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Repeated measures ANOVA controlled by co-variable. Why I can not carry out a posthoc in co-variable?

Good afternoon,

I hope everything is going well.

I am writing because I need to carry out some statistical analyses and I am very naive on it. Given that I have a question:

I have carried out a repeated measures ANOVA 2x2x2. I have controled by a co-variable (Autistic Quotient), and I got some significative interactions (p-value < 0,05).


https://forum.cogsci.nl/uploads/529/B7FS6W4M2KH8.png There was an error displaying this embed.


I performed a post-hoc analysis, but it is not possible to do it with AQ, even when I have a significan interacion between the variables "pse_condition * AQ" and "pse_condition * block * AQ". Do you have any clue about why I can not perform that analysis? Is there another way to know where is the significant interaction occurring?


https://forum.cogsci.nl/uploads/635/CUF8LZVCS10D.png There was an error displaying this embed.


Many thank in advance!! and best wishes for your statistical enterprises,


Mayte

Comments

  • Post hoc tests are t tests that compare one category-level to another (with respect to mean values of the dependent variable). However, a covariate is an interval/ratio variable and so does not produce categories that can be subject to post-hoc t-testing.

    R

  • Thank you so much for your generous help 😊. I am just wondering what does it means that JASP shows as an output:

    pse_condition * AQ -> p= 0,044

    pse_condition * block * AQ -> p= 0,012

    It is weird to me that shows those interactions because the effect of the co-variable is controlled in all the interactions. Actually, if I remove the AQ covariable from the interface, the p-value from the pse_condition * block, also changes (see the image below).



    Thank you a lot in advance,


    Mayte Vergara Manríquez

  • It's not the case that 'effect' of the covariate is controlled in all interactions. It's controlled in all interactions *except*, of course, those interactions that actually include the covariate.

    'pse_condition * AQ -> p= 0.044' refers to the interaction between pse_condition and AQ.

    'pse_condition * block * AQ -> p= 0.012' refers to the interaction between pse_condition, block, and AQ.

    R

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