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Predicting Elections with Regressions Mario Guerrero Political Science 104 Thursday, November 13, 2008 Learning Regression •What is a Regression? •Effect-Descriptive or Causal Inference Coefficient •Approval Rating versus Vote Share Example •Interpreting a Scatterplot •How a Regression works on SPSS and Interpretation •Prediction Models •How to use regression to predict dependent variable •Predicting Vote Share •The 2008 Presidential Election •Did we predict Obama’s victory in June 2008? •Research: Money and Politics •Asking a new question based on money in elections •Classic Case of Operationalizing •Reworking the variables from concepts •My Final Findings •Was I able to predict money in elections? What is a regression? Think back to last week’s lectures: We learned about two different types of coefficients: •Those which are “correlation” that tell you how well your relationship is being measured. (PRE, Q, Gamma) •Those which are “effect-descriptive” that tell you how much you independent variable affects your dependent variable. Regression yields an effect-descriptive coefficient. What is a 2008 Mission: regression? Elections Operationalize Prediction My Final Models Research Results How does a regression work? In regression, we are estimating the relationship between two interval level variables. For example, we might be interested in seeing the relationship between approval ratings and vote share. So far, we’ve learned a couple of ways to estimate the relationship between two variables: Crosstabs, Gamma, t-tests, Scatterplots, Boxplots Only scatterplots can really tell us how two interval level variables interact with each other. What is a 2008 Mission: regression? Elections Operationalize Prediction My Final Models Research Results First Step – Some Data Election Year (President) Approval Rating Election Year (President) Vote Share 1972 (Nixon v. McGovern) 57% 1972 (Nixon v. McGovern) 60% 1976 (Carter v. Ford) 45% 1976 (Carter v. Ford) 48% 1980 (Reagan v. Carter) 32% 1980 (Reagan v. Carter) 41% 1984 (Reagan v. Mondale) 55% 1984 (Reagan v. Mondale) 59% 1988 (Bush v. Dukakis) 51% 1988 (Bush v. Dukakis) 53% 1992 (Clinton v. Bush) 37% 1992 (Clinton v. Bush) 37% 1996 (Clinton v. Dole) 58% 1996 (Clinton v. Dole) 49% 2000 (Bush v. Gore) 55% 2000 (Bush v. Gore) 48% 2004 (Bush v. Kerry) 49% 2004 (Bush v. Kerry) 50% 2008 (Obama v. McCain) 30% 2008 (Obama v. McCain) 46% What is a 2008 Mission: regression? Elections Operationalize Prediction My Final Models Research Results Second Step – Graphing the Data •Scatterplots plots the interval variables so we can visually interpret how low/high values on one variable affects values on another variable. •Regressions simply estimate the relationship between these two variables by drawing a line through the data and estimating its slope and intercept. What is a 2008 Mission: regression? Elections Operationalize Prediction My Final Models Research Results Third Step – Fitting a Line •SPSS is able to plot a line through the data in the scatterplot that best represents the relationship between approval ratings and vote share. •This is regression. However, the regression output simply represents this by using numbers instead of a graphical representation. What is a 2008 Mission: regression? Elections Operationalize Prediction My Final Models Research Results Doing a Scatterplot in SPSS What is a 2008 Mission: regression? Elections Operationalize Prediction My Final Models Research Results Interpreting the Output in SPSS Dependent Variable (Vote Share) y = mx + b Independent Variable (Approval Ratings) y = .500x + 25.667 Don’t forget significance! What is a 2008 Mission: regression? Elections Operationalize Prediction My Final Models Research Results Interpretation of a Regression y = .500x + 25.667 How is this interpreted? •Vote Share (Dependent Variable) is represented by Y. Approval ratings (Independent Variable) is represented by X. •If our independent variable, approval ratings, is zero, then the value of Y, vote share, is 25.667. Incumbent candidates begin with a 26-point vote share, regardless of approval rating. •On average, for every unit increase in approval ratings, we see a .500 increase in vote share. (.500 is our effect-descriptive coefficient!!) What is a 2008 Mission: regression? Elections Operationalize Prediction My Final Models Research Results Controlling with Regression While we can’t add additional variables to a scatterplot, the regression is able to handle more than just two variables. Adding variables allows