Psychology Exam Preparation Notes

The Science of Psychology - Overview Notes

Lesson 1: Types of Studies

• Definition of Psychology: The scientific study of the mind and behavior.
• Empirical Research: The foundation of psychology as a science, including methods like experiments, surveys, and case studies.
  • Importance of Empirical Data: Allows testing of hypotheses and deriving evidence-based conclusions.
• Facts vs. Opinions:
  • Facts: Objective statements confirmed by evidence (e.g., stress increases cortisol levels).
  • Opinions: Subjective beliefs not grounded in empirical evidence.
• Theories in Psychology:
  • Serve as frameworks for guiding research and formulating testable hypotheses (e.g., Social Cognitive Theory).
• Hypothesis: A testable prediction derived from a theory (e.g., exposure to aggression leads to aggressive behavior).
• Falsifiability: Essential criterion in science which requires that hypotheses can be proven false.
• Types of Research:
  • Basic Research: Expands theoretical understanding without immediate application.
  • Applied Research: Addresses practical problems using psychological knowledge.
• Research Methods:
  • Qualitative Research: Explores deeper human experiences without numerical data (e.g., interviews about social anxiety).
  • Quantitative Research: Involves numerical data and statistical analysis (e.g., surveys measuring stress levels).

Lesson 1.2: Naturalistic Observation

• Naturalistic Observation: Observing behavior in natural settings without intervention.
  • Advantages:
  • Authentic context leads to more ecological validity.
  • Challenges:
  • Observer Bias: Influences of researchers' expectations on observations.
  • Hawthorne Effect: Participants change their behavior due to being observed.

Lesson 1.3: Case Studies

• Case Studies: Intensive examinations of individuals or phenomena, providing deep insights.
  • Advantages:
  • Rich, detailed data that can develop or refine theories.
  • Limitations:
  • Limited generalizability and potential subjective biases.

Lesson 1.4: Surveys

• Surveys: Tools for gathering self-reported data from participants.
  • Advantages:
  • Efficient and can gather data from large samples quickly.
  • Limitations:
  • Susceptible to self-serving biases and memory errors.
• Need for Multi-Method Approach: To ensure comprehensive understanding, surveys should be paired with other methods.

Lesson 1.5: Comparing Groups

• Cross-Sectional Research: Studies different groups at one point in time (e.g., comparing students across grades).
  • Limitations:
  • Potential cohort effects.
• Longitudinal Research: Follows the same group over time to assess changes.
  • Advantages:
  • Understanding development and causal relationships.
  • Challenges:
  • Resource-intensive and subject to attrition.

Lesson 2: Experiments

• Experimental Method: Systematic approach to establishing cause-and-effect relationships by manipulating variables.
• Key Components:
  • Experimental vs Control Groups
  • Independent and Dependent Variables
  • Operational Definitions: Essential for clarity and replicability (e.g., "alertness" measurements).
  • Replicability: Reproducing results to validate findings.

Lesson 2.2: Avoiding Confounding Variables

• Validity and Reliability:
  • Reliability: Consistency of results across repeated measures.
  • Validity: Accuracy of measurement in capturing the intended phenomenon.
• Confounding Variables: External factors that may distort findings.
• Experimenter Bias: Researchers' expectations might influence outcomes.
  • Control Techniques: Use of single-blind and double-blind procedures.

Lesson 3: Participants & Ethics

• Participant Selection: Importance of representative sampling methods and random assignment in research.
  • Generalizability: Extent to which findings can apply beyond the sample studied.
• Ethics in Research:
  • Informed Consent: Participants' rights to understand and voluntarily agree to study conditions.
  • Deception and Debriefing: Ethical use of deception must be justified and followed by thorough explanation post-study.

Lesson 4: Analyzing Findings

• Measures of Central Tendency: Mean, median, and mode used for data summary.
  • Mean: Average value.
  • Median: Middle value unaffected by outliers.
  • Mode: Most frequent value.
• Variation in Data:
  • Range: Difference between maximum and minimum scores.
  • Standard Deviation: Average deviation from the mean, indicating data spread.
• Correlation and Causation:
  • Correlation: Statistical relationship between variables, quantified by correlation coefficients.
  • Positive and negative correlations explained with examples.
  • Causation vs Correlation: Correlation does not imply causation; potential for illusory correlations exists.
• Statistical Significance:
  • Evaluating experiment outcomes against the null hypothesis using p-values to determine significance (typically p < 0.05).
  • Confidence Interval: Range providing where the true effect lies.
  • Effect Size: Indicates the magnitude of differences observed.