1. Writing with Data: Incorporating Statistics Into Causal Research Statlab Workshop Spring 2011 Brian Fried and Kevin Callender

2. Outline of Workshop Part I: Causation and Statistics What is Causation? Correlation? What is Causation? Correlation? Why Statistics? Why Statistics? Threats to Inference Threats to Inference Part II: Gathering and Using Data Gathering Data Gathering Data Managing Data Managing Data Part III: Writing with Statistics A General Outline, with an example A General Outline, with an example

3. Causation vs. Correlation Causation… …correlation

4. Why Statistics Probabilistic Relationships (see previous graph) Probabilistic Relationships (see previous graph) Multivariate Relationships We can analyze the relationships between multiple variables at the same time. (e.g. education, age, gender, income …. -> voting) Multivariate Relationships We can analyze the relationships between multiple variables at the same time. (e.g. education, age, gender, income …. -> voting) What is a regression? What is a regression?

5. Threats to Inference Endogeneity (vs exogeneity of errors) Endogeneity (vs exogeneity of errors) Autocorrelation (time series) Autocorrelation (time series) Homo/Heteroskedasticity Homo/Heteroskedasticity Internal vs. external validity Internal vs. external validity Probably the most important step in research design; advanced techniques can often compensate.

6. Part II: Data Think about analyses early! (Ideal vs. Possible) What’s Possible? What’s Convincing? Experimental Ideal Experimental Ideal Practical Data Limitations Practical Data Limitations Collecting Your Own Data Collecting Your Own Data Using Other Data Using Other Data Some data sources: Statlab Webpage (http://statlab.stat.yale.edu) Statlab Webpage (http://statlab.stat.yale.edu) Advisors/Professional Contacts Advisors/Professional Contacts Yale StatCat (http://ssrs.yale.edu/statcat/) Yale StatCat (http://ssrs.yale.edu/statcat/) ICPSR (http://www.icpsr.umich.edu) ICPSR (http://www.icpsr.umich.edu) Reference Librarian (Julie Linden) Reference Librarian (Julie Linden)

7. (Quant.) Data Types and Uses Dependent Variable ( response, outcome, criterion) Independent Variables ( explanatory or predictor variables) Control / Confounding Variables Categorical and Continuous Variables Remember: Types of variables we choose determine the statistics we use Qualitative knowledge always helps!

8. Once You’ve Found or Collected Your Data Download the data and documentation StatTransfer (Statlab) StatTransfer (Statlab) Determine data file type Probably a text file (.txt,.dat,.raw) Probably a text file (.txt,.dat,.raw) Converting text & delimited files Choose a statistical software program

9. Managing your data Back up all Master Data Files Codebook Merging Data Merging Data Adding variables, cases, computing new variables Adding variables, cases, computing new variables Keep a roadmap Keep a log of all analyses with what you have done Keep a log of all analyses with what you have done Save syntax files Save syntax files

10. Syntax Files What are they? Text-files used to enter commands in bulk Why? You will make mistakes, need to make changes How do I know what to write? Program’s manual provides the underlying command

11. Part III: Writing Introduction Theory (Lit Review) Data Description Analysis/ResultsConclusion

12. Introduction Question What is the question you want to answer? Why should we care? Hypothesis Succinctly state your claim Context & Summary

13. Motivation Are politics becoming more programmatic in Brazil? Are politics becoming more programmatic in Brazil? Is Bolsa Familia, a conditional cash transfer (CCT) program that benefits a quarter of Brazil’s population, programmatic? Is Bolsa Familia, a conditional cash transfer (CCT) program that benefits a quarter of Brazil’s population, programmatic? An Illustrative Example: Bolsa Familia

14. Programa Bolsa Família – key facts Conditional cash transfer (CCT) program, launched in October 2003. This was not the first CCT program in Brazil; some existing programs (like Bolsa Escola) were incorporated into Bolsa Familia. Conditional cash transfer (CCT) program, launched in October 2003. This was not the first CCT program in Brazil; some existing programs (like Bolsa Escola) were incorporated into Bolsa Familia. Benefits families with per capita income below US$78. Benefits families with per capita income below US$78. 12 million poor families (almost 50 million people) currently receive support in all 5,564 Brazilian municipalities; 12 million poor families (almost 50 million people) currently receive support in all 5,564 Brazilian municipalities; Size of stipend: between US$13 and US$114, depending on the family’s size and poverty level. Size of stipend: between US$13 and US$114, depending on the family’s size and poverty level. Average amount: US$54 per family Average amount: US$54 per family 2009 Budget: US$ 10.5 billion (0.4% of Brazil’s GDP) 2009 Budget: US$ 10.5 billion (0.4% of Brazil’s GDP) An Illustrative Example: Bolsa Familia

15. Theory/Lit. Review What does existing theory say? What do you believe? What do you believe? Position yourself within theoretical debates. Position yourself within theoretical debates. Identify Testable Hypotheses Choose Method Best Suited to Testing Your Hypothesis Do you need statistics after all? Quantitative v Qualitative research Quantitative v Qualitative research

