UQ Extend Module 3

Ethics in Psychological Research

Definition:

• Ethics = guidelines/principles for moral & just treatment of others.

• In research: focus on how researchers treat participants, run studies, and conduct themselves.

• Based on Universal Declaration of Ethical Principles for Psychology.

Four Guiding Ethical Principles

Respect for the Dignity of Persons and Peoples

• Value, acknowledge, and treat all people equally regardless of origin, beliefs, or identity.

• Special care for vulnerable groups (e.g., children, minorities).

• Ensure equal opportunity to be seen, heard, acknowledged.

• Protect anonymity and confidentiality.

• Example: Evolving gender data collection → beyond male/female binary to non-binary & open responses to show respect and inclusion.

Competent Caring for the Well-Being of Persons and Peoples

• Aim for research findings to enhance well-being.

• Conduct research to benefit participants or at least cause no harm.

• Plan for and mitigate possible harm.

• “Competent” caring → researchers must have proper training for tools/tests used.

• Case Study – Tuskegee Syphilis Study (1932–1972):

  • 600 African-American men (399 with syphilis) misled, denied treatment (penicillin available from 1947).

  • No informed consent → participants not told study details.

  • Ethics committees now prevent such abuse (require informed consent, minimal/no harm).

Integrity

• Conduct research with objectivity and honesty, free from self-interest or outside influence.

• Avoid exploitation and bias in reporting.

• Example – Grossarth-Maticek Research:

  • Linked personality types to cancer/heart disease.

  • Allegations of data falsification (e.g., reclassifying participants, duplicating data).

  • Funded by tobacco companies → possible conflict of interest.

  • Findings not replicated → likely due to falsified data.

Responsibility to Society

• Psychology should contribute to understanding the human condition and improving well-being.

• Researchers must:

  • Understand and follow ethical conduct.

  • Reflect on and update research practices to stay ethical.

Stanford Prison Experiment (1971)

• Conducted at Stanford University by Philip Zimbardo.

• Setup: Mock prison in psychology building; participants randomly assigned as prisoners or guards.

• Payment: $15/day.

• Role of Zimbardo: Prison superintendent.

• Informed Consent Issues:

  • Participants given vague info, not told specifics (e.g., surprise home arrest, strip search).

  • Guards encouraged to be aggressive (no physical harm) to instill fear.

• Ethical Concerns:

  • Prisoners who wanted to leave were told they could not.

  • Planned 2 weeks → ended after 6 days when an outsider raised concerns.

  • Zimbardo admitted losing objectivity due to his role.

  • Formal debriefing not until years later.

Variables in Research

Key Variables

1. Independent Variable (IV):

   • Manipulated by the experimenter (e.g., age groups, drug type).

   • Sometimes cannot be directly manipulated (e.g., age).

2. Dependent Variable (DV):

   • Measured outcome; depends on IV.

   • Example: Studying mental ability vs. age → IV = age, DV = IQ score.

Unwanted (Extraneous) Variables

• Variables that contaminate results and obscure the relationship between IV and DV.

1. Situational Variables:

   • Environmental factors (temperature, noise, lighting, time of day).

   • Can affect all participants differently and unpredictably.

2. Individual Differences:

   • Natural variations between people (height, weight, motivation, anxiety).

   • Combine with situational variables to increase variability.

3. Measurement Error:

   • Inconsistencies in recording data (e.g., misreading ruler, stopwatch error).

   • Linked to experimenter’s attention, training, or bias.

• Effect:

  • Random variability can weaken or completely hide real relationships.

  • Example: Teaching method study → with unwanted variables removed, clear difference; with them present, results less consistent.

Confounding Variables

• Definition: Variables that vary systematically with IV, providing an alternative explanation for results → prevents establishing causation.

• Example:

  • Testing two drugs on rats: all Drug A rats tested in the morning, all Drug B rats in the afternoon.

  • Time of day becomes a confounding variable.

Controlling Confounding Variables:

1. Keep constant: Test all groups under same conditions (e.g., all in morning).

2. Counterbalance: Spread variations evenly (e.g., half of each group in morning, half in afternoon).

True Experimental Designs — Key Features

1. At least two levels of the Independent Variable (IV)

   • One level can be absence of treatment (control group/placebo).

