Chapter 2 Notes (Psych 107)

Why science matters

• Before: Earth considered flat; mental illness thought to be demonic possession.
• Why it matters: Research validates claims; without it, intuition and baseless assumptions prevail.
• Science requires a systematic process and verification.
• Example: trephination—a hole in the skull—was historically believed to let evil spirits escape and cure disorders (Figure 2.2). The image shows the skull with a circular hole; roots in historical beliefs about mental illness being caused by spirits.
• Takeaway: Systematic inquiry helps separate myths from evidence-based conclusions.

Reasoning in the research process

• Deductive reasoning:
  • Premise 1: All living things require energy to survive.
  • Premise 2: Humans are living things.
  • Conclusion: Humans require energy to survive.
  • Driven by logical analysis.
• Inductive reasoning:
  • Based on observations: e.g., humans, dogs, and trees require energy to survive; AI programs require energy to run.
  • Conclusion: AI must be a living thing (an inference from observed data).
• Note: Deduction moves from theory to hypothesis; Induction moves from observations to theory.

Science uses both forms of reasoning

• Ideas formed through deductive reasoning.
• Hypotheses tested through empirical observations.
• Scientists form conclusions through inductive reasoning.
• Conclusions lead to new theories, which generate new hypotheses, creating a cycle: theory → hypothesis → observations → theory.

Theory and Hypotheses

• Theory: well-developed set of ideas that explains observed phenomena.
• Hypothesis: tentative, testable statement about relationships between two or more variables.
  • Predicts how the world will behave if the theory is correct.
  • Usually an if-then statement.
  • Is falsifiable, i.e., can be shown incorrect via empirical methods.

Types of Research

• Not all research is experimental.
• In this course:
  1) The term “experiment” describes a very particular design.
  2) “Empirical” means researchers followed a specific methodology and collected their own data to observe, analyze, and describe.

Case studies

• Focus on one individual.
• The studied individual is often in an extreme or unique psychological circumstance.
• Classic example: Phineas Gage.
• Conclusions: Brain injury (frontal lobe) might impact behaviors and personality, but generalizing to the broader population requires caution.
• Pros: Rich insight into a case.
• Cons: Limited generalizability to the larger population.

Naturalistic observation

• Observation of behavior in its natural setting.
• Reduces performance-related anxiety and yields genuine behavior.
• Observer bias: observations may be skewed to fit observer expectations.
• Mitigation: establish clear observation criteria.
• Pros: Observes genuine behavior.
• Cons: Susceptible to observer bias.
• Example: Seeing a police car behind you may affect driving behavior.

Surveys

• A list of questions delivered in various formats:
  • Paper-and-pencil
  • Electronically
  • Verbally
• Used to gather data from a large sample of individuals from a larger population.
• Pros: Efficient data collection from many people.
• Cons: People may lie; depth of information is limited compared to interviews.
• Data can be quantitative or qualitative.

Archival research

• Uses past records or data sets to answer questions or explore patterns.
• Pros: Data already collected; cost/time efficient.
• Cons: Cannot change what information is available.
• Researchers examine records (hardcopy or electronic).
• Image credits indicate sources for archival examples.

Timing: cross-sectional vs. longitudinal

• Cross-sectional research: compare multiple groups at a single point in time.
• Longitudinal research: take multiple measurements from the same group over time.
• Risk: attrition – participants dropping out over time.

Correlations

• Correlation: relationship between two or more variables; when variables change together.
• Correlation Coefficient: a number from
  -1 to +1, denoted by $r$, indicating strength and direction of the relationship.
• Visualization: scatterplots illustrate strength and direction; closer to a straight line indicates a stronger correlation.

Correlation does not imply causation

• Causation: a cause-and-effect relationship where changes in one variable cause changes in another.
• Can only be established through experimental design.
• Confounding variable: an unanticipated outside factor that affects both variables, creating a false impression of causation.
• Example: Ice cream sales and drowning incidents tend to correlate due to a third variable (hot weather) driving both.
• Statistical note: significance is tested to determine if results could occur by chance.

Issues with correlational research

• Illusory correlations: perceiving a relationship that does not exist.
• Confirmation bias: ignoring evidence that disproves preexisting beliefs.
• Example: The belief that full moons influence behavior, which research does not support.

Cause-and-effect and experiments

• Only experiments can conclusively establish causation.
• Not all research is an experiment.
• Experiments involve:
  • Experimental group: participants who experience the manipulated variable.
  • Control group: participants who do not experience the manipulated variable.
  • Purpose: provide a basis for comparison and control for extraneous factors.

Example experiment: the bystander effect

• Participants randomly assigned to experimental or control group.
• Difference between groups is the presence of others (the manipulation).
• Operational definitions specify how the researchers measure the study variables (e.g., interpretation of an emergency, measured by whether participants act).
• Scenario: Confederate participants present in the experimental group; no others present in the control group.

Other experimental design considerations

• Aim to minimize bias and placebo effects.
• Experimenter bias: researchers' expectations influence results.
• Participant bias: participants' expectations influence results (e.g., placebo effect).
• Solution: blinding.
  • Single-blind: participants do not know which group they’re in.
  • Double-blind: neither participants nor researchers interacting with participants know group assignments.

What are we studying? Variables

• Variable: a characteristic that can vary among subjects.
• Independent variable (IV): what researchers manipulate or control (e.g., group assignment).
• Dependent variable (DV): what researchers measure; may be influenced by the IV.

Selecting participants

• Participants are recruited from a population into a smaller subset called a sample.
• Random sampling is the gold standard for representation and bias prevention.
• Goal: use a sample of a population to generalize findings.

What do the results say? Statistics and significance

• Data are analyzed with statistics to determine if results could have occurred by chance.
• If the probability of the result happening by chance is very unlikely (usually $p < 0.05$), the results are considered statistically significant.

Reporting the findings

• Scientific studies are typically published in peer-reviewed journals.
• Peer review involves other scientists evaluating the study for quality and impact.
• Provides anonymous feedback and improves research quality.

Recognizing good science

• Measures and results should be: Reliable (consistent over time, across situations/raters) and Valid (measuring what it intends to measure).
• Variable and operational definitions:
  • A valid measure is always reliable, but a reliable measure is not always valid.

Ethics in research

• Research must follow ethical principles enforced by review boards/agencies.
• Human subjects research:
  • Institutional Review Boards (IRBs) check informed consent, voluntary participation, awareness of risks, benefits, implications, and confidentiality.
  • Ensure risks vs. benefits are considered for participants.
• Animal subjects research:
  • Institutional Animal Care and Use Committee (IACUC) checks humane treatment of animals.