...

This study proposes robust means modeling (RMM) approaches for hypothesis testing of mean differences for between-subjects designs in order to control the biasing effects of nonnormality and variance inequality. Drawing from structural equation modeling (SEM), the RMM approaches make no assumption of variance homogeneity and employ robust estimation/rescaling strategies in order to alleviate re...