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Latent Growth Models and Their Application to Political Behaviour Research

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arvin
14 diapositives · 272 Views · Jul 17, 2014

This research examines the application of latent growth models (LGM) to analyze political behavior during the 2008 American election. Focusing on the interplay between economic concerns and voter support for Barack Obama, it investigates how individual negativity translates into anti-incumbent votes. By utilizing data from a six-wave survey of over 7,000 registered voters, the study addresses critical research questions regarding economic voting dynamics and party identification. Emphasizing the advantages of LGM over traditional methods, this work contributes to the understanding of political behavior through advanced statistical techniques.

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  1. Latent Growth Models and Their Application to Political Behaviour Research Thomas J. Scotto University of Essex Harold D. Clarke UT-Dallas and University of Essex

  2. 2008 American Election Was Dynamic • In December 2007 and For Primary Season, Election Appeared to be Foreign Policy Based, but this Changes

  3. Why should we care? • Economic Voting Literature Long and Solid • Bad Economic News Usually = Bad News for Incumbents • Shortcoming: Aggregate Time Series Data or Cross-Sectional Data • Assumed that an Individual’s Increasing Negativity Leads to Increasing Anti-Incumbent Vote • We can now test that assumption using 2008 America as Case Study • Not trivial: Note Disagreements between Macro-Micro literature on Party Identification

  4. How to examine these things • Cooperative Campaign Analysis Project • 6 Wave Internet Based Survey of Registered Voters with over 7,000 Respondents • We use December 2007, January 2008, March 2008, September 2008, and October 2008 pre-election waves • Key Research Questions: • Did Rising Concerns with the Economy Lead to Rising Support (Favour) for Obama • How did the impact of the economy compete with other issues and attributes?

  5. Methods and Models? • Latent Curve Modelling • Prominent in Education and Sociology, Rare in Political Science • Structural Equation Modelling Analogue to Random Intercept and Slope Modelling • Why “Better” than Multilevel Modelling? • Five Time Points (Small N at Level 1) • Fit Statistics and SEM

  6. Let’s see some equations! Level 1: Level 2:

  7. Obama Dec. Econ. Ret. Dec αObama αecon.ret Obama. Jan. Econ. Ret. Jan Obama Mar. Econ. Ret. Mar βObama βecon.ret Obama Sep. Econ. Ret. Sept Obama Oct. Econ. Ret. Oct What this looks like in a Figure Nice thing about 5 Waves: Relax Linear Growth Assumption

  8. But Wait—That’s Just an Unconditional Growth Model • Race • Ideology • Partisanship • Religiosity • Social Demographics? • How to incorporate? • Before we move on—what do latent intercepts and slopes mean?

  9. Exogenous Socio-Demographic Covariates Party ID Dec Obama Dec. Econ. Ret. Dec Party ID Dec αObama αecon.ret. Party ID Jan Obama. Jan. Econ. Ret. Jan Party ID Jan Party ID Mar Obama Mar. Econ. Ret. Mar Party ID Mar βObama βecon.ret Party ID Sept Obama Sep. Econ. Ret. Sept Party ID Sept Econ. Ret. Oct Party ID Oct Party ID Oct Obama Oct. New Racism, Religiosity and Ideology Factors Conditional Growth Model Figure

  10. How does this change the Equations? Level 1 Equation: Level 2 Equation:

  11. How do we practically estimate an LGM? • MPLUS • WLSMV ESTIMATOR • Rather Quick to Converge • Interpretation of Output and Things to Look for • Means of the latent slopes • Means and Variances of the intercepts and slopes • “Regression Coefficients” • Lambdas • Fit Statistics

  12. What we Get?

  13. Effects of Main Latent Intercepts and Slopes of Interest, Visually

  14. Substantive Meaning of Estimations • Barack, McCain, Barack vs. McCain • Non-linear dynamics • Ideas about Social Demographics • Do the LGMs tell us Anything more than Simpler Estimations? • Other Applications of LGM in Future Research in Political Science?