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The output seems fine if specify all the factors as main effects rather than interactions.
- This reply was modified 4 years, 6 months ago by Saoirse Connor Desai.
I guess this could be due to the somewhat unorthodox design…
I believe I’m running the latest version of afex (0.18.0) and am running the latest version of R. It also occurs with methods.
This is the output that I get:
Mixed Model Anova Table (Type 3 tests, KR-method)
Model: Inference ~ Correction * Narrative + Order + (1 | participant)
Effect df F p.value
1 Correction 3, 99 34.38 *** <.0001
2 Narrative 3, 99 7.33 *** .0002
3 Order 3, NA NA <NA>
4 Correction:Narrative 6, 99 6.48 *** <.0001
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1
Would it be possible to send you the data? If so, what would be the best method of doing this?
Yes, that works and I can obtain F and p values. Here is the output though.
As you can see, the output for
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.
Thank you, this is very helpful. I have tried this approach and it does work. There was one thing I was unsure about. When I use the anova() function to obtain F ratios and p values for the fixed effects and interaction, there are values for everything except Order. Is there a reason that there are no values for this fixed effect?
This study will actually be included in my PhD thesis, so the simulation study you suggest would actually be very useful. When you say to run a simulation study from generated data without effect, which effect do you mean exactly?
Many thanks for all your help.
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),
narrative=factor(c(“F”, “V”, “H”, “M”)),
correction=factor(c(“NC”, “CO”, “CE”, “CL”)))
- This reply was modified 4 years, 6 months ago by henrik. Reason: corrected image link
Thanks for your help. I think there are several issues with my design that make it a little complicated to analyse. For instance, I have several replications but a number are not complete replicates.
One approach I have seen used is to analyse the residuals rather than the raw scores – so that I can report something in my thesis – with the proviso that I run a new study with a different design.
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.