Testing for homogeneity of variance with Hartley's Fmax test: In order to use a parametric statistical test, your data should show homogeneity of variance: in other words, the spread of scores in each condition should be roughly similar. (The spread of scores is reflected in the variance, which is simply the standard deviation squared).
This paper explains 14 representative HOV tests for 5 types of research situations and concludes with a conceptual summary of four major approaches to HOV testing. Homogeneity of variance (HOV) is a major assumption underlying the validity of many parametric tests. More importantly, it serves as the null hypothesis in substantive studies that focus on crossor within-group dispersion. Despite a

σ 2 is variance, x i is a set constituent, μ is the sample mean, and N is the total number of set constituents. You may think this formula is very similar to the SD formula. That is because variance is SD squared, hence being denoted as σ 2. In the previous section, the SD was ±2.96 units. Should we want to obtain the variance, we just

We will use Bartlett's test to test the assumption that variances are equal across groups. Specify Significance Level. The significance level is the probability of rejecting the null hypothesis when it is true. Researchers often choose 0.05 or 0.01 for a significance level. For the purpose of this exercise, let's choose 0.05.

Basic Concepts. We now show another test for homogeneity of variances using Bartlett’s test statistic B, which is approximately chi-square:. where k = number of groups, each of which contains n j elements, and s 2 is the pooled variance, which as we have seen elsewhere is MS W, and
1. Introduction. The Brown and Forsythe (1974) modification of Levene’s test (1960), commonly referred to as test 50, is perhaps one of the most widely used procedures for testing the homogeneity (equality) of variances. In part, test 50 is popular because it is robust and is asymptotically distribution free.
One of the main assumptions for the ordinary least squares regression is the homogeneity of variance of the residuals. If the model is well-fitted, there should be no pattern to the residuals plotted against the fitted values. If the variance of the residuals is non-constant then the residual variance is said to be “heteroscedastic.”
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    • how to test homogeneity of variance