This tutorial shows how to fit a simple regression model (that is, a linear regression with a single independent variable) using SPSS. The details of the underlying calculations can be found in our simple regression tutorial. The data used in this post come from the More Tweets, More Votes: Social Media as a Quantitative Indicator of Political Behavior study from DiGrazia J, McKelvey K, Bollen J, Rojas F (2013), which investigated the relationship between social media mentions of candidates in the 2010 and 2012 US House elections with actual vote results.

This tutorial shows how to fit a simple regression model (that is, a linear regression with a single independent variable) using R. The details of the underlying calculations can be found in our simple regression tutorial. The data used in this post come from the More Tweets, More Votes: Social Media as a Quantitative Indicator of Political Behavior study from DiGrazia J, McKelvey K, Bollen J, Rojas F (2013), which investigated the relationship between social media mentions of candidates in the 2010 and 2012 US House elections with actual vote results.

This tutorial shows how to fit a simple regression model (that is, a linear regression with a single independent variable) using Stata. The details of the underlying calculations can be found in our simple regression tutorial. The data used in this post come from the More Tweets, More Votes: Social Media as a Quantitative Indicator of Political Behavior study from DiGrazia J, McKelvey K, Bollen J, Rojas F (2013), which investigated the relationship between social media mentions of candidates in the 2010 and 2012 US House elections with actual vote results.

In our previous tutorials, we discussed simple regression and multiple regression with continuous variables, but what happens when our independent variable is nominal rather than interval?
The data used in this tutorial are again from the More Tweets, More Votes: Social Media as a Quantitative Indicator of Political Behavior study from DiGrazia J, McKelvey K, Bollen J, Rojas F (2013), which investigated the relationship between social media mentions of candidates in the 2010 and 2012 US House elections with actual vote results.

Multiple Regression A prior tutorial described simple regression as a mapping of a single predictor to an outcome variable. This tutorial covers the case when there is more than one independent variable, also known as multiple regression. Although simple regression is a useful tool for extracting information about bivariate relationships that goes beyond what we get from a correlation or t-test, the real power of regression comes from its ability to incorporate multiple independent variables.

Regression is a basic method for predicting values of some dependent variable \((Y)\) as a function of one or more independent variables \((X_i)\). Simple regression describes the case when there is only one predictor, whereas multiple regression has multiple predictors. This tutorial will focus solely on simple regression.
The data used in this tutorial are from the article More Tweets, More Votes: Social Media as a Quantitative Indicator of Political Behavior from DiGrazia, McKelvey, Bollen, and Rojas (2013).

This post outlines the steps for performing a logistic regression in R. The data come from the 2016 American National Election Survey. Code for preparing the data can be found on our github page, and the cleaned data can be downloaded here.
The steps that will be covered are the following:
Check variable codings and distributions Graphically review bivariate associations Fit the logit model Interpret results in terms of odds ratios Interpret results in terms of predicted probabilities The variables we use will be:

This post outlines the steps for performing a logistic regression in SAS. The data come from the 2016 American National Election Survey. Code for preparing the data can be found on our github page, and the cleaned data can be downloaded here.
The steps that will be covered are the following:
Check variable codings and distributions Graphically review bivariate associations Fit the logit model Interpret results in terms of odds ratios Interpret results in terms of predicted probabilities The variables we use will be:

This post outlines the steps for performing a logistic regression in SPSS. The data come from the 2016 American National Election Survey. Code for preparing the data can be found on our github page, and the cleaned data can be downloaded here.
The steps that will be covered are the following:
Check variable codings and distributions Graphically review bivariate associations Fit the logit model in SPSS Interpret results in terms of odds ratios Interpret results in terms of predicted probabilities The variables used will be:

This post outlines the steps for performing a logistic regression in Stata. The data come from the 2016 American National Election Survey. Code for preparing the data can be found on our github page, and the cleaned data can be downloaded here.
The steps that will be covered are the following:
Check variable codings and distributions Graphically review bivariate associations Fit the logit model in Stata Interpret results in terms of odds ratios Interpret results in terms of predicted probabilities The variables we use will be: