Impact of unbalancedness and heteroscedasticity on classic parametric significance tests of two-way fixed-effects ANOVA tests
Year: 2016
Type: dissertation
Abstract: Classic parametric statistical tests, like the analysis of variance (ANOVA), are powerful tools \nused for comparing population means. These tests produce accurate results provided the data \nsatisfies underlying assumptions such as homoscedasticity and balancedness, otherwise biased \nresults are obtained. However, these assumptions are rarely satisfied in real-life. Alternative \nprocedures must be explored. This thesis aims at investigating the impact of heteroscedasticity \nand unbalancedness on effect sizes in two-way fixed-effects ANOVA models. A real-life \ndataset, from which three different samples were simulated was used to investigate the changes \nin effect sizes under the influence of unequal variances and unbalancedness. The parametric \nbootstrap approach was proposed in case of unequal variances and non-normality. The results \nobtained indicated that heteroscedasticity significantly inflates effect sizes while unbalancedness \nhas non-significant impact on effect sizes in two-way ANOVA models. However, the impact \nworsens when the data is both unbalanced and heteroscedastic.
Author: Lyson Chaka
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2765111255