Solved – Ordinal data in regression

ordinal-dataregression

My data is almost only ordinal data. My dependent variable is a 4 point scale:

1 = completely agree
2 = agree to some extent
3 = disagree to some extent
4 = completely disagree

I want to use regular OLS using SPSS. Should I use ordinal regression? What is the difference between ordinal regression and linear regression? When I try to use linear and my dependent variable, SPSS refuses to run the analysis. What should I do?

Best Answer

Since your response is ordinal then you should use ordinal regression. At a very high level, the main difference ordinal regression and linear regression is that with linear regression the dependent variable is continuous and ordinal the dependent variable is ordinal.

Now you can usually use linear regression with an ordinal dependent variable but you will see that the diagnostic plots do not look good. When you say SPSS won't run the linear regression what do you mean? Are you getting an error?

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Solved – Ordinal Independent Variables and Dependent Variable Regression Analysis

Likert scale is also known as summated score. You can average the score to get a single variable from different items. Alternatively, you can use Factor Analysis for dimension reduction.

For multiple regression, you can simply use your model

Creativity == constant +  β1 Climate +  β2 Motivation + error

If there is literature support for presence of mediation effect. You can use Sobel's test (If sample size is good enough) or bootstraping to test for it.

Analysis should come after theory. Only on the basis of past evidences, you can take variable 'motivation [or climate] as a mediator or moderator [More complex relationship may occur], analysis for both is different'

Make sure to test for model assumptions as well.

Solved – Ordinal or binomial regression

The model you choose is based on the structure of your outcome variable, not on your model covariates (or independent variables, as you refer to them here), because it is the outcome variable that imposes restrictions on the predicted values from your model. If your outcome is dichotomous, you would use a model where the predicted values would fall between 0 and 1. In this case, as you correctly pointed out above, an ordinary logit or probit would suffice. You would enter your ordinal independent variable as (k-1) dummy variables, where k is the number of categories in that ordinal variable. Standard interpretation applies where the odds ratios or marginal effects for each dummy variable is interpreted relative to the excluded category.

Best Answer

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Solved – Ordinal Independent Variables and Dependent Variable Regression Analysis

Solved – Ordinal or binomial regression

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