1. The Nonequivalent Groups Design

2. The Basic Design
N O X O
N O O
Key Feature: Nonequivalent assignment

3. What Does Nonequivalent Mean?
Assignment is nonrandom.
Researcher didn’t control assignment.
Groups may be different.
Group differences may affect outcomes.

4. Internal Validity   History Maturation Testing Instrumentation
N O X O N O O
History
Maturation
Testing
Instrumentation
Regression to the mean
Selection
Mortality
Diffusion or imitation
Compensatory equalization
Compensatory rivalry
Resentful demoralization  

5. Internal Validity Selection-history Selection-maturation
N O X O N O O
Selection-history
Selection-maturation
Selection-testing
Selection-instrumentation
Selection-regression
Selection-mortality 
Statistical Analysis of Basic two-group pre-post NEGD
cannot use the usual ANCOVA regression model because of measurement error on the pretest which leads to the attenuation of slopes and introduces bias
have to do a reliability-corrected ANCOVA model
adjusts the pretest scores based on estimates of reliability of the pretest

6. The Bivariate Distribution8 7 6 5 4 3 9 P r e t s o

7. The Bivariate Distribution8 7 6 5 4 3 9 p r e t s P o
Program Group has a
5-point pretest
advantage.

8. The Bivariate Distribution
Program
group
scores
15-points
higher on
Posttest. 8 7 6 5 4 3 9 p r e t s P o
Program group has a
5-point pretest
advantage,

9. Graph of Means pretest posttest pretest posttest
MEAN MEAN STD DEV STD DEV
Comp
Prog
ALL

10. Possible Outcome #1 Selection-history Selection-maturation
Selection-testing
Selection-instrumentation
Selection-regression
Selection-mortality 
 (CG not growing)
(PG moving away, CG level)
More low-score PG dropouts

11. Possible Outcome #2 Selection-history Selection-maturation
Selection-testing
Selection-instrumentation
Selection-regression
Selection-mortality 
 (Both growing)
(Wrong direction)
More low-score dropouts

12. Possible Outcome #3   (In PG only) (In PG)
Selection-history
Selection-maturation
Selection-testing
Selection-instrumentation
Selection-regression
Selection-mortality 
 (In PG only)
(In PG)
More high-score PG dropouts not as likely

13. Possible Outcome #4 Selection-history Selection-maturation
Selection-testing
Selection-instrumentation
Selection-regression
Selection-mortality 
 (In PG only)
(In PG)
 More low-score PG dropouts

14. Possible Outcome #5 Selection-history Selection-maturation
Selection-testing
Selection-instrumentation
Selection-regression
Selection-mortality  