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Psychology 242, Dr. McKirnan

Psychology 242, Dr. McKirnan. Right click for “full Screen” or “end show”. Left click to proceed, . Multiple independent variables. 4/14/09. Testing hypotheses about > 1 independent variable Factorial Designs: Main effects, Additive Effects, Interactions

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Psychology 242, Dr. McKirnan

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  1. Psychology 242, Dr. McKirnan Right click for “full Screen” or “end show”. Left click to proceed, Multiple independent variables 4/14/09 • Testing hypotheses about > 1 independent variable • Factorial Designs: Main effects, Additive Effects, Interactions • Examples of complex experiments • The interaction of drug use & attitudes on sex risk among gay men • 3 Independent variables: alcohol and behavioral disinhibition • The interaction of “nature” and “nurture”: Genetics & stress and depression 4/11/14 Multiple independent variables

  2. Multiple independent variables  • Testing hypotheses about > 1 independent variable • Factorial Designs: Main effects, Additive Effects, Interactions • Examples of complex experiments • The interaction of drug use & attitudes on sex risk among gay men • 3 Independent variables: alcohol and behavioral disinhibition • The interaction of “nature” and “nurture”: Genetics & stress and depression Multiple independent variables

  3. Main effects Your paper tested a single Main effect: • Single Independent Variable [IV] • Experimental group v. control group • Placebo v. Dose 1 v. Dose 2, …etc. • Simple group contrast • Male v. female • School 1 v. school 2, …etc. • Experimental groups • [true experiments] • Naturally occurring groups • [quasi-experiments] • This tests a relatively simple theory: • Links one hypothetical construct to one outcome • Arousal  performance • Gender  sex-role attitudes • Assumes the main effect is independent of other key variables Multiple independent variables

  4. Number of major stressful events (Ages 21 to 26) Example of a main effect • Do stressful life events lead to more depression? • Men were sorted into 5 groups, corresponding to # major life stressors they experienced from age 21 to 26. • At age 26 men in groups 3 & 4 were significantly more likely to have lifetime major depression episode than groups 0  2 E X A M P L E Looking only at stress as an Independent Variable, there is a main effect of stress on depression. Partial data from Avshalom C., (2003), Science Magazine. Multiple independent variables

  5. Multiple variables in psychological research Multiple Independent variables allow us to test more complex theories / hypotheses: • Link > 1 hypothetical construct to an outcome. • Arousal and gender  performance • Drugs and expectations  sexual risk • Test if an effect depends upon other key variable(s). • Variable 1 affects the outcome only at one level of variable 2 • Variable 1 has a different effect on the outcome at different levels of variable 2. Multiple independent variables

  6. Designs with > 1 independent variable Two uses of multiple Independent Variables B. Testing hypotheses about2 or more I.V.s 1. Separate, ‘main effects’ of each I.V. (Do each of these variables significantly affect the outcome?) 2. ‘Additive’ effects of > 1 I.V.s simultaneously (What is the combined effect of these variables?) 3. Interactionof 2 or more I.V.s (Does the effect of one I.V. on the outcome depend upon a second variable...?) • Including a ‘control’ variable as an I.V. • E.g., gender, age, race, etc. • Test if the I.V. has the same effect within both groups Multiple independent variables

  7. A. Including a ‘control’ variable as an I.V. • Block the data by gender, age, race, attitudes, etc. • Test if the main Independent Variable has the same effect within both groups • What is the effect of self-reflection on stress reduction? • Hypothesis: Self-reflection training lessens exam stress. • 2nd Question: Is that effect the same in women and men? [Or, old vs. young, high vs. low in exam fear & loathing…] • Main effect: Self-reflection training  less stress • Interaction: [ Training  stress ] works for women, not men. • Conclusion: • Men and women differ in responses to stress reduction • Including a ‘control’ variable allowed us to see these results. E X A M P L E

  8. B. Testing hypotheses about 2 or more I.V.s Examining both trauma and genetic vulnerability allows us to better understand the onset of depression. There is a general (main) effect whereby more trauma leads to greater likelihood of adult depression E X A M P L E

  9. B. Testing hypotheses about 2 or more I.V.s However … the effect of trauma interacts with genetics Childhood trauma has no effect in people who have no genetic vulnerability. E X A M P L E With increasing genetic vulnerability, increasing trauma increases the likelihood of depression This constitutes a more complex theory of depression… …depression results from the interaction of several variables.

