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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 4/16/09 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. Example of main effect test; real data Number of major stressful events (Ages 21 to 26) • 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 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 A. 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 B. Testing hypotheses re: 2 or more I.V.s 1. test separate, ‘main effects’ of each I.V. (Do each of these variables significantly affect the outcome?) 2. test ‘additive’ effects of > 1 I.V.s simultaneously (What is the combined effect of these variables?) 3. test interaction of 2 or more I.V.s (Does the effect of one I.V. on the outcome depend upon a second variable...?) Multiple independent variables

  7. 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

  8. Example: factorial designtesting 2 IVs Hypothesis: Coping skills delivered by a peer help diabetics maintain blood sugar. Independent Variables: Values: None(Placebo / control grp) High(experimental group) Skills training Nurse (“Standard of care”) Peer(experimental group) Trainer Multiple independent variables

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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

  21. 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

  22. 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

  23. 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

  24. 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

  25. 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

  26. 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

  27. 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

  28. 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

  29. Design: 1st Dependent Variable Participant variables Questionnaires Alcohol: Yes No Subjective sense of stimulation Gender: Male Female Subjective sense of sedation First Dependent Variable Repeated measure: each person gets both questionnaires First 2 Independent Variables 1 measured, 1 manipulated Multiple independent variables

  30. Design: 2nd Dependent Variable 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 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

  31. Factorial design details 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. ½ the 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

  32. 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

  33. 1st DV Figure 3Mean ratings of subjective stimulation and sedation on the BAES under 0.65 g/kg alcohol and placebo in women and men. Alcohol (v. placebo) made men more stimulated. Alcohol made women more sedated Multiple independent variables

  34. 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

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

  36. Alcohol & gender results 1 Dependent variable 2:“disinhibition” of button press 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. Alcohol “disinhbition” is much stronger for men than for women But only with a “go” prime. …not for the “no-go” prime. Multiple independent variables

  37. 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 from acting). …primarily among men (v. women) How much external validity does this finding have? Multiple independent variables

  38. 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

  39. 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

  40. Interaction example, 2 [See readings: summary, actual article.] 5 levels of stress. 3 levels of genetic disposition. 4 outcome measures (DVs). Multiple independent variables

  41. Interaction example, 3 [See readings: summary, actual article.] More stress = more depression on all measures Multiple independent variables

  42. Interaction example, 4 [See readings: summary, actual article.] But primarily among genetically vulnerable people Multiple independent variables

  43. Stress leads to depression only for those who are genetically vulnerable. This is illustrated by different stress  depression effects for two key genotypes. Low vulnerability High vulnerability Multiple independent variables

  44. Interaction example, 6 [See readings: summary, actual article.] Interaction is very strong in an analysis of childhood trauma and depression. People with no genetic vulnerability: childhood trauma has no effect on depression People with increasing genetic vulnerability: More trauma  greater likelihood of depression. Multiple independent variables

  45. Multiple IVs; summary 1 # of major stressful events (Ages 21 to 26) Multiple Independent Variables / Predictors tell us much more than simple main effects. Main effect: • Test how one IV in isolation affects the DV Multiple independent variables

  46. Multiple IVs; summary 1 Multiple Independent Variables / Predictors tell us much more than simple main effects. Main effect: One IV  one DV Additive effects: • Two IVs each have a main effect • One combination has a particularly strong effect on the DV Multiple independent variables

  47. Multiple IVs; summary 1 Multiple Independent Variables / Predictors tell us much more than simple main effects. Main effect: One IV  one DV Additive effects: Some variables may combine with others to produce very strong effects Interaction effects: • One IV has a different effect on the DV depending upon another IV: Emotional arousal The effect of alcohol on emotional arousal depends upon gender. IV 1: Emotional arousal IV 2: Gender Interaction: • Stimulation goes up if you are male. • Sedation goes up if you are female. Multiple independent variables

  48. Multiple IVs; summary 1 Multiple Independent Variables / Predictors tell us much more than simple main effects. Increasing vulnerability: More trauma  greater likelihood of depression. Main effect: One IV  one DV Additive effects: Some variables may combine with others to produce very strong effects Interaction effects: • One IV has a different effect on the DV depending upon another IV: • One IV has an effect only at one level of a 2nd IV: No genetic vulnerability: childhood trauma has no effect on depression The effect of childhood abuse on risk for depression depends upon a genetic disposition. IV 1: Level of trauma • IV 2: 3 forms of 5-HTT genotype Multiple independent variables

  49. Multiple IVs; summary 2 Multiple Independent Variables / Predictors: • Are critical to theory development and testing: Changing sexual risk reduction requires that we understand both peoples’ psychological dispositions and their drug use patterns. Stress or other environmental events can “switch on” genes that create psychological or other problems; genetic dispositions and environment are not separate processes. • Establish “boundary conditions” to a theory: when and among whom does a basic psychological process operate? Alcohol makes it more difficult to inhibit behavior, but primarily among men. Multiple independent variables

  50. Summary • Key terms: • Main effect • Additive effect • Interaction • Cross-over interaction • Factorial design • Repeated measure Multiple independent variables

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