• 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

  • our programs
    • Membership
    • Online Workshops
    • Free Webinars
    • Consulting Services
  • statistical resources
  • blog
  • about
    • Our Team
    • Our Core Values
    • Our Privacy Policy
    • Employment
    • Collaborate with Us
  • contact
  • login

dummy variable

Your Questions Answered from the Interpreting Regression Coefficients Webinar

by Karen Grace-Martin  Leave a Comment

Last week I had the pleasure of teaching a webinar on Interpreting Regression Coefficients. We walked through the output of a somewhat tricky regression model—it included two dummy-coded categorical variables, a covariate, and a few interactions.

As always seems to happen, our audience asked an amazing number of great questions. (Seriously, I’ve had multiple guest instructors compliment me on our audience and their thoughtful questions.)

We had so many that although I spent about 40 minutes answering [Read more…] about Your Questions Answered from the Interpreting Regression Coefficients Webinar

Tagged With: dummy coding, dummy variable, effect size, eta-square, Interpreting Interactions, interpreting regression coefficients, Reference Group, spotlight analysis, statistical significance

Related Posts

  • Dummy Coding in SPSS GLM–More on Fixed Factors, Covariates, and Reference Groups
  • Using Marginal Means to Explain an Interaction to a Non-Statistical Audience
  • Interpreting Interactions in Linear Regression: When SPSS and Stata Disagree, Which is Right?
  • About Dummy Variables in SPSS Analysis

Multiple Imputation for Missing Data: Indicator Variables versus Categorical Variables

by Jeff Meyer  Leave a Comment

A data set can contain indicator (dummy) variables, categorical variables and/or both. Initially, it all depends upon how the data is coded as to which variable type it is.

For example, a categorical variable like marital status could be coded in the data set as a single variable with 5 values: [Read more…] about Multiple Imputation for Missing Data: Indicator Variables versus Categorical Variables

Tagged With: categorical variable, dummy variable, indicator, Missing Data, Multiple Imputation

Related Posts

  • Multiple Imputation in a Nutshell
  • Missing Data Diagnosis in Stata: Investigating Missing Data in Regression Models
  • Two Recommended Solutions for Missing Data: Multiple Imputation and Maximum Likelihood
  • Quiz Yourself about Missing Data

Missing Data Diagnosis in Stata: Investigating Missing Data in Regression Models

by Jeff Meyer  Leave a Comment

by Jeff Meyer

In the last post, we examined how to use the same sample when running a set of regression models with different predictors.

Adding a predictor with missing data causes cases that had been included in previous models to be dropped from the new model.

Using different samples in different models can lead to very different conclusions when interpreting results.

Let’s look at how to investigate the effect of the missing data on the regression models in Stata.

The coefficient for the variable “frequent religious attendance” was negative 58 in model 3 and then rose to a positive 6 in model 4 when income was included. Results [Read more…] about Missing Data Diagnosis in Stata: Investigating Missing Data in Regression Models

Tagged With: dummy variable, Linear Regression Model, Missing Data, regression coefficients, Stata

Related Posts

  • Linear Regression in Stata: Missing Data and the Stories it Might Tell
  • Multiple Imputation for Missing Data: Indicator Variables versus Categorical Variables
  • EM Imputation and Missing Data: Is Mean Imputation Really so Terrible?
  • Confusing Statistical Term #13: Missing at Random and Missing Completely at Random

About Dummy Variables in SPSS Analysis

by Karen Grace-Martin  1 Comment

Whenever I get email questions whose answers I think would benefit others, I like to answer them here.  I leave out the asker’s name for privacy, but this is a great question about dummy coding:

First of all, thanks for all those helpful information you provided! Thanks sincerely for all your efforts!

Actually I am here to ask a technical question. See, I have 6 locations (let’s say A, B, C, D, E, and F), and I want to see the location effect on the outcome using OLS models.

I know that if I included 5 dummy location variables (6 locations in total, with A as the reference group) in 1 block of the regression analysis, the result would be based on the comparison with the reference location.

Then what if I put 6 dummies (for example, the 1st dummy would be “1” for A location, and “0” for otherwise) in 1 block? Will it be a bug? If not, how to interpret the result?

