1. Chapter 8 Experimental Design: Dependent Groups and Mixed Groups Designs

2. Dependent Groups Designs Matched Designs Matched Designs Within-Participants Designs Within-Participants Designs –More than one IV – WP Factorial Design

3. Repeated Measures/With-Participants Design Each participant is his or her own control. Each participant is his or her own control. –Increased statistical power (the likelihood of detecting an effect if one is present). –More economical since fewer subjects are needed Often the DV is assessed at multiple time points such as before and after a “treatment” Often the DV is assessed at multiple time points such as before and after a “treatment” –Repeated Measures Repeating treatment conditions enables the participants to identify what is being manipulated. Repeating treatment conditions enables the participants to identify what is being manipulated. –Demand Characteristics  Concern over demand characteristics prevents more widespread use of repeated measures designs

4. Repeated Measures/Within- Participants Design The potential for increased economy and statistical power is weighed against the potential threats to internal validity The potential for increased economy and statistical power is weighed against the potential threats to internal validity –Confounds - conditions that vary systematically with changes in the level of the independent variable  When present in an experiment, it is impossible to tell whether changes in the dependent variable resulted from the different levels of the independent variable or from the different levels of the confounded variable. –History –Maturation –Testing

5. History Anything that happens between the pretest and posttest that is not part of the experimental situation Anything that happens between the pretest and posttest that is not part of the experimental situation The longer the interval between pretest and posttest, the greater the potential for history effect The longer the interval between pretest and posttest, the greater the potential for history effect It is important to know that this is not specifically time passage, but events that occur during that time It is important to know that this is not specifically time passage, but events that occur during that time –Equipment issues –Life events

6. To Minimize History Effects Shorten the interval between the pretest and posttest, so things will not happen Shorten the interval between the pretest and posttest, so things will not happen Control the environment the pretest and posttest as much as possible Control the environment the pretest and posttest as much as possible

7. Maturation Internal processes that occur as a function of the passage of time Internal processes that occur as a function of the passage of time –Growth and aging processes –Motivational effects such as practice and fatigue Especially a problem in: Especially a problem in: –Longitudinal research –Educational research –Therapy research

8. To Minimize Maturation Effects Minimize the interval of time between pretest and posttest Minimize the interval of time between pretest and posttest Keep all experimental conditions identical during pretest and posttest Keep all experimental conditions identical during pretest and posttest

9. Testing Taking a test once affects scores on the second test Taking a test once affects scores on the second test –Taking a pretest affects scores on a posttest  Stroop

10. To Minimize Testing Effects Use alternate forms, if available Use alternate forms, if available Lengthen the interval between pretest and posttest Lengthen the interval between pretest and posttest

11. Determining the Levels of the Factor There should be enough levels to represent the range of values of the treatment variable. There should be enough levels to represent the range of values of the treatment variable. There should be enough levels to show the exact nature of the relationship being tested. There should be enough levels to show the exact nature of the relationship being tested. A true control condition is one in which the treatment variable is absent. A true control condition is one in which the treatment variable is absent. Certain problems arise when manipulating the IV – Carry Over Effects Certain problems arise when manipulating the IV – Carry Over Effects

12. Carry Over Effects Because each participant experiences multiple treatment combinations, experiencing the early treatments can affect responses to subsequent treatments Because each participant experiences multiple treatment combinations, experiencing the early treatments can affect responses to subsequent treatments –Also called “transfer effects”  Order Effects  Differential Order Effects

13. Dealing with Order Effects Randomly determine the order of treatments Randomly determine the order of treatments –Difficult to do unless you have a large number of participants. Completely counterbalanced approach Completely counterbalanced approach –Requires that each condition occurs equally often, and precedes and follows all other conditions the same number of times. Incomplete counterbalancing Incomplete counterbalancing –Requires that each condition occurs equally often.

14. Counterbalancing ParticipantOrder of Conditions 1LAMAHA 2MAHALA 3HALAMA 4LAHAMA 5MALAHA 6HAMALA

15. Differential Order Effects When some levels of the IV may irreversibly influence the DV When some levels of the IV may irreversibly influence the DV –Certain orders may permanently change the individuals  Example – A teaching technique Cannot be controlled by counterbalancing Cannot be controlled by counterbalancing –Need to do a Between-Subjects Design

16. Repeated Measures Designs ADVANTAGES Require fewer subjects Require fewer subjects Take less time to complete Take less time to complete More powerful than between subjects designs because each participant is compared with him/herself More powerful than between subjects designs because each participant is compared with him/herself –Error variance is reduced DISADVANTAGES Problems posed by history, testing, and maturation Problems posed by history, testing, and maturation Problems posed by multiple treatment interference effects Problems posed by multiple treatment interference effects –Differential Order Effects

17. Example – THC, Alcohol & Driving Many studies have shown that both THC and alcohol impair driving ability. Many studies have shown that both THC and alcohol impair driving ability. No studies have compared the two drugs, nor have any studies examined subjective experiences of the drugs while driving No studies have compared the two drugs, nor have any studies examined subjective experiences of the drugs while driving How do we do this study with a within subjects design? How do we do this study with a within subjects design?