us to account for several different explanations for changes in our dependent variable. This is how you run a regression, with or without additional control variables: What is a 2008 Mission: regression? Elections Operationalize Prediction My Final Models Research Results Research: Prediction Models Research in Political Science has utilized the regression model to its advantage. While regression yields an effect-descriptive coefficient, Political Scientists have used it in attempt to predict who will take the White House in each presidential election. How does this work? Each regression yields coefficients for each variable you’re working with. Those coefficients give you the equation of a predicted line based on the data. For example, we were left with the equation in the previous example: y = .500x + 25.667 What is a 2008 Mission: regression? Elections Operationalize Prediction My Final Models Research Results Research: Prediction Models y = .500x + 25.667 In this limited example, we could have potentially predicted the outcome of the 2008 Presidential Election by using this equation. 2008 Presidential Election: (Obama vs. McCain) Incumbent’s (Bush) Approval Rating in June 2008: 30% Incumbent Party’s Predicted Vote Share Total: y = .500(30)+25.667 = 40.667 Incumbent Party’s Actual Vote Share Total: 46.1 The model underpredicted McCain’s performance by around 6% 2004 Presidential Election (Bush vs. Kerry) Incumbent’s (Bush) Approval Rating in June 2004: 49% Incumbent Party’s Predicted Vote Share Total: y = .500(49)+25.667 = 50.167 Incumbent Party’s Actual Vote Share Total: 50.0 The model almost perfectly predicted Bush’s performance. What is a 2008 Mission: regression? Elections Operationalize Prediction My Final Models Research Results Research: Prediction Models In 1992, Lewis-Beck and Rice come up with a model that predicted the Electoral Vote Share by taking into account four different variables. From 1948-1988, Lewis-Beck and Rice were pretty adept at predicting vote share. Y = 7.76EC + 0.86PP + 0.52PS + 19.66CA + 6.83 What is a 2008 Mission: regression? Elections Operationalize Prediction My Final Models Research Results 2008 Elections Y = 7.76EC + 0.86PP + 0.52PS + 19.66CA + 6.83 Economic Conditions (EC): GDP changes 1% from 2007 Q4 to 2008 Q2. Presidential Popularity (PP): Bush’s popularity rating is at 30% in June 2008. Party Strength (PS): The Democrats have 36 more members in Congress at the midterm elections. Candidate Appeal (CA): John McCain was able to win 61% of delegates in primary, so the value becomes 1 for candidate appeal. (Arbitrary cut-off of 60%) Y = 7.76(1) + 0.86(30) + 0.52(36) + 19.66(1) + 6.83 What is a 2008 Mission: regression? Elections Operationalize Prediction My Final Models Research Results 2008 Elections Y = 7.76(1) + 0.86(30) + 0.52(36) + 19.66(1) + 6.83 Did the model correctly predict that John McCain would lose the election and Barack Obama would win the election in June 2008? YES! 7.76(1) + 0.86(30) + 0.52(36) + 19.66(1) + 6.83 = 41.33 In June 2008, the forecasting models predicted that John McCain would lose the election with only 41.33% of the vote. McCain lost with 46% of the vote. It was off by 5%, but it correctly predicted that Barack Obama would win the election. What is a 2008 Mission: regression? Elections Operationalize Prediction My Final Models Research Results My Research: Money in Politics Research Question: Money is connected to elections in some way that researchers have not yet been able to quantify. Are money and elections connected? If we can predict election vote share totals, can we predict how much money campaigns fundraise? Hypothesis: The same variables that affect vote share affect how much money the incumbent party will fundraise. Economic considerations, presidential popularity, party strength, and candidate appeal cause people to donate more money to their political parties. Concepts: economic considerations, presidential popularity, party strength, candidate appeal, political contributions What is a 2008 Mission: regression? Elections Operationalize Prediction My Final Models Research Results A Few Considerations… •My research ended up being much more influenced by congressional politics than presidential politics. While I had learned about forecasting models for predicting presidential elections, I was much more interested in congressional elections. Thus, I immediately had to change my focus. •While I gathered my inspiration from Lewis-Beck and Rice’s research, I had essentially anticipated changing each variable in the equation in order