16. Research Question Do political criteria explain the variation in Bolsa Familia’s coverage across municipalities? Theoretical (Cox and McCubbins 1986, Dixit and Londregan 1996, Lindbeck and Weibell 1987) and empirical (Ames 1987, Levitt and Snyder 1995, Schady 2000, Dahberg and Johansson 2002, Stokes 2004, Kitschelt 2010) reasons to believe that political spending is often targeted, especially given Brazil’s history with clientelism and pork. Theoretical (Cox and McCubbins 1986, Dixit and Londregan 1996, Lindbeck and Weibell 1987) and empirical (Ames 1987, Levitt and Snyder 1995, Schady 2000, Dahberg and Johansson 2002, Stokes 2004, Kitschelt 2010) reasons to believe that political spending is often targeted, especially given Brazil’s history with clientelism and pork. An Illustrative Example: Bolsa Familia

17. How do politicians target? “Core” “Core” “Swing” “Swing” Mobilization Mobilization An Illustrative Example: Bolsa Familia

18. Descriptive Statistics Variables Dependent Variable(s) Independent Variable(s) Important Control Variable(s) Graphs Summary Statistics on Key Variables Number, Mean, Minimum, Maximum, Standard Deviation Cross-Tabs

19. Descriptive Statistics Mean Stand. Dev. MinMaxMissing Dependent Variable Coverage in 20090.9760.2290.0186.27612 Explanatory Variables PT Vote Share for Deputado Federal 0.0600.0480.0000.326345 PT Vote Share for President0.4700.1070.1100.82618 An Illustrative Example: Bolsa Familia

20. Coverage in 2009This continuous variable is the ratio of recipients over the number estimated to be poor in each municipality in November of 2009. PT Voteshare for Deputado FederalThis continuous variable captures a core targeting strategy and measures average PT vote share for federal deputy across the 2002 and 2006 elections. PT Voteshare for PresidentThis continuous variable captures a core targeting strategy and measures average PT vote share for president across the 2002 and 2006 elections. Key Variables An Illustrative Example: Bolsa Familia

21. Descriptive Statistics Mean Stand. Dev. MinMaxMissing Explanatory Variables PT Mayor in 20080.0980.297010 Base Mayor in 20080.6090.488010 Change in Support for PT Presidential Candidate 0.0550.08000.60318 Close Presidential Election in 2006 0.1900.392010 An Illustrative Example: Bolsa Familia

22. So, how do I analyze my data? Correlational design Correlation allows you to quantify relationships between variables (r, r-squared) Correlation allows you to quantify relationships between variables (r, r-squared) Correlation, partial correlation Correlation, partial correlation Regression allows you predict scores on 1 variable from subjects score on another variable(s) Regression allows you predict scores on 1 variable from subjects score on another variable(s) Group differences t-test & ANOVA t-test & ANOVA Chi-square for categorical and frequency data Chi-square for categorical and frequency data Significance v. effect size Simulations

23. Methods of Analysis (Empirical Strategy ) We discussed this in Part I, but one generally devotes a section to explaining how one will identify a causal relationship prior to the results section. Coverage = β 0 + β 1 (political criteria) + β X X + e

24. Results: Explaining Coverage in 2009 Explanatory VariableRegression Coefficient Core Indicators PT Vote Share for Deputado Federal-.473*** PT Vote Share for President-.0972*** PT Mayor-.0241** Base Mayor-.0208*** Swing Indicators Change in Support for PT Presidential Candidate -.175*** Close Presidential Election.00651 An Illustrative Example: Bolsa Familia

25. Effect of Standard Deviation Shift of Explanatory Variables on Coverage in 2009 Shift Explained by Political Criteria Effect of Shift in Support PT Vote Share for Deputado Federal -0.023 PT Vote Share for President -0.010 PT Mayor*-0.024 Base Mayor*-0.021 Change in Support for PT Presidential Candidate -0.014 Close Presidential Election in 2006* 0.007

26. Robustness Identify Threats to Inference! (Do I have any?)

27. Robustness Check: Relationship between Coverage in 2004 and Prior Elections Shift Explained by Political Criteria Effect of Shift in Support PT Vote Share for Deputado Federal in 20020.018 PT Vote Share for President in 20020.034 PT Mayor in 2000*0.002 Base Mayor in 2000*0.005 Change in Support for PT Presidential Candidate (1998 to 2002) -0.003 Close Presidential Election in 2002*-0.016

28. Putting Output into a Paper Cut and Paste Graphs Cut and Paste into Word Processing document Save as.jpeg or.tif file Tables Cut and Paste Format in Word Processing document Import into Excel, format, and then place in Word

29. More Advanced Analysis Multivariate techniques are only a start; they do help to account for confounding factors, allow for testing change over time and more complex hypotheses … (See: Tabachnick & Fidell, Using Multivariate Statistics) 1) Be honest about your abilities. 2) Ask for help 3) Best off including techniques that you fully understand, but may be worth learning something new!

30. Take Away Messages 1) Begin by thinking about what question interests. 2) Look for data and consider appropriate methods; identify what hypotheses are actually testable. 3) Design and run analysis; keep a codebook/syntax files! 4) Back up data 5) Ask for help-especially when choosing method—and seek feedback on research design. 6) Research and Writing an Iterative Process