  10. Multiple independent variables • Testing hypotheses about > 1 independent variable • Factorial Designs: Main effects, Additive Effects, Interactions • Examples of complex experiments • The interaction of drug use & attitudes on sex risk among gay men • 3 Independent variables: alcohol and behavioral disinhibition • The interaction of “nature” and “nurture”: Genetics & stress and depression  Multiple independent variables

  11. Example: Factorial design testing 2 IVs Hypothesis: Coping skills delivered by a peer helps diabetics maintain blood sugar. Independent Variables: Values: None(Placebo / Distraction grp) High(experimental group) Skills training Nurse (“Standard of care”) Peer(experimental group) Trainer Multiple independent variables

  12. Example of a factorial design for testing 2 IVs: • The hypothesis rests on the interaction of two variables • More complex theory of skills training. Independent Variable 1 Experimental v. control groups IV #2 Training condition DV = M glucose control DV = M glucose control DV = M glucose control DV = M glucose control Dependent Variable: Glucose control(assessed for every combination of IV1 and IV2) Multiple independent variables

  13. Basic factorial design Independent variable 1 Each “cell” of the design represents both IVs: Independent variable 2 M M M M • peer, no skills • peer, skills • Nurse, no skills • Nurse, skills Data for each combination of conditions Multiple independent variables

  14. Basic factorial design data table: 2 I.V.s Independent variable 1 • This is a 2 x 2 factorial design: • 4 data cells • each with a M value for the D.V. Independent variable 2 The “Marginals” show overall Ms for each I.V.:  Skills v. no skills (a.k.a. main effect for skills…)  Peer v. nurse trainers (trainer main effect…) Contrasts among individual cells show any interaction effects. M M M M • no skills • skills • Peer trainer • Nurse trainer Multiple independent variables

  15. Basic factorial design data table: 2 I.V.s Independent variable 1 • This is a 2 x 2 factorial design: • 4 data cells • each with a M value for the D.V. Independent variable 2 The “Marginals” show overall Ms for each I.V.:  Skills v. no skills (a.k.a. main effect for skills…)  Peer v. nurse trainers (trainer main effect…) Contrasts among individual cells show any interaction effects. M M M M • no skills • skills • Peer trainer • Nurse trainer Multiple independent variables

  16. Testing Main Effects (hypothetical data) Example of (made up) data showing a main effect • Glucose control is enhanced by skills training • Change is the same for both training groups. Multiple independent variables

  17. Alternate display (hypothetical data) An alternate display of the same main effect data Skills training helps, by about the same amount no matter who it is delivered by… Multiple independent variables

  18. Two (additive) Main Effects These marginals show a main effect of trainer(Peers do better than nurses). And of skills training: Getting skills training helps about the same in both groups …but contact with a peer is generally more helpful (hypothetical data) Multiple independent variables

  19. Additive Main Effects (hypothetical data) Putting these effects together shows a very high value for patients who get skills training by a peer…  Multiple independent variables

  20. Additive main effects: alternate display Alternate display of additive effect of two variables General effect of trainer: peers do better than nurses no matter what the intervention… …AND, getting skills helps, whether they are delivered by a nurse or a peer… Multiple independent variables

  21. Additive main effects: alternate display, 2 Alternate display of additive effect of two variables These two effects “add up”: Skills delivered by a Peer have the best results. Multiple independent variables

  22. Interaction Effects (hypothetical data) Example of a (made up)interaction of trainer by skills condition • Skills training made a difference • But only among patients trained by a peer. • For patients trained by a Nurse, training had little effect Multiple independent variables

  23. Alternate Display (hypothetical data) Alternate display: interaction between two variables Large effect of training versus distraction, …but only for the peer trainer Overall M for skills training not the nurse Overall M for distraction (placebo) Interaction: the effect of the 1st Independent Variable (training) depends upon the 2nd IV; peer v. nurse. Multiple independent variables

  24. Click – 1 What is a main effect? • The simple effect • of one I.V. on the D.V. • One IV combines with another IV to produce an effect on the DV. • One IV has an effect on the DV only at one level of another IV. • An IV has a different effect on the DV at different levels of a second IV. Multiple independent variables

  25. Click – 2 What is an additive effect? • The simple effect • of one I.V. on the D.V. • One IV combines with another IV to produce an effect on the DV. • One IV has an effect on the DV only at one level of another IV. • An IV has a different effect on the DV at different levels of a second IV. Multiple independent variables

  26. Click – 3 What is an interaction? • The simple effect • of one I.V. on the D.V. • One IV combines with another IV to produce an effect on the DV. • One IV has an effect on the DV only at one level of another IV. • An IV has a different effect on the DV at different levels of a second IV. Multiple independent variables

  27. Multiple independent variables • Testing hypotheses about > 1 independent variable • Factorial Designs: Main effects, Additive Effects, Interactions • Examples of complex experiments • The interaction of drug use & attitudes on sex risk among gay men • 3 Independent variables: alcohol and behavioral disinhibition • The interaction of “nature” and “nurture”: Genetics & stress and depression  Multiple independent variables