18. Starting with the Title

23. Data Analysis - Partitioning the Variance The treatment effect is estimated from differences within subjects rather than between subjects. The treatment effect is estimated from differences within subjects rather than between subjects. Between subjects variance reflects differences between subjects, NOT due to the treatment. Between subjects variance reflects differences between subjects, NOT due to the treatment.

24. Analysis of Variance Summary Table Between subjects variance is listed first and then removed from further consideration Between subjects variance is listed first and then removed from further consideration Within subjects variance is partitioned into the variance due to the treatment and error variance (variance due to chance factors). Within subjects variance is partitioned into the variance due to the treatment and error variance (variance due to chance factors). The F ratio compares the variance due to the treatment (numerator) to the error variance (denominator). The F ratio compares the variance due to the treatment (numerator) to the error variance (denominator).

25. Interpretation of the Results of ANOVA Calculated F is compared to critical F: If calculated f is equal to or greater than critical F: Calculated F is compared to critical F: If calculated f is equal to or greater than critical F: F is significant F is significant There is a significant difference among the means of the different treatment levels There is a significant difference among the means of the different treatment levels

26. Get it?

27. Data Analysis

28. Results

32. Discussion Describes the outcome of the research in words Describes the outcome of the research in words –Briefly summarize what you found Integrates the outcome of the study with previous research findings Integrates the outcome of the study with previous research findings –Here is how you data fit in to the larger literature base Draws conclusions Draws conclusions –Based on theory or practical application May present suggestions for future research May present suggestions for future research –Limitations

33. Discussion – Explaining the effect

34. Discussion – Implications & Meaning

35. Discussion – Addressing a limitation

36. Discussion – A summary of the results that happens to be a conclusion

37. Discussion – Addressing a limitation

38. Example – 2X2 WS Factorial

39. Question/Problem Increase in the coadministration of alcohol and caffeine (energy drinks) in college students Increase in the coadministration of alcohol and caffeine (energy drinks) in college students Little research on the interactive effects. Little research on the interactive effects. How does caffeine influence the effects of alcohol on information processing tasks? How does caffeine influence the effects of alcohol on information processing tasks?

40. Methods

41. Procedures & Results

43. Mixed Designs Have at least two independent variables Have at least two independent variables At least one variable is a between subjects factor. At least one variable is a between subjects factor. At least one variable is a within subjects factor. At least one variable is a within subjects factor.

44. Reasons for Using Mixed Designs Repeated measures factors are desirable because they require fewer participants and can take less time. Repeated measures factors are desirable because they require fewer participants and can take less time. Some variables are manipulated between participants to reduce or prevent fatigue, interference effects, or demand characteristics. Some variables are manipulated between participants to reduce or prevent fatigue, interference effects, or demand characteristics. Sometimes participant variables are studied (e.g., gender, handedness, etc.) Sometimes participant variables are studied (e.g., gender, handedness, etc.)

45. Example - Our THC and STM Study Problem – The effects of THC Intoxication on the ability to do occupational tasks requiring STM Problem – The effects of THC Intoxication on the ability to do occupational tasks requiring STM Research Hypothesis – THC intoxication will impair STM Research Hypothesis – THC intoxication will impair STM IV – Three smoked THC doses IV – Three smoked THC doses –0%, 5%, 10% DV – Span test for words at different time intervals DV – Span test for words at different time intervals –15min, 1hr and 3hrs

46. Descriptives

47. Testing the WP Factor

48. Testing Levels of WP Factor

49. Testing the BS Factor

50. Post Hoc Test on the BS Factor

51. Matched groups designs Provides the added power of dependent groups designs and eliminates the problem of carryover effects. Provides the added power of dependent groups designs and eliminates the problem of carryover effects. Matching variables – participants are matched, or made equivalent, on variables that correlate with the DV. Matching variables – participants are matched, or made equivalent, on variables that correlate with the DV. By using appropriate matching variables the error variance due to individual differences is reduced and power is increased. By using appropriate matching variables the error variance due to individual differences is reduced and power is increased.