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Experiment
A study designed to determine causal relationships between variables.
Independent Variable (IV)
The variable manipulated by researchers.
Dependent Variable (DV)
The outcome variable that is measured.
Conditions
Different levels of the independent variable (e.g., number of witnesses in Darley and Latané's study).
Active Intervention
Researchers actively change the level of the IV (e.g., trauma vs. neutral writing).
Importance of Manipulation
Essential to eliminate alternative explanations for observed effects.
Single-Factor Two-Level Design
Experiments can have two levels.
Single-Factor Multi-Level Design
Experiments can have more than two levels.
Extraneous Variables
Variables other than IV and DV that can influence results (e.g., participant characteristics, situational factors).
Holding Variables Constant
(e.g., same location, instructions).
Limiting Participant Characteristics
To reduce variability.
Noise
Extraneous variables can introduce variability, making it difficult to identify the IV's effect.
Confounding Variables
Confounding variables vary systematically with the IV and can offer alternative explanations for results.
Treatment
Interventions aimed at changing behavior (e.g., therapy).
Control Condition
Participants do not receive the treatment, providing a baseline for comparison.
Placebo Effects
Non-active treatments that can still lead to perceived improvements based on expectations.
Between-Subjects Experiments
Each participant experiences only one condition.
Within-Subjects Experiments
Each participant experiences all conditions.
Random Assignment
A method to assign participants to conditions randomly, ensuring each participant has an equal chance of being in any condition.
Carryover Effects
When previous conditions affect participants' behavior in later conditions.
Practice Effect
Improved performance due to practice.
Fatigue Effect
Decreased performance due to tiredness.
Context Effect
Changes in perception based on prior conditions.
Counterbalancing
A technique used to control for order effects by varying the order of conditions across participants.
Complete Counterbalancing
Every possible order is tested.
Latin Square Design
Ensures each condition appears in every position and precedes and follows every other condition once.
Internal Validity
Degree to which an experiment supports a causal relationship between IV and DV.
External Validity
Degree to which study results can be generalized to other people and situations.
Construct Validity
Quality of the experiment's manipulations and operational definitions.
Statistical Validity
Proper statistical treatment of data and soundness of statistical conclusions.
Correlation vs. Causation
Just because two variables are related does not imply one causes the other.
Operationalization
Translating research questions into measurable variables.
Power Analysis
Calculation to determine the required sample size for detecting effects.
Quasi-Experimental Research
Involves manipulating an independent variable without random assignment to groups.
One-Group Quasi-Experimental Design
Design assessing treatment impact without control groups.
One-Group Posttest Only Design
Measures dependent variable after treatment application.
One-Group Pretest-Posttest Design
The dependent variable is measured before and after the treatment.
Interrupted Time Series Design
Multiple measurements taken over time around treatment.
Control Groups
Groups not receiving treatment for comparison.
Posttest Only Non-Equivalent Groups Design
Participants in one group receive a treatment, while a nonequivalent group does not.
Pretest-Posttest Non-Equivalent Groups Design
Participants are assessed before and after a treatment in both a treatment and a control group.
Interrupted Time-Series Design with Nonequivalent Groups
Measurements are taken at multiple intervals before and after an intervention for both a treatment group and a nonequivalent control group.
Pretest-Posttest Design with Switching Replication
Groups receive pretests, followed by an intervention for one group, and later switching the intervention for the second group.
Generalizability
Applicability of results to broader populations.
Treatment Condition
Participants receive intervention aimed at behavior change.
Threats to Internal Validity
Factors that can lead to incorrect conclusions.
Maturation Effects
Changes in participants over time affecting results.
Instrumentation
Changes in measurement tools affecting results.
Regression to the Mean
Extreme scores tend to move closer to average.
Spontaneous Remission
Natural improvement without treatment intervention.
Random Counterbalancing
Randomly assigns order of conditions for each participant.
Recruiting Participants
Identify target population early and plan recruitment strategies.
Formal Subject Pool
Participants from established groups.
Advertisements
Use ads or personal appeals to specific groups.
Volunteer Characteristics
Volunteers may differ from non-volunteers, affecting external validity.
Need for Standardization
Minimize extraneous variables during experimentation.
Written Protocol
Document all procedures and instructions.
Standard Instructions
Provide consistent instructions for all participants.
Automation
Use software to deliver parts of the procedure.
Anticipate Questions
Prepare answers for potential participant inquiries.
Training
Train experimenters uniformly.
Blind Experimenters
Limit knowledge of conditions to reduce expectancy effects.
Importance of Good Records
Maintain a sequence of conditions and participant demographics.
Confidentiality
Use identification numbers instead of names for participant data.
Manipulation Check
Measure to verify the successful manipulation of the independent variable.