Choosing Between One-Way and Two-Way ANOVA for Effective Research Analysis

A t-test is generally used to compare the means of two groups, while ANOVA provides a general linear-model framework for comparing means across multiple factor levels. When more than two groups are compared, ANOVA can assess whether there is evidence of differences among the group means while controlling the overall Type I error rate more effectively than conducting multiple pairwise t-tests..

Assumptions for ANOVA

• Before conducting ANOVA, assess key assumptions such as independence of observations, approximate normality of model residuals, and homogeneity of variance. The degree to which assumption violations affect the results depends on factors such as sample size, design balance, and the nature of the violation. When assumptions are not adequately met, appropriate options may include robust methods, data transformations, or alternative statistical approaches suited to the research design. Normality assumption – The model residuals or errors should be approximately normally distributed. This assumption can be assessed using methods such as Q-Q plots, while formal tests such as the Shapiro–Wilk test may also be considered alongside graphical and contextual assessment.Variance homogeneity assumption – the variances in all groups are similar (use Levene’s test)
• Independence of observations
• The dependent variable must be continuous