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Slides to accompany Weathington, Cunningham & Pittenger (2010), Chapter 15: Single-Participant Experiments, Longitudinal Studies, and Quasi-Experimental Designs. Objectives. Single- N experiments Types of Single- N designs Longitudinal design Types of longitudinal designs
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Slides to accompany Weathington, Cunningham & Pittenger (2010), Chapter 15: Single-Participant Experiments, Longitudinal Studies, and Quasi-Experimental Designs
Objectives • Single-N experiments • Types of Single-N designs • Longitudinal design • Types of longitudinal designs • Quasi-experiments • Types of quasi-experiments
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
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
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
When Not to Use Single-N • Trying to define a population • Examining differences among populations • When IV is not fully controlled by the experimenter
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
Single-N Cause and Effect • Establish baseline • What is the typical behavior to change? • Offers a sort of “control” condition • Examine effects of intervention • ABAB reversals • Replicate • Follow same procedure with multiple folks • Inter- and intra-person replication (ABAB)
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
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
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
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
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
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
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)
What is Next? • **instructor to provide details