PSY 201 - Exam Three - FINAL
Page 1: Research Designs
1. Single-Subject Designs
Definition: Research focused on one subject, often using repeated measures.
Example: Observing the effect of therapy on one person over time.
2. AB Design
Definition: A single-subject design with one baseline (A) and one intervention phase (B).
Example: Measuring a child’s behavior before and after introducing a reward system.
3. ABAB Reversal Design
Definition: Alternates baseline (A) and intervention (B) phases to confirm effects.
Example: Testing whether removing a reward causes behavior to revert.
4. Nonequivalent Group Designs
Definition: Compares groups that are not randomly assigned.
Example: Comparing test scores from two schools with different teaching methods.
5. Pre-Post Designs
Definition: Measures before and after an intervention without a control group.
Example: Measuring heart rates before and after exercise.
6. Cross-Sectional Design
Definition: Compares different groups at one point in time.
Example: Studying the effects of age by comparing 20-, 40-, and 60-year-olds.
7. Longitudinal Design
Definition: Follows the same group over time.
Example: Observing cognitive decline in participants over 10 years.
8. Cohort-Sequential Design
Definition: Combines cross-sectional and longitudinal designs.
Example: Tracking multiple age groups over several years.
9. Time-Sequential Design
Definition: Compares multiple time points across age groups.
Example: Examining changes in attitudes toward technology every five years.
10. Cross-Sequential Design
Definition: Combines elements of longitudinal and cross-sectional designs.
Example: Studying reading skills across different ages over time.
11. True Experiments (Single Factor)
Definition: Manipulates one independent variable with random assignment.
Example: Testing the effect of noise level on concentration.
12. True Experiments (Factorials)
Definition: Examines more than one independent variable and their interaction.
Example: Studying the effects of noise and lighting on concentration.
13. Mixed Factorial Design
Definition: Combines between-subject and within-subject factors.
Example: Testing different diets (between) and exercise routines (within).
Page 2: Key Terms in Research
14. Baseline Observations
Definition: Initial measurements before an intervention.
Example: Recording anxiety levels before therapy.
15. Trend
Definition: A pattern in data across time.
Example: Observing a gradual increase in sales over months.
16. Causation
Definition: One variable directly influences another.
Example: Smoking causes an increased risk of lung cancer.
17. Independent Variables (Factors)
Definition: Variables manipulated by the researcher.
Example: Type of music played during study sessions.
18. Levels of Factors
Definition: Different conditions of an independent variable.
Example: No music, classical music, or pop music.
19. Dependent Variable
Definition: The outcome measured in a study.
Example: Test scores after studying with different music.
20. Conditions
Definition: Specific combinations of factors in an experiment.
Example: High light with soft music.
21. Treatments
Definition: Interventions applied in an experiment.
Example: A new drug for treating depression.
22. Power
Definition: The likelihood of detecting a true effect.
Example: Large sample sizes increase power.
23. Error Variance
Definition: Variability in data not explained by the independent variable.
Example: Mood influencing test scores.
24. Manipulation of Variables
Definition: Changing variables to observe effects.
Example: Altering classroom seating arrangements.
25. Random Assignment
Definition: Randomly placing participants in conditions.
Example: Flipping a coin to assign participants to groups.
26. Internal Validity
Definition: The extent to which a study demonstrates a causal relationship.
Example: Random assignment minimizes confounds, increasing internal validity.
27. External Validity
Definition: The extent to which results generalize to other contexts.
Example: A study on college students may not apply to older adults.
28. Construct Validity
Definition: The degree to which a test measures the intended concept.
Page 3: Validity and Threats to Research
29. Statistical Validity
Definition: The extent to which statistical conclusions are accurate.
Example: Using the right statistical test ensures valid results.
Threats to Validity
1. History
Definition: Events occurring during a study that affect outcomes.
Example: A new law changes behavior during a long-term study.
2. Instrumentation
Definition: Changes in measurement tools over time.
Example: A survey is reworded halfway through a study.
3. Testing Effects
Definition: Repeated testing influences outcomes.
Example: Participants perform better on a test after practice.
4. Maturation
Definition: Natural changes in participants over time.
Example: Children improving their reading skills as they age.
5. Statistical Regression
Definition: Extreme scores tend to move toward the mean on retesting.
Example: High-performing students scoring closer to average later.
6. Assignment Bias
Definition: Non-random group assignments create confounds.
Example: Motivated students are placed in the experimental group.
Design Features
1. Between-Subject Design
Definition: Different groups of participants experience different conditions.
Example: Group A takes a drug; Group B takes a placebo.
2. Within-Subject Design
Definition: The same participants experience all conditions.
Example: Each participant tries both a high-fat and low-fat diet.
3. Order Effects
Definition: The sequence of conditions affects outcomes.
Example: Participants tire during later conditions.
4. Sequence Effects
Definition: Effects due to the specific order of conditions.
Example: The first treatment influences the second.
5. Counterbalancing
Definition: Varying condition order to minimize sequence effects.
Example: Half of participants experience Condition A first, half Condition B.
6. Block Randomization
Definition: Random assignment in blocks to ensure equal group sizes.
Example: Groups are formed with alternating participant assignments.
Page 4: Interactions and Quasi-Experimental Concepts
Interactions in Research
1. Main Effects
Definition: The impact of one independent variable on the dependent variable.
Example: Lighting improves mood regardless of temperature.
2. Interactions
Definition: The combined effects of two or more variables.
Example: Lighting and temperature together influence mood differently.
Quasi-Experimental Concepts
1. Quasi
Definition: Studies lacking random assignment.
Example: Comparing schools with different curriculums without randomization.
2. Subject Variable
Definition: Pre-existing characteristics of participants (not manipulated).
Example: Gender or age as a variable.
3. Limited Generality
Definition: Results apply only to specific populations or settings.
Example: A lab study on rats may not generalize to humans.