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So I am having a rather simple problem of how to create a table of means in R and post-hoc t-tests.

Data:

DV: Score (Continuous variable)

IV1: Country (Nominal, 3 levels)

IV2: Group (Nominal, 4 levels)

IV3: Condition (Nominal, 2 levels)

I have run an ANOVA with interactions and after removing non-significant interactions, I am left with a model that looks like this.

DV ~ IV1+ IV2+ IV3 + IV1*IV2

IV3 is non significant, but the rest are.

The question I have now is how do I create a table of means split by country and group?

E.g.,

            America         UK            France

Group 1       10.3         9.2              11.4

Group 2       7.8          8.6               2.4 

Group 3       4.5          10.7              8.6

Group 4       10.5        10.5               10.6

Secondly how do I then run comparisons to see where the significant interaction is?

I know the pairwise function but I seem to only get that to work for saying comparing the 4 groups, or the 3 countries

pairwise.t.test(Data$Score, Data$Country)

As

pairwise.t.test(Data$Score, Data$ Country, by = Data$Group)

Seems to do the exact same

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1 Answer 1

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First, you should not remove non-significant terms from a model simply based on p-values, that is not their purpose.

Second, to get means for each combination of groups (and comparisons) use a linear model.

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