First: getting started Clear your workspace Anna Petherick: - PowerPoint PPT Presentation

First: getting started Clear your workspace Anna Petherick: anna.petherick@politics.ox.ac.uk

Quick revision I

Quick revision II • Equation for a straight line y=mx+b (or y=b+mx) • Thus, the equation we estimate to describe the relationship between x and y takes the form: • data = model + residuals • data = intercept + coefficient*IV + error

Quick revision III

Quick revision IV • A, B, C, D = different models with same DV • IVs labelled clearly • P-values • Standard errors • Model fit • Number of cases.

Model Specification

What to control for? • Multivariate regression allows us to control for confounders or other possible causes . Theory tells us what these things are. • • Two strategies for thinking of confounding variables: 1. Think of things that affect y but are not in the regression model , and then ask yourself whether they might be related to x. 2. Think of things that affect x (or things that are related to and precede x) but are not in the regression model , and then ask yourself whether they might be related to y.

What to control for? • Absence of control variables can lead to omitted variables bias

Omitted-Variables Bias • Bias: expected value of parameter estimate from sample =/= true population parameter • Omitted-variables bias: bias resulting from failure to include a variable that belongs in the model • So which variables can we omit? Can omit Z if completely unrelated to Y • Can omit Z if completely unrelated to X • Both of which are unlikely •

Omitted-Variables Bias • Failing to control for relevant variables can lead to mistaken causal inferences for variables we do include • Bias may be small , and findings may be right • Or it may be large , and findings may be wrong • Consider this when doing own research, and when reading research articles/books • Can you think of any other independent variables that are likely to be related to both x and y?

What NOT to control for? • Consequences of the treatment variable X Z Y • Can lead to post-treatment bias Effect of party ID on vote choice • Do control for race • Do not control for last-minute voting intentions • Effect of medicine on health • Do control for health prior to treatment decision • Do not control for side effects of treatment •

Think Carefully… • Careful theory: tells us which variables to include

If you want more… Regression bits, Kellstedt & Whitten, Fundamentals of Pol. Sci..Res. p209-216 . Comp Gov: 1. Tsebelis, G. and Nardi, D.J. (2014) ‘A Long Constitution is a (Positively) Bad Constitution: Evidence from OECD Countries’, BJPS. 1 -22. 2. Ross, M. (2006) ‘Is democracy good for the poor?’, AJPS, 50(4), 860 -874. Challenges the view that democracies have any effect on infant and childhood mortality rates. Pol Soc: 1. Jacobsmeier and Lewis (2013). Barking Up the Wrong Tree: Why Bo Didn’t Fetch Many Votes for Barack Obama in 2012. PS: Political Science & Politics, 46(1), 49-59. 2. Inglehart and Norris (2003: Chapter 5) IR: 1. Maoz, Z., and B. Russett. 1993. "Normative and structural causes of democratic peace, 1946- 1986.” American Political Science Review 87: 624– 638. 2. Gartzke, Erik. 2007. "The capitalist peace." AJPS. 51:166 – 191.

Next week: Interactions! — > Brambor, T., Clark W.R. and Golder, M. (2007) ‘Are African party systems different?’, Electoral Studies, 26(2), 315-323.