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Agenda

Agenda. Survey approaches to hypothesis testing The logic and methods of sampling. Alternatives to randomization. Matched-group quasi-experiments Selecting variables for group matching Before-and-after quasi-experiments Using each group as its own control

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Agenda

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  1. Agenda • Survey approaches to hypothesis testing • The logic and methods of sampling

  2. Alternatives to randomization • Matched-group quasi-experiments • Selecting variables for group matching • Before-and-after quasi-experiments • Using each group as its own control • Non-experimental survey approaches • Using statistical analysis to address threats

  3. Matched Group Quasi-Experiment X O X O Matching O O

  4. Before-and-After Quasi-Experiment O1 O2 O3 X O4 O5 O6

  5. Cross-Sectional Survey correlation X O Z 1 Z2 X Y Z3 Z4

  6. If Z explains the X-Y relationship, then controlling for Z should eliminate the correlation X Y Newspaper reading Public affairs knowledge Education Whenhigh Z Xhigh Yhigh When Zmedium Xmedium Ymedium When Zlow Xlow Ylow

  7. Surveys • Ubiquitous • Many (not all) cross-sectional • Causal inferences open to multiple threats • Selection bias on independent variable • Temporal order not established • Major strength: Representation of large populations

  8. Why survey? • To infer the characteristics of populations • Populations have parameters • Samples give us parameter estimates • Central tendencies (e.g., average age, typical rates of TV viewing) • Variations (e.g., range of incomes) • Relationships (e.g., correlation of income with TV viewing)

  9. Some key terms • Population vs. frame vs. sample Sample Population (Census) Sampling Frame

  10. Generalization • Are sample parameter estimates biased? • Bias depends upon: • Sampling method • Response rate • Measures used

  11. Sample designs • Availability (convenience) • Quota • Snowball • Purposive • Probability

  12. Probability sampling • Units selected by chance • Permits estimation of sampling error • Design options • Simple random • Systematic random • Stratified random • Cluster or multi-stage random

  13. Logic of probability sampling Example: What percentage of households have broadband connection? Community of 10,000 households • Randomly select 25 • Randomly select another 25 • Randomly select another 25 …

  14. Results of 100 Samples of N = 25 25 20 15 10 5 Std. Dev = 9.07 Mean = 47 0 N = 100.00 25 35 45 55 65 75 SAMPL25 95 our of 100 of surveys within ~18% of 47 One survey estimated 74% Two-thirds of surveys within ~9% of 47 Three surveys estimated 54% Mean

  15. Results of 100 Samples of N = 100 25 20 15 10 5 Std. Dev = 4.29 Mean = 45 0 N = 100.00 25 35 45 55 65 75 SAMPL100

  16. Results of 100 Samples of N = 500 25 20 15 10 5 Std. Dev = 2.39 Mean = 45 0 N = 100.00 25 35 45 55 65 75 SAMPL500

  17. Results of 100 Samples of N = 1000 25 20 15 10 5 Std. Dev = 1.81 Mean = 45 0 N = 100.00 25 35 45 55 65 75 SAMP1000

  18. Confidence intervals • Express our statistical confidence in a parameter “45 percent plus or minus 3 percent” “The 95% confidence interval is from 42 to 48 percent” Meaning: In 100 purely random samples of this size, approximately 95 of the intervals from 42 to 48 percent include the population value.

  19. Confidence intervals • Only possible with probability samples • Mainly a function of sample size • Big boosts in accuracy as samples grow to 1,000 or 1,500 respondents (where intervals are usually +/- 3%) • Do NOT depend on population size* • Will be larger when subgroups are examined

  20. Confidence intervals • Estimated based solely on sampling theory • Assume high response rates • Do not take into account other sources of noise or bias • Questionnaire design • Question wording

  21. For Thursday • Review assignment three • Implementing survey research designs • Schutt, Ch. 8 • Lavrakas on telephone surveys • Focus on basic design and sampling -- not questionnaire design

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