Showing papers on "Effect size published in 2001"
TL;DR: The use of noncentral test distributions (e.g., noncentral t, noncentral F) to evaluate power or to compute confidence intervals for effect sizes for some ANOVA applications is discussed in this paper.
Abstract: Most textbooks explain how to compute confidence intervals for means, correlation coefficients, and other statistics using “central” test distributions (e.g., t, F) that are appropriate for such statistics. However, few textbooks explain how to use “noncentral” test distributions (e.g., noncentral t, noncentral F) to evaluate power or to compute confidence intervals for effect sizes. This article illustrates the computation of confidence intervals for effect sizes for some ANOVA applications; the use of intervals invoking noncentral distributions is made practical by newer software. Greater emphasis on both effect sizes and confidence intervals was recommended by the APA Task Force on Statistical Inference and is consistent with the editorial policies of the 17 journals that now explicitly require effect size reporting.
115 citations
TL;DR: In this article, effect size indices for the d family and r family were introduced, along with formulas for their direct and indirect computation for both the t test and chi-square test.
Abstract: A statistically significant outcome only indicates that it is likely that there is a relationship between variables. It does not describe the extent (strength) of that relationship. In this article, emphasis is placed on the importance of assessing the strength of the relationship between the independent and dependent variables using effect size indices. Effect size indices for the d family and r family are introduced, along with formulas for their direct and indirect computation for both the t test and chi-square test. A subset of the variables and concepts examined in the Whittaker and Manfredo study are reported here to demonstrate why an effect size index should be computed. Statistical analyses (either t test or chi-square test) were performed on the original sample of 796 and three smaller sample sizes (398, 200, and 100) randomly selected from the initial sample. Effect size indices were computed for each statistical test. The results indicated that the size of the sample directly affects the t or ...
45 citations