  28. Example of interaction effect; AIM study Sexual Risk among gay & bisexual men who combine alcohol and drugs with sex. • People who use drugs during sex are more likely to have unsafe (as well as more) sex. • What causes that… • The drugs themselves (“disinhibition”) • Some characteristics of people who use them? • Project: Awareness Intervention for Men study of interventions for unsafe sex among MSM who use drugs. Multiple independent variables

  29. Example of interaction effect; AIM study, 2 McKirnan, D.J, Vanable, P., Ostrow, D., & Hope, B. (2001). Expectancies of sexual “escape” and sexual risk among drug and alcohol-Involved gay and bisexual men. Journal of Substance Abuse, 13, 137-154. Paper here. Sexual Risk… Two “main effect” hypotheses: • Drug use: More drug use & problems  more sexual risk. • Attitudes: Using drugs to “escape” from having to think about risk  more drugs + risky sex. Interaction hypothesis: • Drugs make people more risky, but primarily if they also have “high risk” (“escape”) attitudes. Implications for theory: Interventions should focus on attitudes & expectations as well as simple drug use. Multiple independent variables

  30. Example of interaction effect; AIM study, 3 Sexual Risk… Main effects: Ignoring drug use, higher escape motive  more risk. Ignoring motives, higher drug use more risk. Multiple independent variables

  31. Interaction of expectations x drug pattern Sexual Risk… Overall / Interaction finding: Drug users with strong escape attitudes were very risky. Drug users without escape attitudes were less risky.  For men who did not use drugs, attitudes did not affect risk.    Drugs & attitudes interact: Drugs lead to risk primarily in the “escape” group. Multiple independent variables

  32. Alternate display of interaction effect Drug users: risk is high, but only for those with strong expectancies No drug use: risk stays low for all participants Multiple independent variables

  33. Multiple independent variables • Testing hypotheses about > 1 independent variable • Factorial Designs: Main effects, Additive Effects, Interactions • Examples of complex experiments • The interaction of drug use & attitudes on sex risk among gay men • 3 Independent variables: alcohol and behavioral disinhibition • The interaction of “nature” and “nurture”: Genetics & stress and depression  Fillmore, M. T., & Weafer, J. (2004). Alcohol impairment of behavior in men and women. Addiction, 99 (10), 1237-1246. Article here. Multiple independent variables

  34. Example: 3 independent variables Core Hypotheses: Men are less able to inhibit behavior in response to alcohol than are women. Men get more aroused by alcohol, women get less aroused Theories: Social learning: Men are socialized to lessen behavioral control in alcohol-related situations, women socialized to increase caution. Bio-behavioral: Basic inhibitory mechanisms in men are more reactive to alcohol and other drugs than in women Operational Definition of “Behavioral Inhibition”: • Participants are given a “go” prime + a “don’t press” stimulus • Can they inhibit pressing the button? Operational Definition of “Arousal”: Two standard questionnaires: subjective stimulation & sedation. Multiple independent variables

  35. Experimental Design Provide alcohol v. placebo beverages to men v. women. (first 2 independent variables) Provide 2 questionnaires: • Subjective arousal / stimulation • Subjective sedation • (First Dependent variable  represents a repeated measure) Conduct a simple reaction time task … Participants told to: • press a button quickly in response to a “go” stimulus, • do not press with a “no go” stimulus. • (Second Dependent variable) Participants are first a. primed to expect a “go” stimulus (“go” prime) b. primed to expect a “no-go” stimulus. • (3rd Independent variable  also a repeated measure) Multiple independent variables

  36. Cues & stimuli: alcohol disinhibition Prime Stimulus Told there is a 80% likelihood of a “No-Go” stimulus Behavioral inhibition condition Go Told 80% like-lihood of a “Go” stimulus No-Go Multiple independent variables

  37. Design: 1st Dependent Variable: Arousal Level Participant variables Questionnaires Alcohol: Yes No Subjective sense of stimulation Gender: Male Female Subjective sense of sedation Third Independent Variable Repeated measure: Stimulation v. sedation First 2 Independent Variables 1 measured, 1 manipulated Multiple independent variables

  38. Actual target stimulus Dependent Variable: Button press? Participant priming Participant variables “go” no Alcohol: Yes No Expect “go” stimulus “no-go” yes Gender: Male Female Expect “no-go” stimulus “go” no “no-go” yes Design: 2nd DV: Impulse Control First 2 I.V.s Third I.V. Repeated measure: each person gets both conditions Common procedure Assess “no-go” condition only Pressing in the “no-go” condition = disinhibition Multiple independent variables

