Non-parametric alternatives to ANOVA Flashcards
non-parametric tests
- no population parameters
- no stringent assumptions
- can deal with small samples
- data can be measured on any scale
parametric tests
- population parameters
- rigorous assumptions
- minimum sample size required
- require interval or ratio data
example of non-parametric tests
chi square test
kruskal wallis test
Friedman test
when to use non-parametric tests
- data is non-normal
- outliers
- DV isn’t interval or ratio
- assumptions are violated
- small sample size
- unequal sample sizes
why not use all the time?
- parametric tests are more powerful and more likely to find differences
- means, SD and error variances vs rankings and frequencies
Kruskal Wallis test is alternative to…
1 way between-subjects anova
alternative to 1 way between-subjects anova
Kruskal wallis test
what is the Kruskall wallis test/what does it do?
generalisation of mann whitney u
tests for sig. differences in 2+ groups of IV/factor on a continuous or ordinal DV
SPSS outputs for Kruskall wallis test
descriptive stats (report median)
ranks (mean rank)
test stats (X^2(df)=KW value, p= )
reporting results of non-parametric tests
- present medians in a table or text
- why was it selected (violated assumptions)
- test type, effect of IVs on DV
- test results (df, test statistic, p value)
- post hoc results
interpret
follow up test for kruskall wallis test
- planned follow up test
- b-s, non-parametric alternative to independent t-test is mann whitney u
- apply bonferroni adjustment
- if KW test is sig. need to do followup pairwise analyses like in ANOVA
non-parametric alternative to 1-way with-subjects ANOVA
friedman test
what is the friedman test an alternative to?
non-parametric alternative to 1-way with-subjects ANOVA
what is the Friedman test and what does it test for?
generalisation of wilcoxon signed ranks
test for sig. difference in 2+ groups of IV/factor on a continuous or ordinal DV
SPSS outputs for friedman test
descriptive stats - medians
ranks - mean ranks = highest score
test stats - X^2(df)=chi-square, p value