Other quasi experimental designs
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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. NO 1 XO 2 NO 1 O 2. Pretest based on recollection or archived data

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Other Quasi-Experimental Designs

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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

NO1XO2

NO1O2

  • Pretest based on recollection or archived data

  • Useful when you weren’t able to get a pretest but wanted to address gain


Separate Pre-Post Samples

N1O

N1XO

N2O

N2O

  • 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.


Separate Pre-Post Samples

R1O

R1XO

R2O

R2O

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


Double-Pretest Design

NOOXO

NOOO

  • Strong in internal validity

  • Helps address selection-maturation

  • How does this affect selection-testing?


Switching Replications

NOXOO

NOOXO

  • Strong design for both internal and external validity

  • Strong against social threats to internal validity

  • Strong ethically


Nonequivalent Dependent Variables Design (NEDV)

NO1XO1

NO2O2

  • 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.


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.


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?


NEDV Pattern Matching

  • A “ladder” graph.

  • What are the threats?

r = .997


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?


Regression Point Displacement (RPD)

N(n=1)OXO

NOO

  • Intervene in a single site

  • Have manynonequivalent control sites

  • Good design for community-based evaluation


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


RPD Example

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RPD Example

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RPD Example

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