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I’m looking to test pre vs post differences in working memory (using R), and have a covariate (IQ) that was only measured once. My data frame looks something like this:

d <- tibble::tibble(time = c(rep("pre", 10), rep("post", 10)),
  wrking_mem = c(abs(rnorm(10, 50, 10.2)), abs(rnorm(10, 70, 13.5))),
  iq = c(rnorm(10, 100, 15), rep(NA, 10)),
  participant_id = as.factor(rep(1:10, 2)))

The R packages I’m using require that the # of outcome observations be equal to the number of predictor / covariate observations to fit the linear model or else you get an error.

Wondering if anyone else has encountered a similar situation, and what you did to solve the issue?

Any help is great - thanks!

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  • $\begingroup$ How was the IQ measured? There are IQ test that involve the working memory. $\endgroup$
    – captcoma
    Oct 7, 2021 at 10:58
  • $\begingroup$ @captcoma Thanks for the comment. In this case, IQ is a hypothetical stand-in variable for any covariate that was measured once in a repeated measures experiment. Apologies, as I should have specified. $\endgroup$
    – giopico
    Oct 7, 2021 at 13:11

1 Answer 1

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Assuming that your intervention is not intended to change IQ, then it might be a reasonable assumption that IQ is constant, so the IQ before can be used as the IQ after.

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  • $\begingroup$ Thanks for this, @MaartenBuis. So simply repeating the same values a second time over sounds like the plan. Much appreciated! $\endgroup$
    – giopico
    Oct 7, 2021 at 13:08

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