The plan is to create a 2x2 between subject research design
1. Seperate the subjects into two groups based on the measurement of the independent variable (group identification: high vs low).
2. Same Measurement for 2 dependent variable (pretest) for both group
3. Each subject on each group randomly receive 2 different treatment (T1 vs T2)
4. After receiving the treatment, all the subject take the same measurement for the 2 dependent variable (posttest).
So I have 4 cell based on their group identification (high vs low) X Treatment (T1 vs T2)
Basically, what I want to know:
1. Is there a different/change between pretest and posttest for each cell?
2. Comparing the score of both dependent variable between each cell?
I'm wondering how to do the statistical analysis. Thanks
You can run an ANCOVA, i.e. a regression:
post = pre + group
Along with the normality of the distributions of pre and post, there should not be an interaction between group*pre; this is the homogeneity of slopes assumption.
see:
https://www.real-statistics.com/analysis-of-covariance-ancova/assumptions-ancova/
Hello Agrandaiz,
I agree with Jos Feys ' recommendation that ancova is a strong candidate for the analysis. I would modify the model, however, to: post ~ pre + group + treatment + group x treatment, given your description of the study design.
The other alternative is to treat pre & post scores as a third factor (a repeated measures factor), but in this approach the test of interest has to do with whether there is an interaction between group & measure (pre, post), treatment & measure, and/or group x treatment & measure (and not so much the other effects).
As Jos indicates, there are assumptions (including normality, homogeneity, independence of observations, homogeneity of regression) that should be reasonable for the data set in order to go this route.
Good luck with your work.
I agree with use of ANCOVA, but wouldn't recommend that you create groups based on low or high identification. This loses information from the identification variable and can introduce superiors statistical significance.
This then gives you:
post ~ pre + identification + treatment + identification x treatment
It is important in some packages to centre the continuous predictors (and this generally aids interpretation). Do this before computing/creating the interaction term.
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