Scheda di revisione: Research Methods in Psychology

Research Methods Revision Sheet

1. 📌 Essentials

• Experiment: study manipulating variables to establish causality.
• Observation: watching behaviors, either passive or active.
• Survey: collecting data via questionnaires or interviews.
• Case Study: in-depth analysis of a single entity over time.
• Sampling: selecting participants to represent a population.
• Correlation: measures relationship strength noation.
• Types of Experiments: field (natural setting), quasi (no randomization), lab (controlled).
• Types of Observation: non-participant, overt, covert, participant.
• Types of Interviews: structured (fixed questions), unstructured (open-ended).
• Sampling Methods: random, stratified, systematic, opportunity, volunteer, snowball, cluster.

2. 🧩 Key Structures & Components

• Field Experiment — conducted in natural environment, high ecological validity.
• Quasi-Experiment — compares existing groups, no random assignment.
• Lab Experiment — high control over variables.
• Longitudinal Study — data collected over extended period.
• Cross-sectional Study — data collected at one point in time.
• Structured Interview — fixed questions, high reliability.
• Unstructured Interview — flexible, rich qualitative data.
• Sampling Techniques:
  • Random: equal chance.
  • Stratified: proportional subgroups.
  • Systematic: every nth individual.
  • Opportunity: convenience-based.
  • Volunteer: self-selected.
  • Snowball: chain recruitment.
  • Cluster: entire groups.

3. 🔬 Functions, Mechanisms & Relationships

• Experiments manipulate independent variables to observe effects on dependent variables.
• Observation allows recording of behaviors in natural or controlled settings.
• Surveys gather large data sets for statistical analysis.
• Case studies provide detailed, contextual insights.
• Sampling methods aim to reduce bias and improve representativeness.
• Correlation indicates the strength/direction of relationships but not causality.
• Hierarchical flow:
  • Experimental design → Data collection (observation, survey, interview) → Data analysis (statistics, correlation).

4. 📊 Comparative Table

Item	Key Features	Notes / Differences
Field Experiment	Conducted in natural environment	High ecological validity
Quasi-Experiment	No random assignment, compares existing groups	Limited causal inference
Lab Experiment	Controlled environment, strict variable manipulation	High internal validity
Longitudinal Study	Data over months/years	Time-consuming, detailed insights
Cross-sectional Study	Data at a single point in time	Quicker, less detailed
Quantitative Survey	Structured questions, numerical data	Suitable for statistical analysis
Qualitative Survey	Open-ended questions, narrative data	Depth over breadth
Correlational Study	Examines relationships, no causation	Correlation coefficient indicates strength

5. 🗂️ Hierarchical Diagram (ASCII)

Research Methods
 ├─ Experiments
 │   ├─ Field
 │   ├─ Quasi
 │   └─ Lab
 ├─ Case Studies
 │   ├─ Longitudinal
 │   └─ Cross-sectional
 ├─ Surveys
 │   ├─ Quantitative
 │   └─ Qualitative
 ├─ Observational Studies
 │   ├─ Non-Participant
 │   ├─ Overt
 │   ├─ Covert
 │   └─ Participant
 ├─ Interviews
 │   ├─ Structured
 │   └─ Unstructured
 └─ Sampling Methods
     ├─ Random
     ├─ Stratified
     ├─ Systematic
     ├─ Opportunity
     ├─ Volunteer
     ├─ Snowball
     └─ Cluster

6. ⚠️ High-Yield Pitfalls & Confusions

• Confusing quasi-experiments with true experiments; no randomization in quasi.
• Mistaking correlation for causation; correlation does not imply causality.
• Overlooking ethical issues in covert observation.
• Assuming longitudinal studies are always better; they are time-consuming.
• Mixing sampling methods; e.g., opportunity vs random.
• Ignoring ecological validity in lab experiments.
• Misinterpreting statistical significance as practical significance.
• Confusing structured and unstructured interviews; reliability vs depth.

7. ✅ Final Exam Checklist

• Understand the differences between field, quasi, and lab experiments.
• Know the advantages and disadvantages of each experimental type.
• Be able to identify and explain various observation methods.
• Differentiate structured vs unstructured interviews.
• Recall sampling techniques and their biases.
• Recognize correlation as a measure of relationship strength, not causality.
• Be familiar with longitudinal and cross-sectional designs.
• Understand ethical considerations in observational research.
• Know when to use quantitative vs qualitative surveys.
• Be able to interpret a comparative table of research methods.
• Visualize the hierarchy of research designs and methods.
• Be aware of common mistakes in research interpretation.
• Remember key definitions and functions of each method.
• Prepare to critically evaluate research approaches based on context and aims.