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

Other Quasi-Experimental Designs


Design variations

Design Variations

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


Proxy pretest design

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

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 samples1

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

Double-Pretest Design

NOOXO

NOOO

  • Strong in internal validity

  • Helps address selection-maturation

  • How does this affect selection-testing?


Switching replications

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

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

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

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 matching1

NEDV Pattern Matching

  • A “ladder” graph.

  • What are the threats?

r = .997


Nedv pattern matching2

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

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

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 example1

RPD Example

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Rpd example2

RPD Example

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Rpd example3

RPD Example

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Rpd example4

RPD Example

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