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I have 3 independent variables (1-5 Likert Scale) questions and I want to check how well these three can predict/explain my DV (1-5 Likert scale)

The three independent variables are: 1. Quality of information 2. Accessibility of staff 3. Quality of technical advice

My DV is: Overall evaluation of service center

All variables are ordinal (1 = low... 5 = high)

Which analysis would be appropriate to run here? I would prefer an easy approach and I think Ordinal Logistic Regression is way too complicated. Can I use a Linear Regression?

Basically, I want to be able to say that (for example) "quality of technical advice" is better at predicting "overall evaluation" than "Accessibility of staff"

Also, I have a 0 value on all variables ("No opinion", so in fact all are measured on 0-5 Likert scale)). How should I treat this variable? Can I replace the 0s with the mean of the observations?

Many thanks!

Fredrik

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Likert scales are usually "disagree" to "agree" scales and not "low" to "high". See wikipedia http://en.wikipedia.org/wiki/Likert_scale -

In your case of a low to high scale, no opinion seems a missing value and not a value below "low, so i would argue you should not use a 0 there.

Methodologically I think there is no problem in using a Linear regression. In true Likert scales (disagree to agree) the item can be seen as an interval variable with interval characteristics and quasi-normal distribution (this info also in the wikipedia page), so no problems in using a linear regression. The problem is that your scale may not be a true Likert and therefore you will be in shakier grounds.

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  • $\begingroup$ Many thanks Jacques! Actually, the scales are (1-5) as below: Not important - very important, Not Satisfied - very satisfied, & Performs much worse - Performs much better. Does that change your answer? $\endgroup$ Commented Aug 25, 2014 at 6:28
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    $\begingroup$ Your answers seems Likert like. Given that, the "no comment' "no opinion" answers are valid answers and correspond to the middle of the scale. Ordered Logistical Regression as suggested by @Walter is a more correct method but I think OLS is also applicable. Be aware that you may face some problems such a OLS result that is above your maximum value (2?) This is discussed in this CV question stats.stackexchange.com/questions/92902/… $\endgroup$ Commented Aug 25, 2014 at 12:55
  • $\begingroup$ Thanks Jacques! I know that the interpretation of the OLS variables will be tricky, but at this point I'm mostly looking for significance and an idea which IV is the stronger predictor. $\endgroup$ Commented Aug 26, 2014 at 7:17
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From the reading that I've done, it seems like an Ordered Logit (Ordered Logistical Regression) would be most appropriate here.

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  • $\begingroup$ Thanks Walter. I have also noticed that OLR would be appropriate, but personally I find it hard to interpret the results and to present the results in a straightforwards way. $\endgroup$ Commented Aug 25, 2014 at 6:27

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