1. Slides to accompany Weathington, Cunningham & Pittenger (2010), Chapter 15: Single-Participant Experiments, Longitudinal Studies, and Quasi-Experimental Designs 1

2. Objectives Single-N experiments Types of Single-N designs Longitudinal design Types of longitudinal designs Quasi-experiments Types of quasi-experiments 2

3. Single-N Experiments Consider how IVs influence a participant –Commonly focused on behavioral outcomes Fechner, Ebbinghaus, and Skinner all used these techniques –Experimental analysis of behavior Used when examining effects of systematic changes in environment 3

4. Single-N: Reliability & Validity Sample size does not determine the validity of a study External validity depends on the type of generalization –When variability is expected to be low, large N not required for external validity (e.g., Figure 15.1) –Internal validity is about variable relationships (cause and effect), which can be observed with few participants 4

5. When to Use Single-N When the researcher has: –Direct control over IV –Ability to regularly measure participant’s behavior –Ability to observe over a long period of time e.g., Studying effect of specific environmental change on behavior –Clinical interventions 5

6. When Not to Use Single-N Trying to define a population Examining differences among populations When IV is not fully controlled by the experimenter 6

7. Types of Single-N Designs Baseline study –Changes in ongoing behaviors –Requires reliable behavior measure –Baseline measurement = control Discrete trial study –Response of participant to specific test conditions 7

8. Single-N Cause and Effect 1.Establish baseline What is the typical behavior to change? Offers a sort of “control” condition 2.Examine effects of intervention ABAB reversals 3.Replicate Follow same procedure with multiple folks Inter- and intra-person replication (ABAB) 8

9. Multiple Baseline Design Alternative to reversal designs (ABAB) Ongoing measurement of behavior, systematic introduction of the IV at different times Multi-baseline across participants, behaviors, or situations See Figure 15.3 9

10. Figure 15.3 10

11. Longitudinal Design For studying how behavior changes over time Requires monitoring sample over time Good for isolating cause and effect relationships Expensive and challenging –Attrition is a problem –Cohort effect 11

12. Cross-Sectional Sequential Design Good for developmental transition studies Does not take as much time as a full longitudinal study Can study groups of people from different age ranges, over time See Figure 15.5 12

13. Figure 15.5 13

14. Survival Analysis Alternative to: –Correlated groups ANOVA (which may have overly restrictive assumptions for your data) –Longitudinal design (which carry a high attrition risk) Time between events is a DV, not IV –Goal is to determine how long it takes an event to occur Can correct for attrition and still provide results that can be validly interpreted 14

15. Quasi-Experiments Useful if true experiment is impractical or unethical Uses an IV and DV and a control group Lacks random assignment to groups –Cannot rule out all alternative explanations Several forms of designs can be considered quasi-experiments 15

16. Nonequivalent Control-Group Two, pre-existing groups Researcher determines which gets the IV and which is the control group Pre-/post- measure of DV for both groups Main threats to validity: –History –Regression to the mean –Instrumentation 16

17. Interrupted Time Series Repeated measures of behavior in a sample pre- /post- a critical event Cannot easily rule out alternative explanations –Better if a control group available (see Figure 15.7 for example) 17

18. Figure 15.7 18

19. What is Next? **instructor to provide details 19