1 / 21

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

cissy
Download Presentation

Agenda

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related