Classic parametric statistical tests, like the analysis of variance (ANOVA), are powerful tools
used for comparing population means. These tests produce accurate results provided the data
satisfies underlying assumptions such as homoscedasticity and balancedness, otherwise biased
results are obtained. However, these assumptions are rarely satisfied in real-life. Alternative
procedures must be explored. This thesis aims at investigating the impact of heteroscedasticity
and unbalancedness on effect sizes in two-way fixed-effects ANOVA models. A real-life
dataset, from which three different samples were simulated was used to investigate the changes
in effect sizes under the influence of unequal variances and unbalancedness. The parametric
bootstrap approach was proposed in case of unequal variances and non-normality. The results
obtained indicated that heteroscedasticity significantly inflates effect sizes while unbalancedness
has non-significant impact on effect sizes in two-way ANOVA models. However, the impact
worsens when the data is both unbalanced and heteroscedastic. / Statistics / M. Sc. (Statistics)
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:unisa/oai:uir.unisa.ac.za:10500/23287 |
| Date | 31 October 2017 |
| Creators | Chaka, Lyson |
| Contributors | Muchengetwa, S., Rapoo, E. |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | 1 online resource (xi, 119 leaves) : illustrations |
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