Multinomial and ordinal logistic regression using SPSS

This video provides an overview of options available through SPSS in carrying out multinomial and ordinal logistic regression. It covers assessment of global and local model fit as well.
Side note: The very last demonstration illustrates the use of binary logistic regression with a dependent variable with ordered categories (although technically presence of children - coded 0=no, 1=yes - is not ordinal). It is worth mentioning that multinomial logistic regression could have been used, and that with 2 categories on the DV all three procedures would yield equivalent results.
You can download a copy of the data here if you want to carry out the procedures as you follow along: drive.google.com/open?id=1Vkb...
Please be check out my newest video (August 2021) on ordinal logistic regression here: • Ordinal logistic regre...
For more instructional videos and other materials on various statistics topics, be sure to my webpages at the links below:
Introductory statistics:
sites.google.com/view/statist...
Multivariate statistics:
sites.google.com/view/statist...

[@mikecrowson2462]

DEAR VIEWERS: I HAVE CREATED TWO NEW VIDEOS (JULY, 2019) ON MULTINOMIAL (

[@mikecrowson2462]

Hi everyone, please be check out my newest video (August 2021) on ordinal logistic regression here:

[@drryangagnon]

Wonderful job of explaining the likelihood ratio tests; will steal (with attribution ;) ) for my next multivariate course!

[@sebsebsn7263]

Oh, how nice you have race in ordinal ranked measure. :) Just joking. Thx for your vid!!! Helped a lot.

[@gerezgihergebrewahd790]

nice video and can help those interested researchers in MLG model.

[@hale5845]

Dear Mike,

[@wasagoo]

This was helpful

[@oneilljaspers]

what if your p-value for pearson is 0,000 but for deviance it is 1,000? is this a problem? cannot find anything on the internet on this topic. thx

[@lingkan1984]

How can we run multinomial regression in spss using complex sample option?

[@sabb7276]

the data on last presentation is binary right not ordinal?

[@atikhomthienthong9310]

I run the multinomial logistic regression. This message appeared "Floating point overflow occurred while computing this statistic. Its value is therefore set to system missing." How should I deal with this?

[@susanwoodruff5299]

I'm wondering why you didn't address the warning at the top.

[@jjk2902]

Thank you, professor. After I run the ordinal logistic regression, SPSS told me that there are 310 (73.1%) cells (i.e., dependent variable levels by observed combinations of predictor variable values) with zero frequencies. Is this variable still qualify to be a dependent variable?

[@TheWolvesII]

thanks for the detailed video!

[@AnG-qx5bd]

Thank you for your video. I learned a lot. I ran the model with the categorical variables along with the continuous variables in the covariate box and got a different result when I ran the categorical variables in the factor box. Do you know why the difference in my models?

[@kossonouprunelle7576]

Thank you for your explanation. I have 2 nominal and 2 ordinal variables as independent variables (4), and 2 ordinal likert-scale dependent variable. In this case should i use ordinal logistic regression or nominal logistic regression? Your reply will help me a lot. Thank you in advance

[@kamalpreetrakhra8071]

Hello Mike, I have a question. Is there a precedence of taking a random sample of one category of the dependent variable so as to have similar proportions to the second for a three category dependent variable. My category proportions are 0.77, 0.20, and 0.027. Is there any other way to model the three category dependent variable for these proportions.

[@toughtexan9]

Thank you for the video, I was curious if you had any insight on estimated respons probability using an ordinal logistic regression.

[@ashabenjamin17]

what if i also have a categorical factor variable (binary) ? How do i interpret it then. Please let me know

[@salmasaad5431]

Thank you so much Dr this is very useful . I am wondering that how can I check if my model has outliers or not.