Introduction to Complex Designs Complex Design has two or more independent variables in the same experiment The simplest complex design; 2X2 two independent variables (IVs) And each independent variable has two levels (conditions) So there are four conditions in a 2X2 design Factorial Combination combine independent variables in an experiment to describe their effects on the dependent variable(s). How many independent variables and conditions in a 2X2X2 design?

Kassin (2003) Research Example Kassin, Goldstein, and Savitsky (2003) pp. 246-250 in SZ&Z textbook Research questions Do interrogators’ expectations about a suspect’s guilt or innocence influence the interrogation tactics they use? Do interrogators have a confirmation bias in which their initial beliefs about a suspect’s guilt cause them to interrogate more aggressively?

Kassin (2003) Research Example Research design 2 x 2 complex design IV-1: Interrogator Expectation (independent groups) Guilty expectation Innocent expectation IV-2: Suspect Status (independent groups) Actual guilt Actual innocence Students participated as interrogators or suspects in a laboratory “mock crime.”

Kassin (2003) Research Example Dependent Variables (there were many) Number of guilt-presumptive questions the interrogator selects for the interview with suspect Number of persuasive interrogation techniques used during the interview with the suspect Ratings of the amount of effort the interrogator used to obtain a confession

Complex Designs Main effect is the effect of an independent variable effect of the independent variable on the dependent variable as if only that variable was manipulated in the experiment. Interaction effect is the combined effect of independent variables effect of one of the independent variables differs depending on the level of the second independent variable.

Complex Designs Kassin (2003) Example This factorial design is called a 2 x 2 design It has four conditions Actual Guilt/ Guilty Expectation Actual Guilt/ Innocent Expectation Actual Innocence/ Guilty Expectation Actual Innocence/ Innocent Expectation In one experiment can study Main effect of Interrogator Expectation Main effect of Suspect Status Interaction of Interrogator Expectation and Suspect Status

Kassin (2003) Research Example Factorial combination of 2 x 2 design Interrogator Expectation Guilty Innocent Suspect Status Actual Guilt Interrogators believed suspect was guilty and suspect actually committed the crime Interrogators believed suspect was innocent and suspect actually committed the crime Actual Innocence Interrogators believed suspect was guilty and suspect did not commit the crime Interrogators believed suspect was innocent and suspect did not commit the crime

Kassin (2003) Research Example Kassin et al.’s (2003) findings “results” Main effects A main effect is the effect of one IV, ignoring (or collapsing across) the effect of the other IV Two main effects are possible Interrogator Expectation Suspect Status

Kassin (2003) Findings MAIN EFFECTS main effect of the Interrogator Expectation compare the two levels of Interrogator Expectation: Guilty Innocent main effect of the Suspect Status compare the two levels of Suspect Status: Actual guilt Actual Innocence

Main Effects (see table 8.1) Number of Guilt-Presumptive Questions Interrogator Expectation Guilty (n = 51) Innocent (n = 47) Suspect Status Actual Guilt (n = 50) 3.54 (n =26) 2.54 (n =24) Actual Innocence (n =48) 3.70 (n =25) 2.66 (n =23) Means for Suspect Status 3.04 3.18 Means for Interrogator Expectation: 3.62 2.60

Main Effects for Number of Guilt-Presumptive Questions Interrogator Expectation Guilt (3.62) compared to Innocent (2.60) This main effect of Interrogator Expectation maybe statistically significant Suspect Status Actual Guilt (3.04) compared to Actual Innocence (3.18) This main effect of Suspect Status probably not statistically significant Even though Suspect Status did not produce a main effect it may have an influence need to look at interaction effects before drawing conclusions Did Suspect Status influence ratings by interacting with Interrogator Expectation ?

Main Effects (see table 8.2) Number of Persuasive Techniques Interrogator Expectation Guilty (n = 51) Innocent (n = 47) Means for Suspect Status Suspect Status Actual Guilt (n = 50) 7.71 (n =26) 6.59 (n =24) 7.15 Actual Innocence (n =48) 11.96 (n =25) 10.88 (n =23) 11.42 Means for Interrogator Expectation: 9.83 8.73

Main Effects for Number of Persuasive Techniques Interrogator Expectation Guilt (9.83) compared to Innocent (8.73) This main effect of Interrogator Expectation is probably not statistically significant Suspect Status Actual Guilt (7.15) compared to Actual Innocence (11.42) This main effect of Suspect Status maybe statistically significant Even though Interrogator Expectation did not produce a main effect it may have an influence need to look at interaction effects before drawing conclusions Did Interrogator Expectation influence ratings by interacting with Suspect Status?

Interaction Effects Independent variables work together to influence behavior. When the effect of one independent variable differs depending on the level of the second independent variable. To look for an interaction effect look at the effect of Suspect Status at each level of the Interrogator Expectation independent variable. When we look for interaction effects between independent variables, we often use the subtraction method.

Interrogator Expectation Difference Between Means Interaction Effects (see table 8.3) Caution the marginal numbers in this table are mean difference because we are looking for interaction effects. Effort to Obtain a Confession Interrogator Expectation Guilty (n = 51) Innocent (n = 47) Difference Between Means Suspect Status Actual Guilt (n = 50) 5.64 (n =26) 5.56 (n =24) 0.08 Actual Innocence (n =48) 7.17 (n =25) 5.85 (n =23) 1.32 Difference Between Means -1.53 -0.29

Interaction Effects The subtraction method can be used to examine the effect of the Interrogator Expectation independent variable at each level of the Suspect Status independent variable: For Actual Guilt, the difference between means for the two scenario conditions was 0.08 For Actual Innocence, the difference between means for the two scenario conditions was 1.32 Because the outcome of the subtraction method yielded very different values (0.08 and 1.32), an interaction effect between the independent variables is likely: but a test of statistical significance would be needed to confirm this.

Interaction Effects Another way to say this is that the effect of one independent variable, Suspect Status, differed depending on the level of the second independent variable, Interrogator Expectation. Recall that this is our definition of an interaction effect.

Interaction Effects (continued) Graphs (“Figures”) can be used to detect interaction effects easily. An interaction effect is likely present in a complex design experiment when the lines in a graph that display the means: are not parallel; that is, the lines either intersect, converge, or diverge.

Stretching Exercise II

Stretching Exercise II

Complex Design with Three Independent Variables Pingitore (1994) study on discrimination in job interviews because of bodyweight IV-1: Weight of the applicant as seen in a video tape (note: applicants are actors) Normal Overweight IV-2: Sex of the applicant as seen in a video tape (note: applicants are actors) Female Male IV-3: Participant’s self report of concern for their own body weight Low Body-Schema High Body-Schema

Complex Design with Three Independent Variables Three Main Effects Main effect of Weight of the applicant Main effect of Sex of the applicant Main effect of Body-Schema Interactions 2Way: Weight of the applicant with Sex of the applicant 2Way: Weight of the applicant with Body-Schema 2Way: Sex of the applicant with Body-Schema 3Way: Weight of the applicant with Sex of the applicant with Body-Schema