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The Nonequivalent Groups Design

The nonequivalent groups design is a critical research framework where groups are assigned nonrandomly, leading to inherent differences that may affect outcomes. Researchers often cannot control the assignment, resulting in potential threats to internal validity such as selection bias, history effects, and testing issues. This overview addresses the fundamentals of nonequivalent assignment, the meaning of "nonequivalent," and factors influencing group differences. Understanding these elements is essential for interpreting research findings and ensuring quality in studies utilizing this design.

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The Nonequivalent Groups Design

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  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 Regression to the mean Selection Mortality Diffusion or imitation Compensatory equalization Compensatory rivalry Resentful demoralization N O X O N O O           

  5. Internal Validity N O X O N O O Selection-history Selection-maturation Selection-testing Selection-instrumentation Selection-regression Selection-mortality      

  6. 9 0 8 0 7 0 t s e t 6 0 t s o P 5 0 4 0 3 0 3 0 4 0 5 0 6 0 7 0 8 0 P r e t e s t The Bivariate Distribution

  7. 9 0 8 0 7 0 t s e t 6 0 t s o P 5 0 4 0 3 0 3 0 4 0 5 0 6 0 7 0 8 0 p r e t e s t The Bivariate Distribution Program Group has a 5-point pretest advantage.

  8. 9 0 8 0 7 0 t s e t 6 0 t s o P 5 0 4 0 3 0 3 0 4 0 5 0 6 0 7 0 8 0 p r e t e s t The Bivariate Distribution Program group scores 15-points higher on Posttest. Program group has a 5-point pretest advantage,

  9. pretest posttest pretest posttest MEAN MEAN STD DEV STD DEV Comp 49.991 50.008 6.985 7.549 Prog 54.513 64.121 7.037 7.381 ALL 52.252 57.064 7.360 10.272 Graph of Means

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

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

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

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

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

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