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Research Design Part II: Cross-sectional and Quasi-Experimental Designs Frankfort-Nachmias & Nachmias (Chapter 6 – Cross-Sectional and Quasi-Experimental.

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Presentation on theme: "Research Design Part II: Cross-sectional and Quasi-Experimental Designs Frankfort-Nachmias & Nachmias (Chapter 6 – Cross-Sectional and Quasi-Experimental."— Presentation transcript:

1 Research Design Part II: Cross-sectional and Quasi-Experimental Designs Frankfort-Nachmias & Nachmias (Chapter 6 – Cross-Sectional and Quasi-Experimental Designs) Gerring (Chapter 8) Campbell and Stanley, “Experimental and Quasi-Experimental Designs for Research.” (remainder) King, Keohane and Verba (Chapter 5, sections ) Applications Stack, S. and Gunlach, J. (1992) “The Effect of Country Music on Suicide.” Social Forces 71: 211–18.“The Effect of Country Music on Suicide.” Lawrence S. Rothenberg; Mitchell S. Sanders, “Severing the Electoral Connection: Shirking in the Contemporary Congress.” American Journal of Political Science, Vol. 44, No. 2. (Apr., 2000), pp (Difference-in- difference) Severing the Electoral Connection: Shirking in the Contemporary Congress.

2 Writing a Literature Review  Typical format for research article Introduction Literature Review Theory Research Design Empirical Analysis and Results Conclusion

3 Writing a Literature Review  Purpose of literature review Inform reader of prior relevant work Persuade reader that your work is important (justify your research)

4 Possible Justifications for Your Research  New question, new theory  New question, existing theory  Old question, new theory  Old question, conflicting theories  Old question, conflicting findings  Old question, new methods  Old question, new data

5 Literature Review Don’ts  Don’t (just) provide a chronological listing of article summaries  Don’t provide every detail of every study

6 Literature Review Do’s  Organize your discussion of the literature in a way that reflects and supports the justification for your research  Provide more detail for seminal studies, less detail (or simply a citation) for less cited studies  For questions that have been studied extensively, it is not necessary to cite every study  End your literature review with a summary and critique that justifies your research

7 Literature Review Assignment  Approx pages, double-spaced  Due November 2nd

8 How to Identify the Relevant Literature  Use electronic databases and keyword searches (Google Scholar, JSTOR)  Prioritize: Articles Articles published in highly-ranked journals Recently published articles Seminal articles  Number of articles: 10-20?

9 Quasi-experimental and Cross- Sectional Designs  What are they? Quasi-experimental – study of more than one sample (often over period of time) Cross-sectional – Analysis of a single sample (lacks random assignment, temporal variation, and manipulation); but includes comparison groups Pre-experimental – Cross-sectional, with no comparison group; causal inference impossible

10 Quasi-experimental and Cross- Sectional Designs  Why? Property-disposition relationship vs. Stimulus-response relationship

11 Pre-Experimental Designs  One-Shot Case Study One group No variation in independent variable X O

12 Pre-Experimental Designs  Example: Dependent variable: Americans’ support for campaign finance reform Independent variable: Watergate scandal Data: 1976 survey of American adults; examine mean level of support

13 Cross-Sectional Designs  Static-Group Comparison Design Two groups – observed at one time Allows variation in the independent variable X O 1 O 2

14 Elaboration of Static Group Comparison Design  Correlational / Cross-Sectional Designs X1 O 1 X2 O 2 X3 O 3 X4 O 4 Xi O i  Problems with correlational/cross-sectional designs?

