I want to run a linear regression in SPSS
N = 1400
Outcome variable = rating from 0 to 800 (participants saw or heard a Mandarin speaker and had to rate how pleasant the speaker was feeling)
- "Status of Mandarin", i.e. 3-level nominal variable (whether the participants are native speakers / learners / non-speakers of Mandarin)
- "Condition", i.e. 4-level nominal variable (whether the participants saw a video recording / heard and audio recording / saw & heard a video recording with sound / only heard an unclear [i.e. low-pass filtered] audio recording)
- Culture, i.e. 10-level nominal variable (1O cultural clusters, some participants not being categorised in any of these clusters)
I dummy-coded all variables to include them in a linear model
As you see from this table, some levels of the "Culture" variable have too few observations, so basically I think that can only include the Confucian, the Anglo, and the Latin Europe groups. How do I deal with that with dummy coding? Should I include all 9 dummy-coded Culture variables in the model (excluding Confucian as I want that group as baseline) and only interpret the significance value and coefficients for the Anglo dummy variable and for the Latin Europe dummy variable?
I am also planning to look at interactions between Status of Mandarin and Condition (and ideally also between Status of Mandarin and Culture + between Condition and Culture, but I am not sure whether I can).
I am using SPSS at the moment but might switch to R.
In response to a comment by @rolando2, I am adding a picture of the instrument showing the gliding scales used to collect the ratings. I indeed will not consider a difference of, say 20, as meaningful.