Data Assumptions: Univariate Normality 5.9k views.Data Assumptions: Its about the residuals, and not the variables’ raw data 6.3k views.Outlier cases – univariate outliers 6.5k views.Which Test: Factor Analysis (FA, EFA, PCA, CFA) 6.8k views.
Analysis of Covariance (ANCOVA) 6.9k views.Outlier cases – bivariate and multivariate outliers 9.1k views.Getting the hang of z-scores 10.1k views.Data Assumption: Homogeneity of regression slopes (test of parallelism) 11.8k views.Data Assumption: Homogeneity of variance (Univariate Tests) 12.1k views.Which Test: Logistic Regression or Discriminant Function Analysis 13.8k views.Repeated Measures ANOVA versus Linear Mixed Models.One-Sample Kolmogorov-Smirnov goodness-of-fit test 15.6k views.Data Assumption: Homogeneity of variance-covariance matrices (Multivariate Tests) 19.1k views.Which Test: Chi-Square, Logistic Regression, or Log-linear analysis 20.4k views.The “compare means function” also allows for an easy comparison of variance across groups. Another easy visual is to compare the mean, variance, and skewness of groups in your statistics programme’s “explore” or “descriptives” command.Do side-by-side box-plots of each group and if the width of the boxes does not vary markedly by group, it suggests no violation of the assumption. Simple box-plots is easy to grasp the graphical way of checking for the lack of homogeneity of variances.The plot should have no obvious pattern (so random data points is evidence of no violation of the assumption). The GLM procedures provide a scatterplot of “spread versus level plot”.Hartley’s F-max test is favoured by some researchers, while others argue that it is extremely sensitive to violations of normality.The Bartlett’s test of homogeneity of variance has largely been replaced by the Levene’s test.The recommendation is that when the Levene’s test is significant (indicating a violation of the assumption of homogeneity of variance), then use Brown & Forsythe’s test and if this is also significant, then accept and report the results of the latter. The Brown & Forsythe’s test of homogeneity of variances is also generally more robust than the Levine’s test when group sizes are highly unequal and with highly skewed data.The Welch test could be better when group sample sizes are highly unequal.In either case, the Levene’s and Box’s M tests should be non-significant. MANOVA), it involves variance/covariance matrices so we need to use the Box’s M test to test for homoscedasticity. With a multivariate procedure (where we have more than one metric dependent variable, e.g. Levene’s test is the most commonly used with a single metric dependent variable.
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