In my research I have one concept consisting of six attributes. This whole concept forms my dependent variable. So basically I have six dependent variables measured on ordinal scale (five point Likert scale).
I have three independent variables, also ordinal (five point Likert scale). From here Logistic regression with ordinal variables I got that I can use ordinal regression method by converting my ordinal independent variables to categorical (because this is my main problem now, I don't understand which method to take since my independent vars are ordinal)
My research questions are to find positive influence of every independent variable on the whole dependent concept (or on every attribute of the concept).
- Is this approach achievable in SPSS?
- and would it work and produce valid results?
- "converting" ordinal independent vars to categorical is not "recoding"?
1) You can either use the Order Logit regression or the Order probit regression.
I do not know whether this approach works in SPSS, but here there is a nice code for the Order Logit Regression in R.
library(MASS) m <- polr(independentvar ~ var1 + var2 + var3, data = ghost291data, Hess=TRUE)
2) You get the following output:
- A list of coefficients like for any regression
- Two intercepts which indicate the differences between the different ordinal datas. You will get n-1 intercepts for n categories of the independent variables.
3) The Algorithm from the
MASS package does the recoding for you.