Topic 7: Research Methods in AQA Psychology A-level

Experimental Method

• Aim: General statement of the researcher's investigation plan.
• Hypotheses:
  • Directional Hypothesis: Predicts the direction of the relationship.
  • Non-directional Hypothesis: Predicts a relationship but no direction.
• Variables:
  • Independent Variable (IV): Manipulated by the researcher.
  • Dependent Variable (DV): Measured to see the effect of the IV.
• Operationalisation: Clearly defining how variables are measured.

Control of Variables

• Extraneous Variables: Non-systematic nuisance variables affecting the DV.
• Confounding Variables: Systematically vary with the IV, affecting the DV.
• Demand Characteristics: Cues influencing participant behavior.
• Investigator Effects: Unwanted researcher influence.
• Randomisation and Standardisation: Use chance to reduce bias and ensure uniform procedures.

Types of Experiments

• Laboratory Experiments: High control but low ecological validity.
• Field Experiments: Conducted in natural settings; higher ecological validity but ethical concerns.
• Quasi-Experiments: IV not manipulated by researcher; high internal validity but issues with participant allocation.
• Natural Experiments: IV not brought by researcher; high external validity but rare and hard to replicate.

Sampling

• Opportunity Sampling: Easy but not representative.
• Random Sampling: No bias but time-consuming.
• Systematic Sampling: Consistent but not truly unbiased unless random.
• Stratified Sampling: Representative but time-consuming.
• Volunteer Sampling: Quick but attracts certain profiles.

Experimental Design

• Independent Groups: Different participants in each condition.
• Repeated Measures: Same participants in all conditions; issues with order effects.
• Matched Pairs: Participants matched on variables; time-consuming.

Pilot Studies

• Small-scale studies to identify problems before the main study.

Single-Blind and Double-Blind Procedures

• Single-Blind: Participants unaware of their group.
• Double-Blind: Neither participants nor experimenters know the group allocations.

Observational Techniques

• Naturalistic Observation: High ecological validity but low control.
• Controlled Observation: Higher control but lower ecological validity.
• Overt vs. Covert: Ethical considerations vs. natural behavior.
• Participant vs. Non-Participant: Insight vs. observer bias.

Correlations

• Positive, Negative, and Zero Correlations: Types of relationships between variables.
• Strengths: Quick and economical.
• Limitations: No cause-effect conclusions; potential for third variables.

Data Analysis: Types of Data

• Qualitative Data: Detailed but hard to analyze.
• Quantitative Data: Easy to analyze but lacks depth.
• Primary vs. Secondary Data: Originality vs. ease of access.

Descriptive Statistics

• Measures of Central Tendency: Mean, median, mode.
• Measures of Dispersion: Range, standard deviation.

Presentation of Quantitative Data

• Tables, Bar Charts, Histograms, Line Graphs: Various methods for data representation.

Distributions

• Normal Distribution: Symmetrical bell-shaped.
• Skewed Distribution: Data clustered to one end.

Peer Review

• Ensures quality and validity of research.
• Issues include anonymity concerns and publication bias.

Implications for the Economy

• Psychological research informs policies and practices impacting economic productivity.

Case Studies

• In-depth analysis providing qualitative data.
• Strengths: Detailed insights; Limitations: Not generalizable.

Content Analysis

• Indirect study of human behavior through produced materials.
• High external validity; observer bias possible.

Levels of Measurement

• Nominal, Ordinal, Interval: Different data scales for analysis.

Scientific Reporting

• Consists of abstract, introduction, method, results, discussion, and references.

Statistical Testing

• Determines significance of results.
• Sign Test: A basic statistical test used under specific conditions.

Choosing an Inferential Statistical Test

• Depends on design, data level, and type of analysis.
• Rule of R indicates when a calculated value should be greater or lesser.

Probability and Significance

• Type I & II Errors: False positive vs. false negative conclusions.

Features of a Science

• Paradigms and Shifts: Changes in central scientific assumptions.
• Falsifiability, Replicability, Objectivity: Criteria for scientific validity.

Ethical Issues

• Informed Consent, Deception, Harm Protection: Key ethical considerations and solutions.

Self-Report Techniques

• Questionnaires and Interviews: Methods for personal data collection.
• Open vs. Closed Questions: Different data collection approaches.

Reliability Across Methods

• Types: Internal and external reliability.
• Improvement in Tools: Ensuring consistency and precision in instruments.

Validity

• Internal vs. External Validity: Accuracy vs. generalizability.
• Ways to improve validity include control groups and standardizing procedures.