Interaction Effects and Theory Testing Kaiser et al. (2006) social identity theory –tested hypotheses about attention to prejudice cues in the environment –From prior research when individuals’ social identity is threatened they are consciously aware of cues in their environment relating to potential prejudice –extend this research –Do threatened individuals pay attention to prejudice cues nonconsciously, without awareness?

Interaction Effects and External Validity Interaction effect is not present –Generalize findings across conditions of experiment Interaction effect is present –Sets limits on generalizing a finding –Conditions of experiment identify the limits

Interaction Effects and External Validity The presence of a statistically significant interaction effect sets limits on the external validity of a finding. For example, in the Kassin et al. (2003) study –Interaction of interrogator expectation and suspect status –Combination of Guilty level of interrogator expectation actual innocence level of the suspect status produces the highest score on effort to obtain confessions So we cannot generalize findings for interrogator expectation –It has an influence when there is actual innocence –It does not when there is actual guilt

Interaction Effects and External Validity Because Kassin et al. (2003) observed a statistically significant interaction effect for Interrogator expectations and Suspect Status Interrogator expectations depends on the suspect status of an individual There are limits to external validity

Interaction Effects and External Validity When an interaction effect is not statistically significant in an experiment, the researcher can generalize the findings across the conditions of the experiment. To understand this, we can look at Kassin et al. (2003) Number of Guilt-Presumptive Questions variable Main effect of interrogator expectation with guilty greater then innocent

Interaction Effects and External Validity More guilt-presumptive questions for guilty compared to innocent This was true for actual guilt and actual innocent therefore, the effect of suspect status generalized across the levels of interrogator expectations

Interaction Effects and External Validity Floor and Ceiling Effects –Sometimes an interaction effect can be statistically significant “by mistake.” –This occurs when: the means for one or more condition reach the highest possible score (ceiling effect) the lowest possible score (floor effect). –When floor or ceiling effects occur, an interaction effect is uninterpretable.

Ceiling Effect: Example This graphs shows an interaction effect between Test Difficulty (easy, hard) and Study Hours (10, 15). Hours of study had an effect only in the hard-test condition, not in the easy- test condition. How do we interpret this interaction effect when we know the highest possible score on the tests is 50?

Ceiling Effect If we have enough “room” in our dependent variable to assess the effect of the independent variables, the interaction effect disappears. This graph shows two main effects: A main effect of Study Hours and a main effect of Test Difficulty.

Interaction Effects and Natural Groups Designs Using complex designs, researchers can test causal inferences for natural groups variables. Recall that we can’t make causal inferences with natural groups variables. –Natural groups variables are correlational. So, how can we make causal inferences using a complex design?

Interaction Effects and Natural Groups Designs Steps for making causal inferences about natural groups variables in a complex design: –State your theory. Why are the groups different? What is the theoretical process? –Identify a relevant independent variable. This IV should influence the likelihood that the theorized process will occur (e.g., relationship maintenance). –Look for an interaction effect. In order to make a causal inference, the natural groups variable and manipulated variable should produce a statistically significant interaction effect in the predicted direction. This interaction effect allows us to make causal inferences about why individuals differ — that is, we begin to understand why people differ.

Interaction Effects and Natural Groups Designs We can make causal inferences about natural groups when we test a theory for why the natural groups differ. –For example, we can theorize that musicians and nonmusicians different in musical performance because of the way that these groups organize melodies –By manipulating type of musical structure –Test for interaction

Single-Case Research Single-case research is idiographic rather than nomothetic. These designs are often used in clinical psychology and neuropsychology. The two major types of single-case research designs are: –case study, and –single-subject experimental designs

Case Study Method Case study: An intensive description and analysis of a single individual. –For example: Genie “feral child” –Data: clinical observations, self-report, archival data (e.g., medical records) –Case studies typically report the results of a treatment. –Use qualitative data –Major problem: Lack scientific control simultaneous treatments, extraneous variables

Case Study Method Advantages of the Case Study Method: –rich source of ideas for developing hypotheses, –opportunity for clinical innovation, –method for studying rare events, –possible challenge to theoretical assumptions, –tentative support for a psychological theory, and –complement to the nomothetic study of behavior

Case Study Method Disadvantages of the Case Study Method: –difficulty drawing cause-and-effect conclusions (limited internal validity), –possible biases when interpreting outcomes due to observer bias and biases in data collection (e.g., due to poor memory), and –problem of generalizing findings from a single individual (limited external validity)

Case Study Method Case studies provide great anecdotal evidence and “testimonials.” Case studies that appear in the popular press are rarely scientific. People want to believe that the treatment in these testimonials will work for them, but often they do not. It’s better to pay attention to the results of single- subject experiments.

Single-Subject(small-n) Experimental Designs Single-subject experimental designs have their roots in B. F. Skinner’s approach called applied behavioral analysis. Single-subject designs improve on case studies, because the researcher attempts to gain more scientific control.

B. F. Skinner’s approach called applied behavioral analysis.

FIGURE 9.2 Applied behavior analysis is used to investigate methods of controlling maladaptive behavior of children and adults.