1. Experimental Design

2. Threats to Internal Validity 1.No Control Group Known as a “one-shot case study” XOXO (IV)(DV)

3. Threats to Internal Validity Example of One-Shot Case Study ParticipantsBrush with CrestAsk preference Can’t tell if there was any effect of toothpaste type XO

4. Concepts Basic Experiment An IV with at least 2 levels Experimental Control Random Assignment Strengthens the Internal Validity - We can tell if the IV caused a change in the DV!

5. Concepts Confound When an uncontrolled variable is present in your experiment You cannot identify whether the IV or the uncontrolled variable is causing the change in the DV Weakens internal validity

6. Exercise Identify the confound!

7. Improving Internal Validity What can we do??? Ensure all aspects of the experiment are equal except for the IV manipulation Add a good equivalent control group (before the manipulation!) Any differences between groups can be attributed to your manipulation

8. Improving Internal Validity Basic Control group design Why does the control group have to be equivalent? XOOXOO

9. Threats to Internal Validity Nonequivalent Control group design Selection differences - When participants who form the groups come from existing natural groups; a confound! XOOXOO Overweight Volunteers Traditional Dieters

10. Well Designed Experiments Posttest Only Design XOOXOO Participants random Benefits: Ensures control and experimental groups are equal Limitation: Can’t demonstrate equality for sure; differences in mortality rate

11. Well Designed Experiments Pretest-Posttest Design OXOOOOXOOOO Participants random Benefits: You can see if mortality rate was due to any preexisting condition Limitations: You might sensitize participants to your hypothesis

12. Design Variations 1. Independent Groups design aka Between Groups design 2 (or more) different groups determined by Simple random assignment Matched Pairs random assignment Used when you need to ENSURE equality on some measure

13. Matched Pairs Assignment Measure groups of control variable of interest (e.g., IQ) Arrange highest to lowest Randomly assign 1st pair to each group; repeat for each pair 110 109 107 104 103 101 98 G1G2 110 107 104 103 98 109 107 103 101 98 Whole Sample Means 104.4103.6

14. Design Variations 2. Repeated Measures design aka Within Groups design Each person acts as their own control, so fewer subjects needed Very sensitive to small differences since both groups are identical on everything Problems???

15. Repeated Measures Design Order Effects When the order in which the levels of the IV are presented affect the DV (threatens internal validity) Practice Fatigue Contrast

16. Repeated Measures Design Overcome by Increasing time interval between conditions counterbalancing Randomly divide the sample into groups and administer the levels of the IV in reverse order analyze all groups together

17. Repeated Measures Design Counterbalancing 1st2nd Sample Group A Group B AlcoholSober Alcohol

18. Repeated Measures Design Counterbalancing Problems: The number of possible conditions dramatically increases the number of orders 2 conditions = 2 orders (2 x 1) 3 conditions = 6 orders (3 x 2 x 1) 5 conditions = 120 orders! At 30 Ss per condition, you need a LOT of subjects

19. Repeated Measures Design Overcoming Counterbalancing Problems: Latin Square Design Special procedure for ensuring that each condition occurs at every position (1st, 2nd, etc.) and that each condition occurs before and after every other condition at least once.

20. Latin Square 1st2nd3rd4th Order 1ABDC Order 2BCAD Order 3CDBA Order 4DACB

21. Between Groups vs Repeated Measures Repeated measures advantages Requires fewer participants Reduces differences between groups - better able to detect small differences Between Groups advantage No order effects

22. Between Groups vs Repeated Measures Also consider Generalization - sometimes we experience in the real world variables alone, but sometimes together - choose the design that mirrors the outside world Conditions with permanent changes don’t lend themselves to repeated measures - the sample is “spoiled” in the first condition

23. Conclusions True experiments improve internal validity Use equivalent control groups Random assignment; matched pairs assignment Between Subjects vs Repeated Measures designs Counterbalancing controls for order effects in repeated measures designs