• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar
The Analysis Factor

The Analysis Factor

Statistical Consulting, Resources, and Statistics Workshops for Researchers

  • Home
  • Our Programs
    • Membership
    • Online Workshops
    • Free Webinars
    • Consulting Services
  • About
    • Our Team
    • Our Core Values
    • Our Privacy Policy
    • Employment
    • Collaborate with Us
  • Statistical Resources
  • Contact
  • Blog
  • Login

SPSS Procedures for Logistic Regression

by Karen Grace-Martin 7 Comments

Need to run a logistic regression in SPSS? Turns out, SPSS has a number of procedures for running different types of logistic regression.

Some types of logistic regression can be run in more than one procedure.  For some unknown reason, some procedures produce output others don’t.  So it’s helpful to be able to use more than one.

Logistic Regression

SPSS Binary Logistic Regression MenuLogistic Regression can be used only for binary dependent variables.  Invoke it using the menu choices at right or through the LOGISTIC REGRESSION syntax command.

The dependent variable must have only two values.  If you specify a variable with more than two, you’ll get an error.

One big advantage of this procedure is it allows you to build successive models by entering a group of predictors at a time.

LOGISTIC REGRESSION VARIABLES BinaryDV
/METHOD=ENTER Factor Covariate1
/METHOD=ENTER Covariate2 Covariate3
/CONTRAST (Factor)=Indicator.

So for example, using this syntax, I’m actually simultaneously running two models, one with only two independent variables, (cleverly) named Factor and Covariate1.

The second model has four independent variables: Factor,  Covariate1, Covariate2, and Covariate3.

This is called “Hierarchical Regression” (not to be confused with Hierarchical Linear Models or HLM). It allows an easy comparison of how model fit and coefficients change as you add predictors.

The Logistic Regression command is invoked through the  menus under Regression–>Binary Logistic, as seen above.  (But don’t forget to Paste your syntax, so you have a record of what you did)!

PLUM

PLUM stands for Polytomous Universal Model.  That’s a mouthful, but just comes down to models for categorical outcomes with more than two ordered categories.

If your outcome categories are not ordered, don’t use PLUM.  You cannot tell it that categories have no order, and it will put them into a logical order.

PLUM OrdinalDV BY Factor WITH Covariate
/LINK=LOGIT
/PRINT=FIT PARAMETER SUMMARY.

SPSS Ordinal Logistic Regression MenuLogistic Regression models are one type of generalized linear model.  PLUM can actually fit 5 types of generalized linear model for ordinal outcomes, including probit and complimentary log-log models.

The LINK=logit command specifies the logistic model.

Logistic regression models in PLUM are proportional odds models.  That means a few things. First,  it models the odds for each ordered category compared to all lower-ordered categories. Second, the odds ratio for each predictor is the same, regardless of whether you’re comparing category 4 to 3 and below or category 3 to 2 and below.

PLUM has a nice option to check whether this assumption is reasonable.  In many data sets it isn’t, so always check it.

PLUM is invoked through the  menus under Regression–>Ordinal, as seen above.

NomReg

NomReg fits Multinomial Logistic Regression models for nominal outcomes.  That means outcomes with more than two unordered categories.

Unlike binary and ordered models, multinomial models cannot also be run in GenLin (see below).

NOMREG MultinomialDV (BASE=LAST ORDER=ASCENDING) BY Factor WITH Covariate
/MODEL=Factor Covariate
/INTERCEPT=INCLUDE
/PRINT=PARAMETER SUMMARY LRT CPS STEP MFI.

SPSS Multinomial Logistic Regression MenuMultinomial logistic regression models simultaneously run a series of binary models. Each one compares the odds of one outcome category to a reference category.

One nice feature in NomReg is you can specify any one of the outcome categories as the reference. Just use the BASE= option (or clicking the “Reference Category” button in the menus).

In NomReg you can specify predictors as categorical by placing them after the BY option (or in the Factor box in the menu dialog) or as continuous by placing them after the WITH option (or in the Covariates box in the menu dialog). This is identical to many other SPSS procedures, like PLUM, GLM, and Mixed.

This can save you a lot of time creating dummy variables for categorical predictors.

GenLin

As mentioned above, logistic regression models are one type of generalized linear model.

SPSS Generalized LInear Models MenuThis means that you can use the GenLin procedure to run binary and ordinal logistic regression models.  It doesn’t however, run unordered multinomial models.

