Psychology Chapter 8-9: Complex Design and Single Subject Design

Complex Design (Factorial Design)

• A factorial design involves two or more independent variables (IVs).
• To calculate the total number of conditions, multiply the number of levels in each variable (A x B).
• There are two types of effects: main effect and interaction effect.
• Main effect refers to the effect of one IV on the dependent variable, while interaction effect refers to the effect of the combination of IVs on the dependent variable.

Understanding Main and Interaction Effects

• Main effect and interaction effect can be understood conceptually, through application, and by recognizing them from graphs.

Ceiling and Floor Effects

• Ceiling effect occurs when the dependent variable reaches a maximum value, and further increases in the IV do not cause any change.
• Floor effect occurs when the dependent variable reaches a minimum value, and further decreases in the IV do not cause any change.

Mixed Design

• A mixed design involves a combination of between-subjects and within-subjects designs.

Single Subject Design

• Single subject design involves only one participant, and is not a true scientific design due to lack of control and random assignment to conditions.
• Single subject design is used in clinical interventions to explore the best strategy to help patients.

Types of Single Subject Design

• ABAB reversal design involves alternating between baseline and treatment conditions.
• Multiple baseline design involves measuring the dependent variable across multiple participants or situations.
• Random time-series study involves measuring the dependent variable at random intervals.

Quasi-Experiment

• Quasi-experiments are conducted when it is not possible to conduct a true experiment due to ethical or realistic issues.
• Different quasi-experiment designs include non-equivalent control group design, simple interrupted time series design, and time series with control group design.

Threats to Internal Validity

• Threats to internal validity include history, maturation, testing, instrumentation, regression, selection, subject attrition, and additive effect with selection.

Inferential Statistics

• Inferential statistics involves making inferences about a population based on a sample.

Z Score

• Z score is a measure of how many standard deviations an observation is from the mean.
• Z score can be calculated using the formula: z = (X - μ) / σ.
• Z score can be interpreted by comparing it to the standard normal distribution.
• Z score has applications in hypothesis testing and confidence intervals.

Normal Curve

• The normal curve is a bell-shaped distribution that represents the probability of different values of a variable.
• The normal curve corresponds to z scores, with the mean at z = 0 and the standard deviation at z = 1.

Probability

• Probability is a measure of the likelihood of an event occurring.
• Probability can be calculated using the formula: P(A) = Number of favorable outcomes / Total number of outcomes.
• Probability corresponds to the normal curve and z scores, with the probability of an event occurring equal to the area under the curve.

Hypothesis Testing

• Hypothesis testing involves testing a null hypothesis against a research hypothesis.
• Statistical decisions involve rejecting or failing to reject the null hypothesis.
• Alpha (α) is the probability of rejecting the null hypothesis when it is true.

Complex Design (Factorial Design)

• Involves two or more independent variables (IVs)
• To calculate the total number of conditions: multiply the number of levels in each variable (A x B)

Key Concepts

• Main effect: the effect of one IV on the dependent variable
• Interaction effect: the effect of the combination of two or more IVs on the dependent variable

Understanding Complex Designs

• Conceptual understanding: recognizing and explaining the concepts of main effect and interaction effect
• Application: applying complex design concepts to real-world scenarios
• Recognizing from graphs: identifying main effects and interaction effects from graphical representations

Ceiling and Floor Effects

• Ceiling effect: when the maximum possible score is reached, limiting further improvement
• Floor effect: when the minimum possible score is reached, limiting further deterioration

Mixed Design

• A combination of between-subjects and within-subjects designs