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Yes, that works and I can obtain F and p values. Here is the output though.
As you can see, the output for
- Order
is NA. I have looked at the structure of the data and it is a factor. There are no warnings aside from the ones created by the output.
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Correction is in each cell. For example, in participant #1 would see Order 1 and see Fire (no correction), Van (correction), Head injury (explanatory correction #1), and missing person (exp correction #2). For Order 2, however, the participant would see a different pairing of Narrative and Correction. So all in all there 16 different combinations of Correction and Narrative but each participant saw four of these. I hope this makes sense. Here is a screenshot of the first few rows of my data frame.
So each participant provided 1 response per Narrative level and one response per Correction level. There is more than one response per cell but there are not an equal number of responses in each cell (e.g., cells 1-4 have 8 whereas cells 5-8 have 13).
Here is a mock-up of the what the first 4 rows of the data frame would look like or what participant #1 would see.
mat <- data.frame(participant=factor(rep(1)),
inference=c(4, 0, 0, 1),
ord=factor(rep(1)),
narrative=factor(c(“F”, “V”, “H”, “M”)),
correction=factor(c(“NC”, “CO”, “CE”, “CL”)))Hi Henrik,
So there were two repeated measures factors – Correction and Narrative. Order (the row factor – which corresponds to the specific pairing of correction and narrative that participants saw) was a between ss factor. Although participants were randomly assigned to the different orders there were, unfortunately, a different number of participants per cell.
Here is a screenshot of the design and how many participants there were per cell. (I hope you can see this!)
The presence of an interaction is a little troublesome I will probably need to run a new study with a different design.
Thank you!