  39. Design: 1st Dependent Variable: Arousal Level Full factorial design with a repeated measure: IVs: Gender Alcohol Consumption Type of arousal 2 x 2 x 2 = 8 cells, using 4 sets of participants. (repeated measure) ½ participants are men, ½ women ½ get alcohol, ½ get placebo ¼ of participants in each cell ¼ Each participant gets both questionnaires ¼ ¼ Multiple independent variables

  40. Design: 2nd DV: Impulse Control Full factorial design with a repeated measure: IVs: Gender Alcohol Consumption Priming condition 2 x 2 x 2 = 8 cells, using 4 sets of participants. (repeated measure) ½ participants are men, ½ women (repeated measure) (repeated measure) ¼ ¼ of participants in each major cell ½ get alcohol, ½ get placebo (repeated measure) (repeated measure) ¼ ¼ Each participant gets two conditions Multiple independent variables

  41. First Dependent Variable • Hypotheses: • Alcohol leads to significant mood changes v. a placebo beverage • stimulation & arousal • Sedation • Mood changes vary according to participant gender • Men  stimulation • Women  sedation • Statistical test: 3-way interaction… Alcohol v. Placebo Male v. Female Stimulation v. Sedation X X Multiple independent variables

  42. 1st DV: 2-way interaction for stimulation There was a 2-way interaction of gender (male v. female) by alcohol level (alcohol v. placebo) on stimulation. Alcoholled to more stimulation than did the placebo… But primarily among men rather than women Alcohol use (IV # 1) led to stimulation, but only at the “male” level of IV #2 (gender). Figure 3Mean ratings of subjective stimulation on the BAES under 0.65 g/kg alcohol and placebo in women and men. Multiple independent variables

  43. 1st DV: 2-way interaction for sedation There was also a 2-way interaction of gender (male v. female) by alcohol level (alcohol v. placebo) on sedation. Alcoholled to more sedation than did the placebo… But primarily among women rather than men Alcohol use (IV # 1) led to sedation, only at the “female” level of IV #2 (gender). Figure 3Mean ratings of subjective sedation on the BAES under 0.65 g/kg alcohol and placebo in women and men. Multiple independent variables

  44. 1st DV: show contrast condition There was a 3-way interaction of gender (male v. female) by alcohol level (alcohol v. placebo) byarousal (stimulation v. sedation). Alcohol (v. placebo) made menmore stimulated. Alcohol made womenmore sedated Figure 3Mean ratings of subjective stimulation and sedation on the BAES under 0.65 g/kg alcohol and placebo in women and men. Multiple independent variables

  45. Alternate portrayal of 3-way mood interaction The alcohol conditions show a classic “cross-over” effect for gender & mood; Placebo conditions do not show much effect Men get aroused M BAES subscale scores Women get sedated Multiple independent variables

  46. External validity: 3-way interaction How much external validity does this finding have? Multiple independent variables

  47. Alcohol & gender results 1 Dependent variable 2:“disinhibition” of button press Alcohol “disinhbition” is much stronger for men than for women But only with a “go” prime. …not for the “no-go” prime. Figure 1Mean proportion of failures to inhibit responses to no-go targets following go and no-go cues under 0.65 g/kg alcohol and placebo in women and men. Multiple independent variables

  48. Summary of the 3-way interaction Alcohol (vs. no alcohol) makes it difficult to inhibit behavior …when they are primed to act, (vs. when they are primed to keep fromacting). …primarily among men (v. women) How much external validity does this finding have? Multiple independent variables

  49. Multiple independent variables • Testing hypotheses about > 1 independent variable • Factorial Designs: Main effects, Additive Effects, Interactions • Examples of complex experiments • The interaction of drug use & attitudes on sex risk among gay men • 3 Independent variables: alcohol and behavioral disinhibition • The interaction of “nature” and “nurture”: Genetics & stress and depression  Multiple independent variables

  50. Interaction example, 1 Interaction of genetics & stress on depression. Overall hypothesis:stress “switches on” genes that confer vulnerability to depression Independent variables: • Variations in a gene that controls serotonin production in the brain [a measured variable]. • The number of “serious” stressful life events between ages 21 and 26 [also a measured variable] Outcome variables: • Symptom counts • Major depression episode • Suicide attempt • Others’ reports of depression Avshalom C., et al. (2003). Influence of Life Stress on Depression: Moderation by a Polymorphism in the 5-HTT Gene. SCIENCE, 301 (July 18), 386-389 [www.sciencemag.org]. [See readings: summary, actual article.] Multiple independent variables

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