GenLin can run many more models that just logistic.  So it requires that you specify the outcome distribution as either binomial or multinomial (for which it will run an ordinal model) and a logit link function.

GENLIN BinaryDV (REFERENCE=LAST) BY Factor (ORDER=ASCENDING) WITH Covariate
/MODEL Factor Covariate Factor*Covariate INTERCEPT=YES
DISTRIBUTION=BINOMIAL LINK=LOGIT
/PRINT CPS DESCRIPTIVES MODELINFO FIT SUMMARY SOLUTION.

If you could use Logistic or PLUM, why would you ever use GenLin?

GenLin has a few advantages in certain situations.  Here are three that I commonly use.

1. GenLin can run binary models in the Events/Trials format.  Logistic can’t.

2. GenLin prints EMMeans in both the original scale (ie. probabilities) and the transformed scale (log-odds).

This is a huge advantage if you have categorical predictors.  One or two categorical predictors are not hard to interpret using the regression coefficients. But if you have many, if they have many categories per predictor, or if you have interactions among them, the means are much easier to interpret.

3. GenLin can run repeated measures models using Generalized Estimating Equations.  Again, this is not just an advantage, but a necessity, if you have a repeated measures design.

Binary, Ordinal, and Multinomial Logistic Regression for Categorical Outcomes
Get beyond the frustration of learning odds ratios, logit link functions, and proportional odds assumptions on your own. See the incredible usefulness of logistic regression and categorical data analysis in this one-hour training.

Tagged With: genlin, logistic regression, nomreg, plum, SPSS

Related Posts

  • Opposite Results in Ordinal Logistic Regression, Part 2
  • Opposite Results in Ordinal Logistic Regression—Solving a Statistical Mystery
  • Dummy Code Software Defaults Mess With All of Us
  • Member Training: Explaining Logistic Regression Results to Non-Researchers

Reader Interactions

Comments

  1. weldu says

    June 7, 2019 at 5:30 am

    interested guide for my thesis.Thank you!

    Reply
  2. Lindsay Lucas says

    January 18, 2017 at 1:23 pm

    Hi Karen, do you have any tips for finding the predicted probabilities for a certain value of a continuous variable. It is so simple in R and SAS but I can’t find a way to do it in SPSS other than writing out the equation manually.

    Thanks,
    Lindsay

    Reply
    • Lindsay Lucas says

      January 18, 2017 at 1:28 pm

      I am currently using GENLIN with the repeated statement.

      Reply
  3. Martin says

    February 12, 2015 at 8:54 pm

    Just one question,

    is it possible to use the /METHOD=ENTER-command to create a sequential regression for the NOMREG model, too?

    Thank you for your help!

    Reply
    • Jabessa Hatahu says

      May 19, 2021 at 12:35 am

      How many independent interest for one dependent variable please?

      Reply
    • Karen Grace-Martin says

      May 26, 2021 at 10:09 am

      Hi Martin,

      No. That’s just in LOGISTIC REGRESSION.

      Reply
  4. Dr Albano Mbyuzi says

    May 17, 2014 at 6:58 am

    Very useful article, elaborated and user friendly esp beginners.

    Keep it up!

    Reply

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Please note that, due to the large number of comments submitted, any questions on problems related to a personal study/project will not be answered. We suggest joining Statistically Speaking, where you have access to a private forum and more resources 24/7.

Primary Sidebar

This Month’s Statistically Speaking Live Training

  • Member Training: Analyzing Pre-Post Data

Upcoming Free Webinars

Poisson and Negative Binomial Regression Models for Count Data

Upcoming Workshops

  • Analyzing Count Data: Poisson, Negative Binomial, and Other Essential Models (Jul 2022)
  • Introduction to Generalized Linear Mixed Models (Jul 2022)

Copyright © 2008–2022 The Analysis Factor, LLC. All rights reserved.
877-272-8096   Contact Us

The Analysis Factor uses cookies to ensure that we give you the best experience of our website. If you continue we assume that you consent to receive cookies on all websites from The Analysis Factor.
Continue Privacy Policy
Privacy & Cookies Policy

Privacy Overview

This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience.
Necessary
Always Enabled
Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.
Non-necessary
Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, ads, other embedded contents are termed as non-necessary cookies. It is mandatory to procure user consent prior to running these cookies on your website.
SAVE & ACCEPT