# Principal component analysis / multinomial logistic regression

I'm trying to see how level of scepticism impacts willingness to change diet. To measure sceptism I've used a 7 point likert scale. The study I'm basing my research on used a principal components analysis when analysing the Likert scale results to measure level of sceptism. I've got this far. But then the study uses a multinomial logistic regression to assess how skepticism effects willingness to change. Any idea on how to take the results of the PCA and put them into the MLA? (I'm using SPSS)

• Why did they use multinomial logistic regression? What is your response variable? If it's just willing / not willing, then regular logistic regression is appropriate. What are you / they doing PCA on? If you have only 1 var, there is no point in doing PCA. – gung May 7 '15 at 19:41
• the response variable is 'certainly', 'I'm already doing that' and 'No'. The PCA was conducted on 5 items on the likert scale. The study says 'the factor score of the first unrotated component was used as a measure of skepticism.' I don't have to follow the study, I'm just interested in measuring skepticism from the likert scale and then comparing it to willingness to change – Tim Rose May 7 '15 at 19:48
• Do you also have 5 likert items, or only 1? Are you interested in all of those responses individually? If so, would it be reasonable / would you be willing to think of them as ordinally related (ie, already > certainly > no)? – gung May 7 '15 at 19:54
• Yes, also 5 items. no I'm more interested in making a measure of skepticism in general, not each individual item. I'm beginner so I'm interested in doing what is going to be easiest for me, I'm not too sure what ordinally related means – Tim Rose May 7 '15 at 20:00
• No, I mean for your response variable you can have a categorical variable w/ 3 nominal / unrelated categories, or 3 ordinally related categories. If you think the latter is reasonable, you can use ordinal logistic regression instead of multinomial LR. The former will be more powerful & informative. – gung May 7 '15 at 20:04