I have a question about how to interpret a regression analysis I did following a factor analysis.
I did principal axis factoring (direct oblimin)
I got a 3 factor solution. The three factors were (1) feelings (2) kinship and (3) interactions. (I'm looking at father-child relationships)
Regarding factor loadings, (1) feelings had all positive factor loadings, (2) kinship had all negative factor loadings and (3) interactions had all negative loadings.
I did a multiple regression analysis using the factor scores - saved as regression scores - as outcome variables. I'm not bringing them together to form a total scale, so I don't think I need to do a MANOVA instead, or adjust the p-value.
For (1) feelings, all of my predictor variables have positive beta weights
For (2) kinship, one of my predictors (participation in healthcare) has a negative beta weight
For (3) interactions, again, one of my predictors (participation in healthcare) has a negative beta weight
I'm confused about whether the predictors with negative beta weights actually imply that less participation in healthcare means a higher score on kinship and on interactions. Or whether, because those two outcome variables have all negative factor loadings, should the negative beta be interpreted as a positive beta? (i.e. do the negatives cancel each other out?).
If the latter is the case, do the predictors with a positive beta for outcome variables (2) and (3) actually have negative betas?