Ordinal or binomial regression? 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.
 A: 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.
A: You should use binomial logistic regression and not ordinal regression, though there are some concerns that you should be aware of when using ordinal predictors in a logistic regression.
Notably, if you're representing your ordinal variables numerically but the relationship between the rising levels of ordinality of an independent variable and the response of the dependent variable are not linear, the model can falsely estimate the linear response.
For example, given an independent variable with ordinal levels (a, b, c) and a response variable, consider two possible variable responses:
[A]

[B]

In [A], the ordinal predictors would very poorly model the binomial response, whereas in B the linear response would be just fine.
In [A], you would definitely want to use the ordinal predictor as a categorical variable instead of as a numeric representation, whereas in [B] the numeric representation of ordinal levels would be just fine.
