I want to run ordinal logistic regression (OLR) in SPSS. My data include 6 predictor variable (two continuous and 4 categorical ) but my outcome variables are also 6 (categorical-likert scale).
e.g my dependent variable is Business development and 6 likert scale questions were asked for this, increase in profit, sales, size, asset, marketing and labour.
1-If I composite these 6 variable into one by first sum all the 6 variables and then recode them into again five categories like before (In SPSS in Transform, Compute variable and then recode into different variables). I think in this way I lost my original data and may categorize them wrong.
2-If I think of using Categorical Principle component analysis for reducing the dependent variables, it will give results (object scores) in continuous form on which I have to run linear regression. I do not want to run linear regression as my original data is categorical.
3-So option left is to run OLR without combining the dependent variable on each DV. It means that there is no model for Business development and 6 models for profit, sales….
My question is that
- Is it preferable to run OLR with each DV and then summarize the results
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OR Is there any other method for reducing DV and running OLR.
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OR Is there any better method than ordinal logistic regression for this data.
Best Answer
Among the many possible ways to analyze this dataset, a one that I would try first is multivariate linear regression. Multivariate means that you have multiple outcome variables (the six Likert scales). Linear, rather than logistic, means that you treat each Likert scale as a continuous output. (It's true that ordinal logistic methods are more appropriate theoretically for rating scales, but that seems like overkill for a first attempt.) Rencher, "Methods of Multivariate Analysis" seems like a decent introduction.