In this section we extend the concepts from Logistic Regression where we describe how to build and use **binary logistic regression** models to cases where the dependent variable can have more than two outcomes. Using such models the value of the categorical dependent variable can be predicted from the values of the independent variables.

We first address the categorical case where there is no order to these outcomes (**multinomial logistic regression**). We then turn our attention the situation where there is order (**ordinal logistic regression**).

Topics:

- Basic concepts of multinomial logistic regression
- Finding multinomial logistic regression coefficients
- Real Statistics capabilities
- Ordinal logistic regression

Dear Charles,

IF the Model fitting is not significant, should I proceed?

If yes, what does it mean for the model fitting to be not significant while the parameter estimates

is significant?

￼Model Fitting Information

￼￼￼￼￼￼￼￼￼￼￼￼￼￼Model Fitting Criteria

-2 Log Likelihood

95.673 90.756

Likelihood Ratio Tests

￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼Model

Intercept Only Final

Chi-Square df

4.917

Sig.

￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼2

.086

￼￼￼￼￼￼￼￼￼￼￼IF the Model fitting is not significant, should I proceed?

If yes, what does it mean for the model fitting to be not significant while the parameter estimates is significant?

If the model is not significant, then there is no point in proceeding. You might as well use the L0 baseline model.

Charles

what is the L0 baseline model?

Fatimah,

It is the model without any independent variables.

Charles

Dear Sir,

Please help me, I’m a newbie about this problem.

Well, I’m now completing a research study about the relationship between narcissism (IV) and cyberbullying (DV) to instagram user. My independent variable has low-mid-high (interval data) and my dependant variable has a categorical data which consist of cyberbullying perpretator-cyberbullying victim-and the unidentified one.

Yesterday, i tried a multinomial logistic regression analysis in SPSS, and it gave me a warning:

“There are 1 (11,1%) cells (i.e., dependent variable levels by subpopulations) with zero frequencies.

Unexpected singularities in the Hessian matrix are encountered. This indicates that either some predictor variables should be excluded or some categories should be merged.

The NOMREG procedure continues despite the above warning(s). Subsequent results shown are based on the last iteration. Validity of the model fit is uncertain.”

What’s the warning means ? I don’t understand

And is a multinomial logistic regression analysis that i’ve choosen right to be analysed in my research ?

Sam

Thankyou, Sir

Sam,

From your description, multinomial logistic regression analysis seems to be a good choice, except for the warning. You should pay attention to warning “There are 1 (11,1%) cells (i.e., dependent variable levels by subpopulations) with zero frequencies.” You can ignore the rest of the warning.

I don’t use SPSS and so I can’t comment further about the warning message, but I suspect that your sample is very small with not enough data to find a fit for the logistic regression model.

Charles

Is it possible to use your resource pack for conditional logisitic regression? Think of analyzing which horse will win a given horse race relative to the other horses….Thanks!

Dennis,

I’ll need to look into this and possibly add it to a future release.

Charles

I would love that feature too!

Sir

Please help me with this notification i am very new to real statistic package while i am trying to perform multinomial logistic regression its saying “last column of input range must contain all the values 0,1,2,…, and only these values where r=max value in the last column of input range (r must be <25). How can i solve this problem ?

Ashik,

If you send me an Excel file with your data, I will explain what you need to do. You can find my email address at Contact Us.

Charles

Hi Dear Dr. Zaiontz,

Im am completing a research study looking to see if there is an association between rates of hypotension (yes/no) during surgery (primary outcome) and use of a certain blood pressure medication (given /held prior to surgery). I have multiple regressors / confounding variables that I am trying to account for. Some are binary in nature (0,1) and some are continuous (ex. blood pressure readings). Someone had suggested I split my regression analysis: 1) do a multi nominal analysis for comparing my independent variable and nominal data, 2) do a multivariate linear regression for comparing independant variable with continuous regressors. What is your opinion on the above advise? What type of test do you feel would be most appropriate?

Thanks,

Sarah

Sarah,

These approaches could be useful, but I would need to have a more complete picture of the situation before I could definitively answer your question.

Charles

My Independent variables are gender and academic achievements in term of CGPA. While my DV is Emotional intelligence EI. What type of tests i will do to prove that gender has relationship with EI, and Academic achievements predict EI.

One option is to use multiple linear regression.

Charles

Dear Dr. Zaiontz,

I am planning on using Conjoint Analysis to measure preference for new products. As you know, it uses a multinomial logit model. However, I have found special softwares to conduct such analysis but they are very expensive. Do you know if Conjoint Analysis could be performed using Excel, or are there other ways of doing it? (I have been told that I could find free codes to use it on R, but I got lost when I saw those). Any help is greatly appreciated.

Sincerely,

Hamad

Dear Hamed,

The Real Statistics Resource Pack doesn’t yet support conjoint analysis, but I have found the following website which seems to have an example Excel worksheet which may be useful to you.

http://www.dobney.com/Conjoint/conjoint_simple.htm

Charles

Hi Prof. Zaiontz

I appreciate if you kindly help me in doing multinomial logistic regression between my categorical phenotypic data (as dependent variables) and genotypic data (both binary and allelic states as independent variables).

FYI, I am analysing my data in a panel of 143 barley genotypes for association mapping in barley. I have used GLM and MLM models for my quantitative and ordinal phenotypic data in TASSEL software(http://www.maizegenetics.net/index.php?option=com_content&task=view&id=89&Itemid=119).

regards,

Hossein

Hossein,

What sort of help are you looking for?

Charles

Hi

Please let me know how I can send you a sample of my data to you. Once you kindly look at it, I can say more details on what I mean and looking for.

Hossein

Hi Hossein,

See Contact Us.

Charles

Hi Charles

I sent an example to you. Did you have time to have a look at?

Hossein

Hossein,

I have received your email, but have not had time to look at it yet.

Charles

Dear sir,

Can u tell me, when we have Categorical variable for both dependent & Independent variables, How we will do the regression analysis

Iresha,

When all the variables are categorical you can use log-linear regression. See the webpage http://www.real-statistics.com/log-linear-regression/ for more details.

Charles