1 Finding Supporters Political Predictive Analytics Using Logistic Regression Multivariate Solutions
2 What is Logistic Regression? In a political application, logistic regression can describe the outcome of whether a person will vote or not vote ; or Vote for my candidate, Not Vote for my candidate. Logistic regression should be used in situations when a campaign would like to ascertain which voters are: Likely Voters Inclined to vote for their candidate Undecided Donor possibility Logistic regression in a nutshell: Logistic regression is used for prediction of the probability of occurrence of an event by fitting data to a logistic curve. Logistic regression makes use of several predictor variables that may be either numerical or categorical.
3 Example: Finding the Liz Anderson Supporter For example, in a political race, logistic regression could be used to analyze the factors that determine whether an individual should be targeted with strategic communications; in the same sense as a swing state in president elections. In our example, our candidate, Liz Anderson, is running for Congress. Let us define a variable Outcome Vote for Liz Anderson Outcome = 1 If 'The Individual Is Likely to vote for Liz = 0 If 'The Individual Is Not Likely To Vote for Liz. The outcome takes only two possible values.
4 Variables in the Model These are the variables in the Anderson for Congress model Gender-Male=1,Female=2 Married Own home Urban=1/Rural=0 Working women in Household Completed college Credit card debt
5 Regression Output Vote for 'Liz' Logistic Regression Regression Output Regression Beta Sig. Odds Ratio Gender 1=Male 2=Female Marital Status 1=Married/0=Not Married College Graduate 1=Yes, 0=No Working Woman in House 1=Yes, 0=No Urban 1=Urban, 0=Not urban Housing 1=Own, 0=Rent Credit Card Debt 1=Yes, 0=No Shading indicated statistical significance This slide is descriptive, and shows which of the variables are most influential in determining whether a voter is a Liz for Congress supporter. The campaign can now determine, by examining this table, that educated married, working woman who live in urban areas are a key demographic to target in the days leading up to the election. These should be targets for both Strategic Communications and Get Out the Vote field operations.
6 Odds-Ratio When a respondent s choices are set within the regression model, an odds-ratio for each respondent is created using the formula of 1/(1+e -z ). Z is the outcome of the regression equation once all the questions are input. A simulator can be used to classify individuals based on demographic data or a survey screen. Two examples follow:
7 Example One Urban College-Educated Working Woman Vote for 'Liz' Logistic Regression Regression Output Answer Regression Beta Sum (b*c) Gender 1=Male 2=Female Marital Status 1=Married/0=Not Married College Graduate 1=Yes, 0=No Working Woman in House 1=Yes, 0=No Urban 1=Urban, 0=Not urban Housing 1=Own, 0=Rent Credit Card Debt 1=Yes, 0=No Sum Odds Ratio (1/(1+e -z ) 0.97 (Chances to 'Vote for Liz') 97% Using the logistic output, the chances of a college educated, urban, working, married woman who owns her own home and has no credit card debt to be a supporter shown above. The logistic regression formula (1/(1=e -z ) gives this woman an odd-ratio (in purple) of.97, or 97% chance that she will vote for Liz.
8 Example Two Urban College-Educated Working Man Vote for 'Liz' Logistic Regression Regression Output Answer Regression Beta Sum (b*c) Gender 1=Male 2=Female Marital Status 1=Married/0=Not Married College Graduate 1=Yes, 0=No Working Woman in House 1=Yes, 0=No Urban 1=Urban, 0=Not urban Housing 1=Own, 0=Rent Credit Card Debt 1=Yes, 0=No Sum Odds Ratio (1/(1+e -z ) 0.68 (Chances to 'Vote for Liz') 68% Using the logistic output, the chances of a college educated, urban, working, married MAN who owns his own home and has no credit card debt to be a Liz supporter is shown above. The logistic regression formula (1/(1=e -z ) gives this man an odd-ratio (in purple) of.68, or 68% chance that he will vote for Liz (down from 97% for a women with the same demographic characteristics).
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