   • Other = presence of treatment (experimental group).

2. Random Assignment

   • Equal chance of being in any group (coin flip, random number table, etc.).

   • Purpose: Distribute extraneous factors (motivation, ability, age, health) evenly so they don’t vary systematically with the IV.

3. Control for Confounding Variables

   • Prevent alternative explanations for observed differences between conditions.

Independent Groups Design (Between-Subjects)

• Structure:

  • Two or more groups, each experiencing a different level of the IV.

  • Participants randomly assigned to one group.

  • Experimental group receives IV; control group does not.

• Example — Tickling Experiment:

  • IV: Who does the tickling (robot vs. self).

  • DV: Ticklishness rating (1–10).

  • 32 participants → random assignment into 2 groups of 16.

  • Robot group tickled by robot; self group tickled themselves using robot arm.

  • Results: Robot tickle group generally rated higher ticklishness, but some overlap.

• Drawbacks:

  • High variability from individual differences (e.g., natural differences in ticklishness).

  • Requires more participants than repeated measures.

Repeated Measures Design (Within-Subjects)

• Structure:

  • Same participants tested in all conditions of the IV.

  • Fewer participants needed (e.g., 16 instead of 32 in tickling example).

  • Reduces random variability due to individual differences.

• Order Effects:

  • Experiencing one condition may influence responses in the next.

  • Controlled via counterbalancing:

    • Half participants → Condition A then B.

    • Half participants → Condition B then A.

• Example — Tickling Experiment:

  • All 16 participants experienced both robot and self tickling.

  • Order counterbalanced to control for order as a confound.

  • Results showed less spread in data → reduced variability from individual differences.

When Repeated Measures is NOT Suitable

• If one condition permanently changes participant responses (e.g., learning effects).

• Example: Comparing teaching methods for statistics → learning from first method influences performance in second method.

Summary Table

Observational Designs

Two types covered:

1. Correlational Design

2. Quasi-Experimental Design

Why not use true randomized experiments?

• Sometimes impractical or unethical (e.g., cannot assign people to start smoking).

Example: Smoking & Health

Hypothesis: Smoking is bad for health.

Operational definition of health: Number of doctor visits per year.

Prediction: More smoking → more doctor visits.

Correlational Study

• Method:

  • Observe people who already smoke/don’t smoke.

  • Measure:

    • Cigarettes smoked/day (IV)

    • Doctor visits/year (DV)

  • Example: Ask 200 people about both variables.

  • Create scatter plot: each point = one person’s data.

• Observation: Positive relationship — heavier smokers see doctors more often.

• Key point: No variables manipulated → just observation.

• Limitation: Cannot conclude causation.

Quasi-Experimental Design

• Similar to true experiment, but no random assignment.

• Method:

  • Form groups based on pre-existing characteristics (e.g., smoking habits).

  • Example:

    • Light smokers: 0–10 cigarettes/day

    • Heavy smokers: 20–30 cigarettes/day

    • DV = doctor visits/year

  • Plot results: heavy smokers have more doctor visits.

• Key difference from true experiment: Grouping based on existing traits, not random assignment.

• Other uses:

  • Age (e.g., young vs. older adults)

  • Health conditions (e.g., high blood pressure vs. normal)

  • Income levels (e.g., wealthy vs. middle class)

Causation?

• From correlational or quasi-experimental designs → No causal conclusion possible.

• Unknown third variables could explain results (e.g., drinking, diet).

Conditions for Causal Inference

(Only true randomized experiments can fully meet all three)

1. Relationship: Regular & reliable changes in one variable associated with changes in the other.

2. Time Order: Cause precedes effect.

3. No Other Explanations: Alternative causes ruled out (via randomization).

Summary

• Observational Designs = Correlational studies + Quasi-experiments.

• Correlational study: Measures 2+ variables in same group, examines relationship.

• Quasi-experiment: Like true experiment but without random assignment.

• Limitation: Lack of randomization → cannot infer causation.

• Only true experiments (with randomization) allow causal claims.