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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

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Other Quasi-Experimental Designs

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  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

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