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Alternative Research Designs

Alternative Research Designs. Visualizing Designs. Block notation Useful for visualizing conditions, interactions Design notation Useful for seeing the temporal sequence of the experiment. Design Notation. Elements: Observations/Measures: O Treatments/Programs: X

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Alternative Research Designs

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  1. Alternative Research Designs

  2. Visualizing Designs • Block notation • Useful for visualizing conditions, interactions • Design notation • Useful for seeing the temporal sequence of the experiment

  3. Design Notation • Elements: • Observations/Measures: O • Treatments/Programs: X • Conditions: Each condition has its own line • Assignment to group: • R = Random assignment • N = Nonequivalent groups • C = Assignment by cutoff • Time: Moves from left to right

  4. Design Notation • Kasser & Sheldon (2000) • Bransford & Johnson (1972)? R XMS F O R Xmusic F O

  5. Alternative Research Designs • Quasi-experimental designs • Nonequivalent groups • Interrupted time series • Small-N designs

  6. Quasi-Experimental Designs • Strictly speaking—true experiments use random assignment or counterbalancing • Quasi-experiments: • You cannot randomly assign experimental participants to groups • You DO manipulate an IV • You DO measure a DV

  7. When to use quasi-experimental design? • Study participants in certain groups • Evaluate an ongoing or completed program/intervention • Study social conditions (examples: poverty, race, unemployment) • Expense, time, or monitoring difficulties • Ethical considerations

  8. Nonequivalent Groups Design • Structured like a pretest-postest randomized experiment • Key is to create as equal a comparison group as possible through our selection criteria N O X O (treatment) N O O (comparison)

  9. Nonequivalent Groups Design • Example: Geronimous (1991) • Typical outcomes for teen mothers: • Poverty, high-school dropout rates increase, higher infant mortality • Geronimous believed that family factors, like SES, were better predictors of outcomes than teen pregnancy • How to find a comparison group as similar as possible?

  10. Nonequivalent Groups Design: Analysis • Example: Fry pan company wants to institute a flextime schedule • Pittsburgh plant (experimental group) • Cleveland plant (comparison group) NPitt O Xflextime O NClev O O

  11. Nonequivalent Groups Design: Analysis • Your groups began the experiment as not equivalent • So, the important question is not whether there was a difference • Is the difference the same as before the experiment?

  12. Nonequivalent Groups Design • Threats to Internal Validity • Maturation • Instrumentation • Interaction between selection and history • Statistical regression

  13. Regression and Matching • Use matching as a control procedure to create equivalent groups • BUT! What if the groups come from populations that are different on the factor that’s used as the matching variable?

  14. Regression and Matching • Example: Improving reading skills in disadvantaged youth • Treatment (reading program): Most in need • Comparison group: Similar neighborhood in other cities • Match groups according to initial reading skill

  15. Regression tendency Improvement tendency 25 17 Population mean Regression tendency 35 Population mean 29 25 Regression and Matching Experimental Group Pretest = 25 Reading program Posttest = 25 Comparison Group Pretest = 25 Posttest = 29

  16. Nonequivalent Groups Design • Interpretation of findings must be more cautious than for a true experiment, but a strong research design

  17. Interrupted Time-Series Design • Measure a group of participants repeatedly over time • Interrupt with a treatment • Measure participants repeatedly again O1 O2 O3 O4 X O5 O6 O7 O8

  18. Interrupted Time-Series Design • Threats to Internal Validity • History • Maturation • Instrumentation

  19. O1 O2 O3 O4 X O5 O6 O7 O8 O1 O2 O3 O4 O5 O6 O7 O8 Interrupted Time-Series Design • How to control for history threats? • Frequent measurement intervals • Comparison group

  20. Interrupted Time-Series Design • How to control for history threats? • Frequent measurement intervals • Comparison group • Measure, treatment, measure, then “undo” the treatment, measure again • Not always feasible

  21. Small-N Designs • Single-subject or very few subjects • Why use? • Sample may exhaust the population • A single negative refutes a theory • Participant may be extremely rare • Research is time consuming, expensive, requires lots of training • How is this different from a case study?

  22. Small-N Designs • Behavior of the individual must be shown to change as a result of treatment • Characteristics: • Repeated measures • Baseline measurement • Change one variable at a time

  23. Small-N Designs • A-B design • Problems? • A-B-A design • Problems? • A-B-A-B design

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