Dear all, I am struggling to make sense of ranova with multcompare vs JASP (a statistical program). They are providing different results.
What I have is a table where rows correspond to subjects, and columns correspond to conditions. I want to compare each condition to each other condition. Therefore I am using ranova and multcompare.
load('data.mat','resultsAll')
rTable = array2table(resultsAll);
varNames = strsplit(string(num2str(1:size(resultsAll,2))));
withinDesign = table(varNames','VariableNames',{'State'});
withinDesign.State = categorical(withinDesign.State);
conditionString = sprintf('%s-%s ~ 1',rTable.Properties.VariableNames{1},rTable.Properties.VariableNames{end});
rm = fitrm(rTable,conditionString,'WithinDesign',withinDesign);
fullTable = ranova(rm,'WithinModel','State');
disp(fullTable)
SumSq DF MeanSq F pValue pValueGG pValueHF pValueLB
________ __ _________ ______ __________ _________ __________ _________
(Intercept) 0.43612 1 0.43612 26.656 0.0066842 0.0066842 0.0066842 0.0066842
Error 0.065443 4 0.016361
(Intercept):State 0.43911 5 0.087821 20.282 3.2733e-07 0.0019177 0.00014406 0.010793
Error(State) 0.086601 20 0.0043301
c = multcompare(rm,'State');
disp(c);
State_1 State_2 Difference StdErr pValue Lower Upper
_______ _______ __________ _________ ________ __________ _________
1 2 0.3161 0.056406 0.02833 0.048612 0.58359
1 3 0.25331 0.059127 0.069544 -0.027085 0.5337
1 4 0.33228 0.056094 0.023317 0.066273 0.59829
1 5 0.3353 0.067885 0.043651 0.01338 0.65723
1 6 0.33858 0.068912 0.044434 0.011784 0.66537
2 1 -0.3161 0.056406 0.02833 -0.58359 -0.048612
2 3 -0.062793 0.040148 0.65386 -0.25318 0.12759
2 4 0.016182 0.0072343 0.37993 -0.018125 0.050488
2 5 0.019203 0.01907 0.89461 -0.07123 0.10964
2 6 0.022477 0.019813 0.84709 -0.071481 0.11643
3 1 -0.25331 0.059127 0.069544 -0.5337 0.027085
3 2 0.062793 0.040148 0.65386 -0.12759 0.25318
3 4 0.078975 0.037339 0.42181 -0.098094 0.25604
3 5 0.081996 0.035042 0.3474 -0.08418 0.24817
3 6 0.085269 0.034084 0.30158 -0.076361 0.2469
4 1 -0.33228 0.056094 0.023317 -0.59829 -0.066273
4 2 -0.016182 0.0072343 0.37993 -0.050488 0.018125
4 3 -0.078975 0.037339 0.42181 -0.25604 0.098094
4 5 0.0030213 0.016544 0.99994 -0.075434 0.081477
4 6 0.0062946 0.017727 0.99861 -0.077772 0.090361
5 1 -0.3353 0.067885 0.043651 -0.65723 -0.01338
5 2 -0.019203 0.01907 0.89461 -0.10964 0.07123
5 3 -0.081996 0.035042 0.3474 -0.24817 0.08418
5 4 -0.0030213 0.016544 0.99994 -0.081477 0.075434
5 6 0.0032733 0.0025707 0.78816 -0.0089175 0.015464
6 1 -0.33858 0.068912 0.044434 -0.66537 -0.011784
6 2 -0.022477 0.019813 0.84709 -0.11643 0.071481
6 3 -0.085269 0.034084 0.30158 -0.2469 0.076361
6 4 -0.0062946 0.017727 0.99861 -0.090361 0.077772
6 5 -0.0032733 0.0025707 0.78816 -0.015464 0.0089175
On the other hand, JASP gives very different results for a repeated measures ANOVA.
I get the same F value of 20.282.
But the p-values in multiple comparisons is very different.
I am using all the default parameters of JASP.
