Introduction to Statistics: Political Science (Class 1) Answering Political Questions with Quantitative Data (political variables, review of bivariate regression, & thinking about causality)

Why learn how to answer political questions with quantitative data? Area to apply/practice using statistics –Tools can be applied elsewhere (on the job, health decisions [Atkins/gluten free?]) Understand cause and effect in politics –Academic reasons – develop knowledge that can be passed on to others –As a citizen – evaluate evidence about policies; who deserves credit/blame Prepare for your future responsibilities as political officials???

What types of questions can data analysis help us to answer? International relations –Why do countries go to war? Comparative politics –Why does the rate of infant mortality vary across countries? Policy –How can we improve student test scores? Public opinion/political behavior –How do people decide whether to vote? –What policies does the public support and why?

Today’s agenda… Measuring political concepts Review of bivariate regression Thinking about causality

Measurement: Units of analysis What are the cases/rows in political data? Actors: individuals, elected officials Geographic/political units: states, countries, precincts Events: individual congressional races, elections (e.g., “seats won”), court cases Unit/Time: country-year, individual at time T

Measurement: Data Sources Government / historical records –Vote by precinct; GDP/economic data; individual turnout Expert assessments –Level of democracy; presidents’ personalities Surveys –Reported attitudes / behaviors

For example…. Distribution of a variable in politics What is this “margin of error +/- 3%”?

Relationships between variables (regression analysis) Two types of variables: –Dependent variable (or predicted variable or “regressand”) – what we want to predict –Independent variable (or explanatory variable or “regressor”) Bivariate regression model Υ = β 0 + β 1 X + u

How does presidential approval affect midterm election outcomes? Unit of analysis: midterm election ( ) Dependent variable: seats gained by incumbent president’s party (House) Independent variable: presidential approval on Labor Day of election year –0 (no one approves) 100 (everyone approves) Coef SE Coef T P Presidential Approval Constant

Υ = β 0 + β 1 X + u Seats = (1.32 * Approval) + u In 1978, Carter’s approval was 49(%) Remember: in regression analysis (aka “Ordinary Least Squares”), the “best fit” line is the one that minimizes the sum of the squared residuals -15 Obama’s approval rating was 46(%)

Democratic Peace Theory: Democracies tend not to go to war with one another – why would this be? What does a democracy look like? How could we measure “democracy”?

Polity III Democracy score (0-10) Competitiveness of Executive Recruitment –Selection (e.g., hereditary, military-based, rigged) (0 points) –Dual/Transactional (one hereditary/one by elections) (1 point) –Election (2 points) Constraints on Chief Executive –Unlimited Authority (0 points) –Substantial limitations (2 points) –Parity/Subordination (4 points) Openness of Executive Recruitment –0 or 1 point Competitiveness of participation –Repressed/no participation (0 points) –Factional (ethnic/parochial factions battle it out; 1 point) –Transitional –Competitive (stable and enduring secular political groups compete for political influence at the national level; 3 points)

Democracy  Peace? Units of analysis: country-dyad-years –Restricted to “relevant” dyads ( ) Dependent variable: number of years the pair of countries have been at peace Independent variable: sum of countries’ democracy scores (0-20) Coef SE Coef T P Democracy Scores Constant Why are these SEs so small / T values so big??? N=35,554

Causal relationships Identifying associations is nice, but usually we want to identify causality Two primary threats –Reverse causation If we find an association, what causes what? –Confounding / missing variables Additional factors that might lead us to give too much “credit” to an explanatory variable

Reverse Causation? Intent to Vote Contact by a Political Campaign ? NOTE: Solid lines = proposed causal relationship; dotted lines = non-causal correlation Let’s say we have some survey data…

Missing variable? Forest Fires Ice Cream Sales ? Hot Weather “Common Response” NOTE: Solid lines = proposed causal relationship; dotted lines = non-causal correlation

What else might explain midterm outcomes? Were we giving too much “credit” to presidential approval ratings as an explanation in our bivariate analysis? Presidential Approval  Midterm Outcomes Midterm Outcomes Presidential Approval (Labor Day before election) ? Economic Conditions

Democracy  Peace? Pair of Countries (do not) Go to War Level of Democracy in Pair of Countries ? Military Power of Pair of Countries Explanations for lower likelihood of war that might confound the relationship between democracy and peace?

For the next few weeks… Thinking about and accounting for more than one possible explanation –Next 4 classes: using multivariate regression to deal with known, measured confounds –Later: dealing with unknown confounds and reverse causation

Goals By the end of the semester you will be......able to conduct and interpret multivariate regression analysis and analyze experimental data...better prepared to understand quantitative findings reported in political science (and other) research...able to think critically about and recognize the strengths and weaknesses of these analyses

Grading/expectations No new books – but you’re encouraged to have *a book* 4 homework assignments –Conduct and interpret analysis –Think about how analyses could be improved Participation –If you don’t understand, ask! The final: about 1/3 focused on first segment of the class, 2/3 on this segment

Note on next week First homework assignment will be handed out this Thursday. Due next Thursday. No class next Tuesday TAs will hold extra office hours on Monday (November 1 st – see syllabus for times) Take a look at the homework before Monday – you may need help!

Next time (Thursday) What multiple regression analysis (regression with more than one explanatory variable) can get us