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I. Survey Design Basics

I. Survey Design Basics. A. Foundations. What is your idea or argument? Ex. Public anger about the ACA will hurt the Democrats in 2014. What does that argument imply about data (your hypothesis)? Democrats will do worse than expected or normal

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I. Survey Design Basics

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  1. I. Survey Design Basics

  2. A. Foundations • What is your idea or argument? • Ex. Public anger about the ACA will hurt the Democrats in 2014. • What does that argument imply about data (your hypothesis)? • Democrats will do worse than expected or normal • Their underperformance will be accounted for by public attitudes toward ACA.

  3. B. Conceptualization • What concepts are you trying to measure? • What? Toward whom? When? • Electoral support (what) for Democratic candidates (who) in the November 2014 election (when) • What Quantities are you trying to measure? • Averages: Means, Percentages (expectations, sizes of groups in society) • Quantiles: Medians, quintiles, etc. (value of X such that Q percent are less than X). (inequalities) • Variance: Spread (risk) • Relationships: correlations, differences in means, regressions (association, causation, prediction)

  4. C. Population • Definition. Universe of all persons (or units) you seek to study. • Finite and infinite. Finite: known, fixed population. All people in US today. Infinite: Continuous variables, Future (distribution). Stock market value tomorrow.

  5. D. Sample Construction • Mode of Contact. How communicate. • In person, mail, phone, internet • Who is contacted? • Random • Representative

  6. E. Survey Instrument • Means of collecting information • Question Format • Constraints – time limits, change behavior by asking too much.

  7. F. Examples • Exit Poll Questionnaire: 18 questions Sample Precincts (problem of clustering) Sample individuals as leave Respondents and Non-Respondents Device (paper, handheld?) • Phone Polls Questionnaire about 20 or so questions Random Digit Dialing Very high non-response (what’s random?)

  8. II. Thinking About Data

  9. Questions, Frequencies and Tables In politics today, do you consider yourself to be a Democrat, Republican, Independent, or something else?

  10. Race and Hispanicity

  11. Census Version: Race Question

  12. Census Version: Hispanicity Question

  13. Census Version: Race and Hispanicity are Separate Question

  14. Tabular PresentationRace and Hispanicity

  15. Marginal and Joint Frequency • Terminology • Variables Y = i, i = 1, 2, … I X = j, j = 1, 2, … J • Marginal Frequencies or Probabilities. P(Y=i) or P(X=j) • Joint Frequencies or Probabilities. P(Y=i and X = j)

  16. Tabular PresentationRace and Hispanicity

  17. Tabular PresentationMarginals on Hispanicity

  18. Tabular PresentationRace and Hispanicity

  19. Tabular PresentationJoint Frequencies for Race and Hispanic

  20. Thinking Conditionally • Definition • A Conditional Statement is the Set of Values of a variable (say Y) subject to the restriction that another variable or variables take on a specified set of values. • Terminology. • Y given X=j or Y|X=j • Example: Y=Hispanic|X=Non-White. • That’s different from Y= Hispanic and X = Non-White. How So? How is a statement Y given X different from Y and X?

  21. Conditional Frequency or Probability, Defined • P(Y=i|X=j) = P(Y=i and X = j)/P(X=j) • P(X=j|Y=i) = P(Y=i and X = j)/P(Y=i) • In statistical software these are called row and column percentages in tables. Joint frequencies are called cell frequencies tab y x, row col cel

  22. Census Version: Race and Hispanicity are Separate Question

  23. Census Version: Race and Hispanicity are Separate Question W and H

  24. Ex. Race and Hispanic • Marginal Probabilities Joint Probabilities • Hispanic = Yes: .17 H, W: .15 • Hispanic = No: .83 H, NW: .02 • White = Yes: .78 NH, W: .63 • White = No: .22 NH, NW: .20 • Conditional • P(W|H) = .15/.17 = .88 • P(H|W) = .15/.78 = .19

  25. Example 2. Race and Party (CCES 09)

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