# Multinomial logistic regression

I would like to get help from an expert or anyone who knows about this. As a beginner in SPSS, I've googled the steps to do multinomial regression. I've decide to use multinomial because from what I googled and understood, binomial is for situations that only have 2 categories like yes or no, which you could code 0 for yes and 1 for no. This is what I understand if you choose to use binomial.

My situation is I have a set of questionnaires that use more than 2 answers. For example, level of income: 1. $2000, 2.$3000, 3. $4000, 4.$5000. That is the kind of question I have. So, obviously I choose to use multinomial regression.

My IV's are 1) social capital, 2) training, 3) credit/loan; and my DV is effectiveness which can be measured by 1) income, 2) saving, 3) repayment rate.

In my questionnaire, I only have two kind of question which "yes or no" and choose answer like above mention "1 or 2 or 3 or 4." So my questions are:

1. Did I choose the right regression to use, that is multinomial, or do I have to use binomial?
2. If it's multinomial, I need help with the steps like do I have to calculate all my DV questions and transform them into one, which will make new "item" in SPSS columns, then put it in the dependent column of multinomial regression? Because I have approximately 15 questions to measure my DV. Then in the factors columns what did I put? Every question of my IV's (like 30 questions separately) or just the total of the 30 questions? And what should I put on covariates?

http://sdrv.ms/JtOHu3

above is my questionaire sample link. income level, savings and repayment is to measure my dv ~ effectiveness of microfinance. other is my iv, that is credit/capital , training and social capital. feel free to improve my questionaire if you like.

• I tried to edit this to help with your English and make your questions clearer. However, I could not follow all of your sentences. You should make sure that in its current state, it still represents what you want. You may also want to see if you can clarify your questions further. Commented May 20, 2012 at 3:45
• im sorry if u cant understand my english. which part didnt u understand? really need help on this matters Commented May 20, 2012 at 5:20
• With logistic regression with more than two classes, sometimes multinomial logistic makes sense but sometimes ordinal logistic regression makes more sense. If there is an inherent ordering to the classes then ordinal logistic regression might make sense. You mentioned four levels of income...is that your target variable or was that just an example? Commented May 20, 2012 at 5:36
• yes, that four levels of income is one of my target variable as in dependent variable along with other question to measure the levels income.for example~ income before and after joining a microfinance institution. i might just upload my questionaire sample. Commented May 20, 2012 at 8:38
• i already upload my sample set of questionaire. feel free to look at it, and tell me how i should regress it. sdrv.ms/JtOHu3 Commented May 20, 2012 at 8:53

I recommend you delay the question of which sort of model to use in relating your DV to your IVs. First, you need to tackle scale development and see what sort of DV you arrive at. Maybe it'll end up something measured on an interval scale that is suitable for a linear regression.

As Peter has said, combining variables that could be components of your ultimate DV could be challenging since they're measured on different scales, but it can be done. You'll need to put in careful thought about how to combine them in a way that preserves the weights you believe each one deserves. I don't have an answer about that to offer you, but I can suggest a few authors you might read: Robert Devellis, Paul Spector, and Lewis Aiken. Or there are probably lots of websites that deal with scale development and with the reliability and validity of scales one forms based on several "ingredients." If you search for terms like these and add the qualifier "different scales" you'll probably find good sources.

• thanks for the advice. i'll read on the reference first. thnaks again for the advice. Commented May 21, 2012 at 11:45