StatLab Workshop Fall 2011 Brian Fried and Jeremy Green Slide 2
Outline of Workshop Part I: Causation and Statistics What is
Causation? Correlation? Why Statistics? Threats to Inference Part
II: Gathering and Using Data Gathering Data Managing Data Part III:
Writing with Statistics A General Outline, with an example Slide 3
Causation vs. Correlation Causation correlation Slide 4 Why
Statistics 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) What is a regression? Slide 5 When not to
use statistics Insufficient observations. Observations are not
easily comparable. . Other methods may still be appropriate
(historical, interpretative, qualitative, constructivist to name a
few in the social sciences). Slide 6 Threats to Inference
Endogeneity (vs exogeneity of errors) Autocorrelation (time series)
Homo/Heteroskedasticity Internal vs. external validity Probably the
most important step in research design; advanced techniques can
often compensate. Slide 7 Part II: Data Think about analyses early!
(Ideal vs. Possible) Whats Possible? Whats Convincing? Experimental
Ideal Practical Data Limitations Collecting Your Own Data Using
Other Data Some data sources: Statlab Webpage
(http://statlab.stat.yale.edu) Advisors/Professional Contacts Yale
StatCat (http://ssrs.yale.edu/statcat/) ICPSR
(http://www.icpsr.umich.edu) Reference Librarian (Julie Linden)
Public sources (generally government or academic) Slide 8 Data
Types and Uses Dependent Variable ( response, outcome, criterion)
Independent Variables ( explanatory or predictor variables) Control
/ Confounding Variables Categorical and Continuous Variables
Individual vs. Aggregate Data Remember: Types of variables we
choose determine the statistics we use Qualitative knowledge always
helps! Slide 9 Once Youve Found or Collected Your Data Download the
data and documentation StatTransfer (Statlab) Determine data file
type Often a text file (.txt,.dat,.raw) Other formats
(.csv,.sas,.xls) Converting text & delimited files Choose a
statistical software program SPSS a good place for basic analysis
Slide 10 Managing your data Back up all Master Data Files Codebook
Merging Data Adding variables, cases, computing new variables Keep
a roadmap Keep a log of all analyses with what you have done Save
syntax files Slide 11 Syntax Files What are they? Text-files used
to enter commands in bulk Why? You will make mistakes, need to make
changes Many statistical programs let you use pull down menus How
do I know what to write? Program manuals (help screens, online
documentation, hard copy books) provide the underlying commands
Slide 12 Part III: Writing Introduction Theory (Lit Review) Data
Description Analysis/Results Conclusion Slide 13 Introduction
Question What is the question you want to answer? Why should we
care? Audience Hypothesis Succinctly state your claim Context &
Summary Slide 14 Motivation Broad Question: Are politics becoming
more programmatic in Brazil? Testable hypothesis/question: Is Bolsa
Familia, a conditional cash transfer (CCT) program that benefits a
quarter of Brazils population, programmatic? An Illustrative
Example: Bolsa Familia Slide 15 Programa Bolsa Famlia 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.
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; Size of stipend: between
US$13 and US$114, depending on the familys size and poverty level.
Average amount: US$54 per family 2009 Budget: US$ 10.5 billion
(0.4% of Brazils GDP) An Illustrative Example: Bolsa Familia Slide
16 Theory/Lit. Review What does existing theory say? What do you
believe? Position yourself within theoretical debates. Identify
Testable Hypotheses Choose Method Best Suited to Testing Your
Hypothesis Do you need statistics after all? Quantitative vs.
Qualitative research Slide 17 Research Question Do political
criteria explain the variation in Bolsa Familias 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 Brazils
history with clientelism and pork. An Illustrative Example: Bolsa
Familia Slide 18 How do politicians target? Core Swing Mobilization
An Illustrative Example: Bolsa Familia Slide 19 Descriptive
Statistics Variables Dependent Variable(s) Explanatory
(Independent) Variable(s) Control (Independent) Variable(s) Graphs
Summary Statistics on Key Variables Number, Mean, Minimum, Maximum,
Standard Deviation Cross-Tabs Slide 20 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 Slide 21 Descriptive Figures An Illustrative Example: Bolsa
Familia Slide 22 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 Slide 23 Coverage in 2009 This 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 Federal
This 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 President This 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 Slide 24 So, how
do I analyze my data? Correlational design Correlation allows you
to quantify relationships between variables (r, r-squared)
Correlation, partial correlation Regression allows you predict
scores on 1 variable from subjects score on another variable(s)
Group differences t-test & ANOVA Chi-square for categorical and
frequency data Significance v. effect size Simulations Slide 25 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 = 0 + 1 (political criteria) + X X + e
Slide 26 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 Slide 27
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 An Illustrative Example: Bolsa Familia
Slide 28 Identify Threats to Inference! (Do I have any?) Slide 29
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 An
Illustrative Example: Bolsa Familia Slide 30 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 Slide 31 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 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! Slide 32 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 Slide 33 Feedback Please give us feedback! (Go to
the StatLabs workshop page, click on we welcome feedback, and
select Introductory Research Design Or go to:
statlab.stat.yale.edu/workshops/feedback.jsp)
