Introduction to Research Methods in Psychology
Study guide – concise, organized, and easy to read
1. Why Research Methods Matter
| Purpose |
Example |
| Explain behavior |
Identify why stress leads to memory lapses |
| Predict outcomes |
Forecast how a new therapy will affect anxiety scores |
| Control variables |
Isolate the effect of sleep on reaction time |
| Build theory |
Develop a model of motivation based on empirical data |
2. The Scientific Process in Psychology
- Observation – Notice a phenomenon (e.g., people perform better on tests after a short walk).
- Question – Form a clear, answerable question (“Does a brief walk improve test performance?”).
- Literature Review – Summarize what previous studies have found; locate gaps.
- Hypothesis – A testable prediction (e.g., H₁: Participants who walk for 5 min will score higher than those who sit).
- Operationalization – Define variables in measurable terms (walk = 5‑minute treadmill at 3 mph; performance = number of correct answers).
- Research Design – Choose a method (experiment, survey, observation, etc.).
- Data Collection – Gather information using appropriate tools (questionnaires, reaction‑time software, physiological sensors).
- Data Analysis – Apply statistical techniques (t‑tests, ANOVA, regression) to test the hypothesis.
- Interpretation – Relate results back to theory, consider alternative explanations.
- Report & Replicate – Publish findings; other labs attempt replication.
3. Major Research Designs
| Design |
Key Features |
Strengths |
Limitations |
| Experimental |
Random assignment; manipulation of independent variable (IV) |
Strong causal inference; control over confounds |
May lack ecological validity; ethical constraints |
| Quasi‑experimental |
No random assignment; natural groups |
Feasible in real‑world settings |
Weaker causal claims; possible selection bias |
| Correlational |
Measures relationship between variables (no manipulation) |
Easy to conduct; can study variables that cannot be manipulated |
No causation; third‑variable problem |
| Cross‑sectional |
Data from different participants at one time point |
Quick, inexpensive |
Cannot assess change over time |
| Longitudinal |
Same participants measured repeatedly |
Tracks development, causal direction |
Time‑consuming; attrition |
| Case Study |
In‑depth analysis of a single individual or group |
Rich qualitative detail |
Limited generalizability |
| Survey |
Standardized questionnaires or interviews |
Large samples; flexible |
Self‑report bias; response rates |
4. Variables and Their Types
| Concept |
Definition |
Example |
| Independent Variable (IV) |
Variable that the researcher manipulates or categorizes |
Type of exercise (walking vs. sitting) |
| Dependent Variable (DV) |
Outcome measured to assess effect of IV |
Test score |
| Control Variable |
Held constant to prevent confounding |
Room temperature |
| Extraneous Variable |
Uncontrolled factor that could influence DV |
Participant’s caffeine intake |
| Confounding Variable |
Extraneous variable that varies with IV, threatening internal validity |
If walking participants also receive more encouragement |
| Moderator |
Variable that changes the strength/direction of the IV‑DV relationship |
Age (effect of exercise may differ by age) |
| Mediator |
Variable that explains how the IV influences the DV |
Increased arousal mediates the effect |