Thanks a lot!

Great question!

If you put in a 6th dummy code for Location A, your reference group, the model will actually blow up. (Yes, that’s a technical term).

This is one of those cases of pure multicollinearity, and the model can’t be estimated uniquely.

It’s the same situation you learned back in Algebra where you have two equations, one unknown.  The problem isn’t that it can’t be solved–the problem is there are an infinite number of equally good solutions.

If an observation falls in Location A, the reference group, we’ve already gotten that information from the other 5 dummy variables.  That observation would have a 0 on all of them.  So we already know it’s location is A.  We don’t need another dummy variable to tell the model that.  It’s redundant information.  And so perfectly redundant that the model will choke.

Dummy coding is one of the topics I get the most questions about.  It can get especially tricky to interpret when the dummy variables are also used in interactions, so I’ve created some resources that really dig in deeply.


Bookmark and Share

Tagged With: dummy coding, dummy variable, interpreting regression coefficients

Related Posts

  • Your Questions Answered from the Interpreting Regression Coefficients Webinar
  • Interpreting (Even Tricky) Regression Coefficients – A Quiz
  • Dummy Coding in SPSS GLM–More on Fixed Factors, Covariates, and Reference Groups
  • Making Dummy Codes Easy to Keep Track of

Interpreting (Even Tricky) Regression Coefficients – A Quiz

by Karen Grace-Martin  1 Comment

Here’s a little quiz:

True or False?

1. When you add an interaction to a regression model, you can still evaluate the main effects of the terms that make up the interaction, just like in ANOVA.

2. The intercept is usually meaningless in a regression model. [Read more…] about Interpreting (Even Tricky) Regression Coefficients – A Quiz

Tagged With: analysis of covariance, dummy variable, Interpreting intercept, interpreting regression coefficients

Related Posts

  • Interpreting the Intercept in a Regression Model
  • Your Questions Answered from the Interpreting Regression Coefficients Webinar
  • The General Linear Model, Analysis of Covariance, and How ANOVA and Linear Regression Really are the Same Model Wearing Different Clothes
  • About Dummy Variables in SPSS Analysis

Logistic Regression Models for Multinomial and Ordinal Variables

by Karen Grace-Martin  59 Comments

Multinomial Logistic Regression

The multinomial (a.k.a. polytomous) logistic regression model is a simple extension of the binomial logistic regression model.  They are used when the dependent variable has more than two nominal (unordered) categories.

Dummy coding of independent variables is quite common.  In multinomial logistic regression the dependent variable is dummy coded into multiple 1/0 variables.  There is a variable for all categories but one, so if there are M categories, there will be M-1 dummy variables.  All but one category has its own dummy variable.  Each category’s dummy variable has a value of 1 for its category and a 0 for all others.  One category, the reference category, doesn’t need its own dummy variable as it is uniquely identified by all the other variables being 0.

The multinomial logistic regression then estimates a separate binary logistic regression model for each of those dummy variables.  The result is [Read more…] about Logistic Regression Models for Multinomial and Ordinal Variables

Tagged With: Binary Logistic Regression, dummy variable, Multinomial Logistic Regression, Ordinal Logistic Regression, Proportional Odds Model

Related Posts

  • How to Decide Between Multinomial and Ordinal Logistic Regression Models
  • Opposite Results in Ordinal Logistic Regression, Part 2
  • Opposite Results in Ordinal Logistic Regression—Solving a Statistical Mystery
  • Confusing Statistical Terms #1: The Many Names of Independent Variables

  • Go to page 1
  • Go to page 2
  • Go to Next Page »

Primary Sidebar

This Month’s Statistically Speaking Live Training

  • Member Training: Moderated Mediation, Not Mediated Moderation

Upcoming Workshops

    No Events

Upcoming Free Webinars

TBA

Quick links

Our Programs Statistical Resources Blog/News About Contact Log in

Contact

Upcoming

Free Webinars Membership Trainings Workshops

Privacy Policy

Search

Copyright © 2008–2023 The Analysis Factor, LLC.
All rights reserved.

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