Complex Research Designs in Psychology

Complex Research Designs

• Psychologists often observe the effects of an independent variable (IV) on multiple dependent variables (DVs).
• This can lead to carryover effects from testing participants through multiple DVs.
• Manipulation checks are necessary when constructs are manipulated indirectly (e.g., emotions, internal states).
• Researchers need to ascertain if their manipulation was effective.
• Multiple measures of the same construct are used in some studies. This strategy employs self-report along with physiological measurements for examining the effects, e.g., exercise on stress.
• In multi-response measures of stress, each separate item on a rating scale can be treated as a separate DV, but it's better to use a multi-response measure of the construct, (e.g., stress).
• Assess internal consistency using Cronbach's alpha to ensure test items correlate with each other.

Multiple Independent Variables

• Factorial design enables researchers to investigate whether the effect of one independent variable depends on the level of another.

• An independent variable is also known as a factor.

• To understand the interaction between factors, examine how the different levels of each factor depend on each level of the other factor (i.e., consider all possible combinations).

• Factorial designs can have multiple independent variables (e.g., two, three, or more).

• A 2 x 2 design means two independent variables, each with two levels, resulting in four experimental conditions.

• Similarly, a 2 x 3 design involves two independent variables, with one having two levels and the other three. This creates six conditions.

• Examples include treatment (e.g., behavior modification, psychotherapy), dosage (e.g., 100 mg, 300 mg), and treatment setting (e.g., inpatient, day treatment, outpatient).

• It's unusual to have designs with more than three factors, each with more than two or three levels. These designs can swiftly become unwieldy.

Assigning Participants to Conditions

• Researchers must choose whether to treat factors as between-subjects or within-subjects variables.
• For between-subjects variables, participants are assigned to only one condition of an independent variable.
• For within-subjects variables, participants are assigned to every condition or level of an independent variable.
• Mixed factorial designs are possible where researchers use both between-subjects and within-subjects variables.

Non-manipulated Independent Variables

• Factors that aren't manipulated but are instead measured can be treated as between-subjects factors.
• But note that no causal conclusions can necessarily be drawn from non-manipulated factors (versus manipulated ones).
• An example involves a study examining memory for health-related words and non-health related words, with participants divided based on self-reported levels of hypochondriasis.
• Non-manipulated independent variables can play an important but not causal or experimental role.

Graphing Results of Factorial Experiments

• Two key results to analyze in factorial designs are main effects and interactions.
• A main effect represents the influence of one independent variable averaged across all levels of the other independent variables.
• An interaction happens when the effect of one factor depends on the level of another.

Correlational Studies with Factorial Designs

• Factorial designs can be used in correlational studies to understand relationships among variables.
• Researchers might use variables like mood and self-esteem as independent variables to understand their relationship with something like the willingness to have unprotected sex (the dependent variable).

Assessing Relationships Among Multiple Variables

• Analyzing the associations among multiple variables, both categorical and quantitative, is a key aspect of research.
• Factor analysis is a technique that creates clusters or groups of variables based on their shared relationships. This is used to reduce many variables into underlying factors.

Examples of Psychological Measurements

• Examples of measurement factors include mathematical intelligence, verbal intelligence, and big five factors.

This quiz explores complex research designs used in psychology, focusing on the interaction of independent and dependent variables. Learn about manipulation checks, multi-response measures, and the significance of internal consistency using Cronbach's alpha. Ideal for understanding advanced research methodologies.