1 / 18

Other Quasi-Experimental Designs

Other Quasi-Experimental Designs. Design Variations. Show specific design features that can be used to address specific threats or constraints in the context. Proxy Pretest Design. N O 1 X O 2 N O 1 O 2. Pretest based on recollection or archived data

bevan
Download Presentation

Other Quasi-Experimental Designs

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. Other Quasi-Experimental Designs

  2. Design Variations Show specific design features that can be used to address specific threats or constraints in the context

  3. Proxy Pretest Design N O1 X O2 N O1 O2 • Pretest based on recollection or archived data • Useful when you weren’t able to get a pretest but wanted to address gain

  4. Separate Pre-Post Samples N1 O N1 X O N2 O N2 O • Groups with the same subscript come from the same context. • Here, N1 might be people who were in the program at Agency 1 last year, with those in N2 at Agency 2 last year. • This is like having a proxy pretest on a different group.

  5. Separate Pre-Post Samples R1 O R1 X O R2 O R2 O N • Take random samplesat two times of people at two nonequivalent agencies. • Useful when you routinely measure with surveys. • You can assume that the pre and post samples are equivalent, but the two agencies may not be. N

  6. Double-Pretest Design N O O X O N O O O • Strong in internal validity • Helps address selection-maturation • How does this affect selection-testing?

  7. Switching Replications N O X O O N O O X O • Strong design for both internal and external validity • Strong against social threats to internal validity • Strong ethically

  8. Nonequivalent Dependent Variables Design (NEDV) N O1 X O1 N O2 O2 • The variables have to be similar enough that they would be affected the same way by all threats. • The program has to target one variableand not the other.

  9. NEDV Example • Only works if we can assume that geometry scores show what would have happenedto algebra if untreated. • The variable is the control. • Note that there is no control grouphere.

  10. NEDV Pattern Matching • Have many outcome variables. • Have theory that tells how affected(from most to least) each variable will be by the program. • Matchobserved gains with predicted ones. • If match, what does it mean?

  11. NEDV Pattern Matching • A “ladder” graph. • What are the threats? r = .997

  12. NEDV Pattern Matching • Single group design, but could be used with multiple groups(could even be coupled with experimental design). • Can measure left and right on different scales(e.g., right could be t-values). • How do we get the expectations?

  13. Regression Point Displacement (RPD) N(n=1) O X O N O O • Intervene in a single site • Have manynonequivalent control sites • Good design for community-based evaluation

  14. RPD Example • Comprehensive community-based AIDS education • Intervene in one community (e.g., county) • Have 29 other communities(e.g., counties) in state as controls • measure is annual HIV positive rate by county

  15. RPD Example 0 . 0 7 0 1 0 . 0 6 Y 0 . 0 5 0 . 0 4 0 . 0 3 0 . 0 3 0 . 0 4 0 . 0 5 0 . 0 6 0 . 0 7 0 . 0 8 X

  16. RPD Example 0 . 0 7 0 1 0 . 0 6 Regressionline Y 0 . 0 5 0 . 0 4 0 . 0 3 0 . 0 3 0 . 0 4 0 . 0 5 0 . 0 6 0 . 0 7 0 . 0 8 X

  17. RPD Example 0 . 0 7 0 1 0 . 0 6 Regressionline Y 0 . 0 5 Treated communitypoint 0 . 0 4 0 . 0 3 0 . 0 3 0 . 0 4 0 . 0 5 0 . 0 6 0 . 0 7 0 . 0 8 X

  18. RPD Example 0 . 0 7 0 1 0 . 0 6 Regressionpine Y 0 . 0 5 Treated communitypoint 0 . 0 4 Posttestdisplacement 0 . 0 3 0 . 0 3 0 . 0 4 0 . 0 5 0 . 0 6 0 . 0 7 0 . 0 8 X

More Related