15 Example: Wine and Health  Hypothesis: Drinking wine causes individuals to be healthier (esp. heart)  Existing studies: compared the health of wine drinkers to the health of those who do not drink wine: Research design X 1 (Wine drinkers) O 1 (Health) X 2 (Non-drinkers) O 2 (Health)

16 Spurious Results?

17 Controlling for Affluence Research design: X 1 (Affluent Wine drinkers) O 1 (Health) X 2 (Affluent Non-drinkers) O 2 (Health) X 3 (Poor Wine drinkers) O 3 (Health) X 4 (Poor Non-drinkers) O 4 (Health)

18 Another Example (?)  dyn/content/article/2006/05/25/AR html dyn/content/article/2006/05/25/AR html  4/ htm 4/ htm

19 Quasi-Experimental Designs  Contrasted Groups Design Multiple groups, based on some categorical variable Observed at one point in time (similar to cross-sectional) O1 O2 O3 O4 Oi  Problems with contrasted groups designs?

20 Quasi-Experimental Designs  One-Group Pretest-Posttest Design One group Allows variation in the independent variable O1 X O2

21 Elaborations of the One-Group Pretest- Posttest Design  Time Series Designs simple vs. extended O 1 …O k X O k+1 …O m k = # of pretest observations m = total # observations Or (“equivalent time samples design”) X 1 O 1 X 2 O 2 X 3 O 3 … X m O m

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23 Example: Murray’s “Poverty-Spending Paradox” (Schram 1991)

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25 Nonequivalent Control Group Design  No random assignment O 1 X O 2 O 3 O 4 or O 1 X 1 O 2 O 3 X 2 O 4

26 Control Series Designs  Addition of second (control) group to time series design (CS: “multiple time series design”) O1 O2 O3 X1 O4 O5 O6…… O7 O8 O9 X2 O10 O11 O12……

27 Panel Designs  Repeated observations of the same units over time Also goes by:  Pooled time-series design  Pooled cross-sectional time-series design

28 The Effect of Sanctioning in the TANF Program

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32 Donahue and Levitt  Hypothesis: The (legal) availability of abortion in a state is negatively related to the crime rate (many years later). O1 O2 O3 X1(early legalization) O4 O5 O6… O7 O8 O9 X2 (legalize 1973) O10 O11 O12…

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34 Gerring’s Criteria for Research Design  Plentitude (N)  Boundedness (relevant cases)  Comparability (descriptive/causal)  Independence  Representativeness  Variation (X, Y, X&Y)  Analytic Utility (of the sample)  Replicability  Mechanism  Causal Comparison

35 KKV – Overcoming Common Problems  Omitted Variable Bias  Inclusion of Irrelevant Variables  Endogeneity  Assigning Values of the IV’s  Controlling the Research Situation

36 Does Country Music Cause Suicide?

37 Stack & Gundlach  Hypothesis: There is a positive relationship between exposure to country music and suicide rates  Research design: X 1 (no country music) O 1 (suicide rate) X 2 (1 station) O 2 (suicide rate) X 3 (2 stations) O 3 (suicide rate) X 4 (3 stations) O 4 (suicide rate) X i ( etc.) O i (suicide rate)

38 Stack & Gundlach  Findings: 51% of the variation in urban white suicide rates can be explained by variation in airtime devoted to country music  Internal Validity?

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45 O 1A Retired A O 4A O 2B SW-Office B O 5B O 3C Returned C O 6C

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47 Regression Discontinuity Designs  Example: What is the effect of an award on later achievement? (from CS)

48 Regression Discontinuity Designs  Example: What is the effect of an award on later achievement? (from CS) Inferential challenge: Award recipients are likely to do well anyway, even without the award, because criteria for receiving award also predict future success

49 A regression discontinuity design is appropriate for any research design in which the assignment of the treatment is determined by a continuous variable that is also related to the outcome of interest.

50 Ludwig and Miller, 2007

51 Head Start  Head Start is a federal program that provides comprehensive education, health, nutrition, and parent involvement services to low- income children and their families.  Head Start began in 1965  Local nonprofits/education agencies must apply to federal government to be a Head Start provider

52 Head Start  Research Question: What is the effect of Head Start on health/well-being of former participants?  What problems might we encounter if we were to conduct the following study: X O (where X = Head Start participation) O

53 Ludwig and Miller, 2007  OEO and application assistance to poor counties in 1965 “Treatment” group – 300 poorest counties that received head start application assistance “Control” group – 228 poor counties that did not

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