Let's say I run a repeated measures ANCOVA, with valence (negative and neutral) as repeated measure and age as a covariate. What I want to know is how age affects my data.

  1. I get a significant interaction between the covariate age and valence. As post-hoc I would do a linear regression, where I regress age on the difference between negative and neutral (negative minus neutral), since Anovas work on the difference between levels of the dependent variables.

  2. I get a significant covariate effect (between subject table) and as a post-hoc I am doing a linear regression, regressing age on the mean of valence ((negative+neutral)/2).

My questions,

  • First of all, is the approach to perform a repeated measures ANCOVA appropriate, since I am interested in the effect of the covariate and do not want to partial it out?
  • Further, are linear regressions, as described above appropriate as testing the type of the relationship between the covariate and the variables that were significant?
  • And last, is the way I would create the variable to be regressed on (mean for BS and difference between levels of the independent variable) how it should be? If not, on what should I regress the mean?

Thank you!!!!


1 Answer 1


For sure is too late, but you should work with linear mixed models instead of ancova or Lm. Your proposals include obtaining a new variable in both cases to consider it as a DV. With linear mixed models you dont need to do this ( and you can also include trial by trial data)

As a general example:

ID Condition value Age 1 neutral 6 35 1 negative 3.5 35 2 neutral 7 21 2 negative 2.3 21 .. . ...

Here, "value" is the average value of whatever you are using in each condition. In short, if you have this data structure you can easily analise it this way:

lmer(value ~ condition + age + (1|ID))

With this model you can test all that you said!

  • $\begingroup$ Sorry, I tried to represent a dataframe but I failed. To be concise, the data must be in long format! $\endgroup$ Mar 19, 2021 at 0:39
  • $\begingroup$ Isn't there a minimum cluster size of 3 observations per ID required for random effects models? $\endgroup$ Aug 27, 2021 at 11:31

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