Comprehensive Study Notes on Experimental Designs in Psychology
Different Types of Experimental Designs
Chapter Overview
Focuses on various experimental designs used in psychological research.
Quiz Section
A short quiz to assess understanding of experimental design concepts.
Types of Claims
Claims in Research
Frequency Claims: Describe a certain rate or level of a variable.
Association Claims: Indicate a relationship between two variables.
Causal Claims: Suggest that one variable directly affects another.
Types of Evidence Supporting Claims
Evidence Types
Observational Study or Poll: Used mainly for frequency claims.
Correlational Study: Evaluates association claims.
Quasi-experiment: Used to infer causal relationships in situations where random assignment is not possible.
Causal Claims: Supported by controlled experiments.
Experimental Designs Overview
Types of Experimental Designs
Between-subjects Design: Different groups of participants are assigned to different conditions of the experiment.
Within-subjects Design: The same participants are used across all conditions of the experiment.
Key Variables in Experiments 「実験における変数」
Types of Variables
Independent Variable (IV):
Definition: The variable that is manipulated by the experimenter.
Role: Considered the “cause” in cause-and-effect relationships.
Dependent Variable (DV):
Definition: The variable that is measured to assess the effect of the independent variable.
Role: Represents the “effect” in the relationship.
Control Variable:
Definition: Any variable that is held constant across experiments to prevent it from influencing the outcome.
Between-subjects vs. Within-subjects Designs
Design Characteristics
Levels/Conditions/Groups: Refers to the different variations of the independent variable within the design.
Condition 1 and Condition 2 examples.
Independent-groups Design Characteristics
Sample Size Example:
Total participants = 40
Distributed across conditions as illustrated (details not provided).
Within-groups Design Characteristics
Sample Size Example:
Total participants = 20
All exposed to all conditions.
Types of Between-Subjects Designs
Pretest/Posttest Designs
Definition and Purpose: Used to assess the effect of the intervention by measuring the dependent variable before and after the manipulation.
Illustrations of mindfulness and stretching effects on stress ratings.
Posttest-only Design
Conduct experiments without pretesting to immediately assess the outcomes post-treatment.
Variations in Between-Subjects Designs
Capable of incorporating more than two levels of the independent variable.
Example: Effects of stretching and mindfulness on stress ratings across different variables such as positive mood.
Within-Subjects Designs 「＞二の独立変数を持つ」Characteristics
Can incorporate more than two levels of the independent variable.
Able to measure multiple dependent variables.
Stress ratings and positive mood as potential measures.
Factorial Designs 「＞一つの独立変数を持つ」
Definition
Involves more than one independent variable.
Allows researchers to evaluate the effect of all combinations of those independent variables.
Example: Examining variables like activity type and music type across multiple conditions of stretching and mindfulness leading to different stress ratings.
Summary of Types of Experimental Designs
Design Types Overview
Posttest Only Design: Measure output only after the manipulation.
Pretest/Posttest Design: Measure output before and after manipulation (common in between-subjects).
Repeated-measures Design: The same participants are measured multiple times under different conditions.
Concurrent Measures Design: Collect data simultaneously from the same subjects under different conditions.
Assignments in Experimental Design
Group Assignment Overview
Each group will be assigned a specific experimental design to present, highlighting the type and reasoning behind the design choice.
Reflection Questions
Key differences between between-subjects and within-subjects designs.
Explanation of how pretest/posttest designs and repeated-measures designs compare, referencing independent and dependent variables as well as experimental conditions.
Evaluating Claims and Evidence
Evaluation Criteria for Claims
Focuses on validity:
Internal Validity: The degree to which a study accurately establishes a cause-and-effect relationship.
Construct Validity: The degree to which a test or tool measures the theoretical construct it is intended to measure.
External Validity: The extent to which the results of a study generalize to or have relevance for settings, people, times, and measures other than the one used in the study.
Statistical Validity: The accuracy of the conclusions that can be drawn from statistical analysis.
Review of Criteria for Causation
Causality Requirements
Covariance: Demonstrates that changes in one variable are associated with changes in another.
Temporal Precedence: Establishes that the cause precedes the effect in time.
Internal Validity: Ensures that the observed relationship is not due to confounding variables.
Threats to Internal Validity
Confounds
Definition: Factors that may falsely suggest a cause-and-effect relationship between measures.
Systematic Variability detail: Review of factors contributing to confounding.
Evaluating Systematic Variability
Potential Confounds Example: Room type may vary affecting activity type and the outcome.
Importance of recognizing when variability is unsystematic versus systematic.
Good Experimental Practices
Good experiments should control for systematic variability to ensure that changes in the dependent variable are due to the independent variable manipulation alone.
Upcoming Assignments and Focus Areas
Introduction to potential threats to internal validity due tomorrow.
Discussion of relevant research articles and their implications for the class project commencing in lab discussion sessions.