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Factorial repeated measures ANCOVA

Is it possible to carry out a factorial repeated measures (pre- and post-study data) ANCOVA in JASP? Specifically, this would be similar to a mixed model analysis while controlling for pre-study data as the co-variate. Any help would be appreciated.

Comments

  • Thanks!

    Is there a tutorial as to how to input the data into the respective fields in the program for analysis?

  • edited October 2020

    Hi Exscistats,

    You can take a look at our "How to use JASP" page, which contains links to blogposts/youtube videos/gifs that illustrate particular analyses (for example ANOVA).

    Cheers

    Johnny

  • Thanks Johnny. I have looked at that page but it doesn't include a tutorial on a factorial RM Ancova; do you know if one exists? Or can anyone explain what I should do to carry out the analysis?

  • Hi Exscistats,

    There is a youtube video where they do a RM ANOVA in JASP, maybe that one is useful?

    In the table on the page, you can find it under RM ANOVA (so not factorial RM ANOVA).

    Or maybe I misunderstood your question, and you have pre and post scores for several repeated measures? In that case, you can use the compute columns function in JASP to compute change-scores for each of the repeated measures levels, and then use those as the input in the RM ANOVA (this should lead to equivalent results compared to adding them as covariates). Does that help?

    Cheers

    Johnny

  • My question for is for an ANCOVA; I am able to do the mixed model ANOVA on JASP, but the ANCOVA function seems that it is just for a one-way model, or perhaps I'm misunderstanding how to employ it?

  • For the ANCOVA you can specify multiple continous or factor covariates, with a single dependent variable. Maybe you are talking about MANCOVA? If so, that is not yet possible unfortunately, but it's high on my todo list!

  • Thanks Johnny. Is there a tutorial for the ANCOVA?

  • Yes, the ANCOVA youtube video from the table is this one: https://www.youtube.com/watch?v=Jxrq_T8InBY&feature=youtu.be

  • Great. Thanks!

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