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"?

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#### Best Answer

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.