Solved – Ordinal or binomial regression

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I have designed a question and now intend to use SPSS to analyse the results:

My dependent variable is: intention to vote. Yes or No.

My independent variables are: a series of question ranked on a scale from strongly disagree to strongly agree.

Do I need to use ordinal regression, if the dependent variable is Dichotomous? Even thought the independent variables are ordinal.

Or do I need to use binomial logistic regression, as my dependent variable is simply yes or no? Would it be possible to do this with ordinal independent variables?

Many thanks.

Best Answer

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.

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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 – Is the sum of a number of ordinal variables still ordinal

When you say things like 4+1 = 3+2 = 5, -- which you must do when you sum the components -- you (pretty much unavoidably) assumed they were interval at that time.

[If the components weren't interval, in general 4+1 $\neq$ 3+2 ... so you'd certainly have no business calling both of them "5".]

If the components were interval when you summed them, their sum is certainly interval.

[People may well disagree with me on this, but I can't see any basis for saying things like 4+1 = 3+2 = 5 -- along with all the similar statements that must be made -- unless you have assumed an interval scale. What basis would there be for thinking the summed-category-labels are equivalent outside the assumption that all gaps between adjacent values are equi-distant?]

Don't take this as an assertion that people should not add scale-items; in general I think it's a pretty reasonable thing to do. But in any case, once you do it, you shouldn't be uncomfortable about calling the sum interval-scale; you already went there.

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Solved – Ordinal Independent Variables and Dependent Variable Regression Analysis

Solved – Is the sum of a number of ordinal variables still ordinal

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