BUSH 623: Getting Beyond Fear and Loathing of Statistics

1. BUSH 623: Getting Beyond Fear and Loathing of Statistics Lecture 1 Spring, 2006

2. Don’t Panic • Motivation: this course is about the connection between theoretical claims and empirical data • What we’ll cover (after a very brief review): • Part 1: bi-variate regression • Part 2: multiviariate regression • Part 3: logit analysis and factor analysis

3. The place of statistical analysis • Programs, policies, legislation typically consist of sets of normative claims and a (sketchy?) theory about how to achieve objectives • Policies typically attempt to map a set of beliefs and empirical claims into society, the economy, international relations. (E.g., welfare reform) • Policy analysts need to be able to identify the values served, distill the theory, and evaluate its empirical claims.

4. The place of statistical analysis • Ingredients of strong empirical research • Theory  claims for policy (and counter-claims) • Hypotheses  measurement  analysis • Findings  Back to theory… • Implications for policy • Characterizing data • Data Quality: Valid? Reliable? Relevant? • Appropriate model design and execution • Are statistical models appropriate to test hypotheses? • Are models appropriately specified? • Do data conform to statistical assumptions?

5. How to survive this class • Use the webpage • http://www.tamu.edu/classes/bush/hjsmith/courses/bush632.html • Lectures and book: as close as possible • Readings: Read ‘em or weep. • Questions: Bring ‘em to class, lab, office hours • Or send via email, and (if appropriate) and I will post answers • Stata: Use it a lot • “In-class lab” examples and exercises • Download exercises and data in advance • The place of exercises in Bush 632 • Nothing late; don’t miss class…

6. Class Exams • Three Take-home Exams • Usually 4 days to complete • Characteristics and Grading Criteria • Connection to theory • Clear hypotheses • Appropriate statistical analyses • Clear and succinct explanations • Class data will be provided • National Security surveys • US/European Scientist dataset

7. A Brief Refresher on Functions and Sampling • Statistical models involve relationships • Relationships imply functions • E.g.: Coffee consumption and productivity • Functions are ubiquitous (or chaos prevails) • Most general expression: Y f (X1, X2, … Xn, e)

8. Linear Functions

9. Non-Linear Functions

10. More Non-Linear Functions

11. Functions in Policy • Welfare and work incentives • Employment = f(welfare programs, …) Pretty complex • Nuclear weapons proliferation • Decision to develop nucs = f(perceived threat, incentives, sanctions, …) • Educational Attainment • Test Scores = f(class size, institutional incentives, …) • Successful Program Implementation • Implementation = f(clarity, public support, complexity…)

12. Reliance on samples is also ubiquitous • “Knowing” a person: we sample • “Knowing” places: we sample • Samples are necessary to identify functions • Samples must cover relevant variables, variable ranges, contexts, etc. • Strategies for sampling • Soup and temperature: stir it • Stratify sample: observations in appropriate “cells” • Randomization

13. BREAK Review National Security 2005 survey questions and responses Get them here from the homepage Look at distributions of beliefs; policy preferences Hypothesize about factors influencing public support (as functions) for: Aggressive international response to terror strikes in the US Tough (and possibly intrusive) domestic policies to intercept terrorists in the US