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Jeromy Anglim
Jeromy Anglim
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Mixed ANOVA

I think a 2 (between subjects; alcohol group: low, high) by 3 (within subjects; condition; reward, neutral, punishment) mixed ANOVA would probably be a good way to analyse the data.

  • The main effect of group would tell you overall whether the groups differ in mean performance levelaveraged across the three conditions
  • The main effect of condition would tell you whether overall performance varied across conditions.
  • The group by condition interaction would tell you whether the effect of condition varied across groups.

Potential follow up tests

You could potentially perform follow up tests to further decompose the two main effects and interaction effects. You may wish to make performance of follow up tests conditional on the outcome of the initial main effect and interaction significance tests. Here are a few ideas:

  • If the interaction effect is significant, perform analysis of simple effects, or interaction contrasts.
  • If interaction effect is not significant or if performing analysis of simple effect of condition, and the condition effect is significant, then do some form of contrast or pair wise comparison of means to decompose effect of condition.

Alternatively, if you don't need statistical significance tests for all the little pairwise comparisons, you could just present a table or graph of the means (and some measure of error or variation) and comment on what you think the significance tests for the initial main and interaction tests mean. This is less rigorous, but sometimes sufficient.

Comments on the MANOVA Idea

I'm not a fan of the idea of running a MANOVA with group as IV and the three conditions as DVs. You wouldn't be testing the effect of condition or the interaction effect. If you truly don't care about such things, then the MANOVA would be a reasonable option; or you could simply create a composite variable (i.e., mean of the three conditions) and do an independent groups t-test on the composite.

Mixed ANOVA

I think a 2 (between subjects; alcohol group: low, high) by 3 (within subjects; condition; reward, neutral, punishment) mixed ANOVA would probably be a good way to analyse the data.

  • The main effect of group would tell you overall whether the groups differ in mean performance level across the three conditions
  • The main effect of condition would tell you whether overall performance varied across conditions.
  • The group by condition interaction would tell you whether the effect of condition varied across groups.

Potential follow up tests

You could potentially perform follow up tests to further decompose the two main effects and interaction effects. You may wish to make performance of follow up tests conditional on the outcome of the initial main effect and interaction significance tests. Here are a few ideas:

  • If the interaction effect is significant, perform analysis of simple effects, or interaction contrasts.
  • If interaction effect is not significant or if performing analysis of simple effect of condition, and the condition effect is significant, then do some form of contrast or pair wise comparison of means to decompose effect of condition.

Alternatively, if you don't need statistical significance tests for all the little pairwise comparisons, you could just present a table or graph of the means (and some measure of error or variation) and comment on what you think the significance tests for the initial main and interaction tests mean. This is less rigorous, but sometimes sufficient.

Comments on the MANOVA Idea

I'm not a fan of the idea of running a MANOVA with group as IV and the three conditions as DVs. You wouldn't be testing the effect of condition or the interaction effect. If you truly don't care about such things, then the MANOVA would be a reasonable option; or you could simply create a composite variable (i.e., mean of the three conditions) and do an independent groups t-test on the composite.

Mixed ANOVA

I think a 2 (between subjects; alcohol group: low, high) by 3 (within subjects; condition; reward, neutral, punishment) mixed ANOVA would probably be a good way to analyse the data.

  • The main effect of group would tell you overall whether the groups differ in mean performance averaged across the three conditions
  • The main effect of condition would tell you whether overall performance varied across conditions.
  • The group by condition interaction would tell you whether the effect of condition varied across groups.

Potential follow up tests

You could potentially perform follow up tests to further decompose the two main effects and interaction effects. You may wish to make performance of follow up tests conditional on the outcome of the initial main effect and interaction significance tests. Here are a few ideas:

  • If the interaction effect is significant, perform analysis of simple effects, or interaction contrasts.
  • If interaction effect is not significant or if performing analysis of simple effect of condition, and the condition effect is significant, then do some form of contrast or pair wise comparison of means to decompose effect of condition.

Alternatively, if you don't need statistical significance tests for all the little pairwise comparisons, you could just present a table or graph of the means (and some measure of error or variation) and comment on what you think the significance tests for the initial main and interaction tests mean. This is less rigorous, but sometimes sufficient.

Comments on the MANOVA Idea

I'm not a fan of the idea of running a MANOVA with group as IV and the three conditions as DVs. You wouldn't be testing the effect of condition or the interaction effect. If you truly don't care about such things, then the MANOVA would be a reasonable option; or you could simply create a composite variable (i.e., mean of the three conditions) and do an independent groups t-test on the composite.

Source Link
Jeromy Anglim
Jeromy Anglim
  • 45.7k
  • 24
  • 157
  • 259

Mixed ANOVA

I think a 2 (between subjects; alcohol group: low, high) by 3 (within subjects; condition; reward, neutral, punishment) mixed ANOVA would probably be a good way to analyse the data.

  • The main effect of group would tell you overall whether the groups differ in mean performance level across the three conditions
  • The main effect of condition would tell you whether overall performance varied across conditions.
  • The group by condition interaction would tell you whether the effect of condition varied across groups.

Potential follow up tests

You could potentially perform follow up tests to further decompose the two main effects and interaction effects. You may wish to make performance of follow up tests conditional on the outcome of the initial main effect and interaction significance tests. Here are a few ideas:

  • If the interaction effect is significant, perform analysis of simple effects, or interaction contrasts.
  • If interaction effect is not significant or if performing analysis of simple effect of condition, and the condition effect is significant, then do some form of contrast or pair wise comparison of means to decompose effect of condition.

Alternatively, if you don't need statistical significance tests for all the little pairwise comparisons, you could just present a table or graph of the means (and some measure of error or variation) and comment on what you think the significance tests for the initial main and interaction tests mean. This is less rigorous, but sometimes sufficient.

Comments on the MANOVA Idea

I'm not a fan of the idea of running a MANOVA with group as IV and the three conditions as DVs. You wouldn't be testing the effect of condition or the interaction effect. If you truly don't care about such things, then the MANOVA would be a reasonable option; or you could simply create a composite variable (i.e., mean of the three conditions) and do an independent groups t-test on the composite.