Thomas J. Scotto University of Essex Harold D. Clarke UT-Dallas and University of Essex.

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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
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Transcript of Thomas J. Scotto University of Essex Harold D. Clarke UT-Dallas and University of Essex.

  • Slide 1
  • Thomas J. Scotto University of Essex Harold D. Clarke UT-Dallas and University of Essex
  • Slide 2
  • In December 2007 and For Primary Season, Election Appeared to be Foreign Policy Based, but this Changes
  • Slide 3
  • 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 Individuals 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
  • Slide 4
  • 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?
  • Slide 5
  • 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
  • Slide 6
  • Level 1: Level 2:
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  • Obama Dec. Obama. Jan. Obama Mar. Obama Sep. Obama Oct. Econ. Ret. Dec Econ. Ret. Jan Econ. Ret. Mar Econ. Ret. Sept Econ. Ret. Oct Obama Obama econ.ret econ.ret Nice thing about 5 Waves: Relax Linear Growth Assumption
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  • Race Ideology Partisanship Religiosity Social Demographics? How to incorporate? Before we move onwhat do latent intercepts and slopes mean?
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  • Party ID Dec Obama Dec. Party ID Jan Obama. Jan. Party ID Mar Obama Mar. Party ID Sept Obama Sep. Party ID Oct Obama Oct. Party ID Dec Econ. Ret. Dec Econ. Ret. Jan Party ID Jan Party ID Mar Econ. Ret. Mar Econ. Ret. Sept Party ID Sept Party ID Oct Econ. Ret. Oct Oba ma Obam a econ.r et econ.r et. New Racism, Religiosity and Ideology Factors Exogenous Socio- Demographic Covariates
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  • Level 1 Equation: Level 2 Equation:
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  • 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
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  • ParameterObama FavorEconomic Retrospections mu-alpha00 mu-beta0.18(.10) + 0.41(.07)*** Lambda(Dec07)00 Lambda(Jan08)0.39(0.22) + 0.91(0.45)* Lambda(Mar08)2.91(0.20)***2.83(0.34)*** Lambda(Sept08)8.40(0.43)***7.72(0.75)*** Lambda(Oct08)10 psi-alpha alpha4.52(.36)***3.25(.25)*** psi-beta beta0.06(.01)***0.02(.004)*** psi-alphabet-0.02(.02)-0.07(.02)*** Latent PredictorAlpha-Obamaalpha-Econ. Ret alpha-Econ Ret.0.08(0.03)**--- Ideology.0.30(0.03)***0.20(0.03)*** Religiosity-0.14(0.04)***-0.01(0.03) New-Racism1.60(0.07)***0.23(0.04)*** Latent PredictorBeta-Obamabeta-Econ. Ret alpha-Econ. Ret.0.05(0.01)*** --- beta-Econ. Ret.0.66(0.10)*** --- Ideology0.02(0.01)***0.00(0.00) Religiosity0.004(0.01)0.01(0.01) + Race0.05(0.01)***-0.013(0.004)** + p