Testing for Homogeneity

Homogeneity

When conducting bivariate or multivariate tests, to check our data is normally distributed, we utilise Q-Q plots. We can run Q-Q plots for normal distribution and then run a Levene’s Test for homogeneity. If we wanted to test the hypothesis: There will be a gender difference in Life Satisfaction in Great Britain.

Running and Interpreting Levene’s Test for Homogeneity 

Levene’s tests whether our data is homogenous (or not) homogenous means the sample has similar characteristics. To run a levene’s test for this example we would use the following command: 

leveneTest(opintfd$`Life Sat`,opintfd$Gender)

To ensure a homogenous sample the result must not be significant. It must be above p>0.05. This would mean our sample is homogenous and meets parametric assumptions. 

Our Levene’s test is p=.242 which means that our data is homogenous. 

Presenting findings for assumption testing 

The data was tested prior to further analysis in order to ascertain whether it met parametric assumptions. An assessment of Q-Q plots demonstrated that the data was approximately normal. The Levene’s test (p=.242) showed that our data was homogenous, hence the data met parametric assumptions and a parametric test t-test was selected for further analysis of this data. 

Below is an example of data that is not homogenous. 

The data was tested prior to further analysis in order to ascertain whether it met parametric assumptions. An assessment of Q-Q Plots demonstrated that the data was approximately normal. The Levene’s test (p = .017) showed that the data was heterogeneous; hence the data failed to meet parametric assumptions and a non-parametric test Mann-Whitney was selected for further analysis of this data.

Now you have completed both sprints on parametric assumptions, check out the how-to video on normality and homogeneity…