Heteroscedasticity produces a lack of type I error control in Studentâs t test for difference between means. Pretesting for it (e.g., by means of Leveneâs test) should be avoided as this also induces type I error. These pretests are inadequate for their objective: not rejecting the null hypotheses is not a proof of homoscedasticity; and rejecting it may simply suggest an irrelevant heteroscedasticity. We propose a method to establish irrelevance limits for the ratio of variances. In conjunction with a test for dispersion equivalence, this appears to be a more affordable pretesting strategy
A modification to testing pairwise comparisons that may provide better control of Type I errors in t...
Seven test statistics known to be robust to the combined effects of nonnormality and variance hetero...
A common question of interest to researchers in psychology is the equivalence of two or more groups....
Heteroscedasticity produces a lack of type I error control in Student’s t test for difference betwee...
Tests of equivalence, which are designed to assess the similarity of group means, are becoming more ...
Tests of equivalence, which are designed to assess the similarity of group means, are becoming more ...
Prior to comparison of means, there is k-population variances need to be tested. &n...
Investigating differences between means of more than two groups or experimental conditions is a rout...
One of the central messages of this dissertation is that (a) unequal variances may be more prevalent...
Numerous authors suggest that the data gathered by investigators are not normal in shape. Accordingl...
Preliminary tests for homoscedasticity may be unnecessary in general linear models. Based on Monte C...
In analysis of variance (anova), we generally assume that the error terms are independent and normal...
When multiple hypothesis tests are conducted on a single data set, it is necessary to control for th...
Several tests for group mean equality have been suggested for analyzing nonnormal and heteroscedasti...
This paper shows that a test for heteroskedasticity within the context of classical linear regressio...
A modification to testing pairwise comparisons that may provide better control of Type I errors in t...
Seven test statistics known to be robust to the combined effects of nonnormality and variance hetero...
A common question of interest to researchers in psychology is the equivalence of two or more groups....
Heteroscedasticity produces a lack of type I error control in Student’s t test for difference betwee...
Tests of equivalence, which are designed to assess the similarity of group means, are becoming more ...
Tests of equivalence, which are designed to assess the similarity of group means, are becoming more ...
Prior to comparison of means, there is k-population variances need to be tested. &n...
Investigating differences between means of more than two groups or experimental conditions is a rout...
One of the central messages of this dissertation is that (a) unequal variances may be more prevalent...
Numerous authors suggest that the data gathered by investigators are not normal in shape. Accordingl...
Preliminary tests for homoscedasticity may be unnecessary in general linear models. Based on Monte C...
In analysis of variance (anova), we generally assume that the error terms are independent and normal...
When multiple hypothesis tests are conducted on a single data set, it is necessary to control for th...
Several tests for group mean equality have been suggested for analyzing nonnormal and heteroscedasti...
This paper shows that a test for heteroskedasticity within the context of classical linear regressio...
A modification to testing pairwise comparisons that may provide better control of Type I errors in t...
Seven test statistics known to be robust to the combined effects of nonnormality and variance hetero...
A common question of interest to researchers in psychology is the equivalence of two or more groups....