to get the best prediction model. This is a form of operationalization. •My dependent variable would undoubtedly change from electoral vote share to percentage of the incumbent party’s fundraising total. •Most of the independent variables were subject to scrutiny and criticism for their inclusion in the model. What is a 2008 Mission: regression? Elections Operationalize Prediction My Final Models Research Results Operationalizing Variables Independent Concepts: economic considerations presidential popularity party strength candidate appeal Dependent Concept: political contributions Independent Variables: Real GDP per capita Real disposable income Gallup’s popularity rating in June How many seats the incumbent party has against the non-incumbent party in Congress If the candidate won 60% of the vote in the primary. Dependent Concept: Percentage incumbent has fundraised against non-incumbent What is a 2008 Mission: regression? Elections Operationalize Prediction My Final Models Research Results Operationalizing Variables However, Lewis-Beck and Rice claim to have adopted a model to predict House seat change, which would be much more appropriate for our model’s purposes: Independent Concepts: economic considerations presidential popularity party strength party appeal (not candidate) Dependent Concept: political contributions Independent Variables: Real GDP per capita Real disposable income Gallup’s popularity rating in June Seat exposure calculation Time the incumbent party has held in the White House Dependent Concept: Percentage incumbent has fundraised against non-incumbent What is a 2008 Mission: regression? Elections Operationalize Prediction My Final Models Research Results The Independent Variables economic considerations •Real GDP per capita •Real disposable income •Considerations of GDP in both a midterm and presidential year •Considerations of income in both a midterm and presidential year presidential popularity •Gallup’s presidential popularity rating in June •Gallup’s congressional popularity rating in June party strength party appeal •Seat exposure •Time the calculation incumbent •Difference in party has held seats between in the White parties House •Number of •Duration of incumbents majority party’s hold in Congress I also attempted to add two control variables: interest groups effects and media effects. What is a 2008 Mission: regression? Elections Operationalize Prediction My Final Models Research Results Final Results -- Equation In the beginning, I began with: But through operationalizing, I ended up with: What is a 2008 Mission: regression? Elections Operationalize Prediction My Final Models Research Results Final Results -- Regression These circled numbers are my coefficients for each of the variables. What is a 2008 Mission: regression? Elections Operationalize Prediction My Final Models Research Results Final Results -- Regression This is the intercept for my regression where all my independent variables will equal zero. What is a 2008 Mission: regression? Elections Operationalize Prediction My Final Models Research Results Final Results -- Regression The stars next to each of the coefficients and intercept indicate that each one of my coefficients turned out to be significant. What is a 2008 Mission: regression? Elections Operationalize Prediction My Final Models Research Results Final Results -- Regression The final prediction equation that we come up with is: Y = -.0600(EC1) + .0817(EC2) + .0227(CP) + -.0072(NI) + -.1707(DM) + 3.089 What is a 2008 Mission: regression? Elections Operationalize Prediction My Final Models Research Results Final Results -Predictions Y = -.0600(EC1) + .0817(EC2) + .0227(CP) + -.0072(NI) + -.1707(DM) + 3.089 Actual Probability: The actual percentage that the incumbent party fundraised. Predicted Probability: The predicted percentage that my model predicted. Error: The difference between the two. For 2008, the model predicts that Democrats would fundraise three times as much as the Republicans (~25%). What is a 2008 Mission: regression? Elections Operationalize Prediction My Final Models Research Results Learning Regression •It all began with a regression. •I built on previous research out there (consistency). •My research started with a question and a hypothesis. •To answer my question, prediction and verification were absolutely necessary. My research is a great example of operationalizing. •The analysis and application of my findings is relevant to current questions about politics. •The topic was intrinsically interesting and most of all, it ended up being fun.