PSYCH307 – Lecture 1 Notes

Course Overview

Purpose and Emphasis

• PSYCH307 introduces a range of research theories and methods to strengthen expertise in psychological research.

• Strong emphasis on quantitative research; expect extensive work with statistics.

Administrative Essentials

• Course outline, announcements, assignment portals, and laboratory sign-ups are all housed on Moodle – check it frequently.

• All assignments are submitted through Moodle and Turnitin is used for plagiarism checks.

Core Resources

• Main statistics text: Field (2018, 5th ed.) Discovering Statistics Using IBM SPSS Statistics (earlier editions acceptable).

• APA Publication Manual (7th ed.) is compulsory; earlier editions are not acceptable.

• Additional readings will be released as the course progresses.

Timetable

• Lectures: Monday, 1 – 3 pm
  • Hamilton: Room L.G.03
  • Tauranga: Room TCBD.3.06
  Attendance is highly encouraged.

• Laboratories (Teams-based workshops) begin Week 2.
  • Some labs have an instructor; others are drop-in.
  • Sign-up opens today at 4 pm on Moodle under “Laboratory Signup.”
  • Labs focus on statistical analyses required for the lab-report assessments.

Assessments (No Final Exam)

General Assessment Advice

• Start early, avoid last-minute rush.

• Collaborate for understanding but submit your own work.

• Communicate problems early—staff can only help if they know.

• Engage actively; learning is greatest when you do.

Revision of Research Methods

Inspirational Quotes

• “Research is to see what everybody else has seen, and to think what nobody else has thought.” — Albert Szent-Györgyi

• “There is nothing as practical as a good theory.” — Kurt Lewin (1943/1951)

The Research Process (Cycle)

1. Observation of behaviour/phenomenon.

2. Theory generation (or theory testing).

3. Hypothesis formation from the theory.

4. Research design & data collection.

5. Data analysis & interpretation.

6. Theory refinement / new hypotheses → back to Step 3.

Example: Human Response to Soundscapes

• Theory: Natural sounds are restorative; environmental noise is harmful.

• Medvedev et al. (2015): Two studies measured physiological and psychological responses after stress/rest while manipulating soundscapes.

• Results loop back to theory: supported? falsified? If falsified, refine the theoretical model and restart the cycle.

Stokes’ Quadrant Model of Scientific Research

            CONSIDERATION OF USE?  ─────────────────────►  YES
            │                           Pure Applied (Edison)
QUEST FOR    │                           
FUNDAMENTAL   │  Use-Inspired Basic (Pasteur)
UNDERSTANDING?│
            │                           Pure Basic (Bohr)
            ▼  NO

• Bohr quadrant: Pure basic research, e.g., atomic structure.

• Pasteur quadrant: Simultaneously seeks understanding and use (e.g., vaccination).

• Edison quadrant: Pure applied, focused on utility.

Most psychological projects sit somewhere between quadrants, balancing theoretical insight and practical application.

Key Topics in Research Logic

Inductive vs Deductive Reasoning

• Induction: Build theory from observations (data → theory).

• Deduction: Start with theory, derive hypotheses, and test with data (theory → data).

• Real-world studies usually blend both; quantitative work leans deductive, qualitative leans inductive.

Correlation vs Causation

• Correlation: $\text{Change in A} \; \longleftrightarrow \; \text{Change in B}$
  Occur together but A may not cause B.

• Causation: $\text{Change in A is necessary for Change in B}$
  Requires temporal precedence, covariation, and elimination of alternatives.

Independent vs Dependent Variables

• Independent Variable (IV): Manipulated or classified cause.

• Dependent Variable (DV): Measured outcome.

• Manipulation grants control—researcher sets IV levels, enabling “switch on/off” tests of causal effects.

The Third-Variable Problem

• Apparent IV → DV relationship may be spurious, produced by a hidden variable.

Examples

1. Violent video games ↔ aggression; stress could cause both.

2. Population density ↔ crime; socioeconomic status (SES) could be the real driver.

Third Variable vs Confound vs Intervening (Mediator)

(For entertainment: “Pirates vs Global Warming” – a famous spurious correlation.)

Experimental vs Non-Experimental Designs

• Experiments:
  • Manipulation of IV
  • Control of extraneous variables (random assignment, control groups)
  → can establish causation.

• Non-experimental (observational): Limited to correlations; useful when manipulation is impossible, unethical, or when seeking ecological validity.

Why Still Use Observational Methods?

• Ethical/practical constraints (e.g., cannot assign sex or force smoking).

• Pilot data to discover potential causal pathways.

• Study behaviour in natural contexts.

Validity

Measurement Validity

• Face Validity: Superficial appearance—does the measure look right?

• Construct Validity (umbrella term): Degree to which a test truly measures the theoretical construct.

  • Convergent Validity: Correlates with established measures of same construct.

  • Predictive Validity: Forecasts future behaviours/abilities.

Design Validity

• Internal Validity: Confidence that DV changes are caused by IV manipulation; requires control of extraneous factors.

• External Validity: Generalisability of findings beyond sample/setting.

The Trade-Off

• High internal validity often uses tight lab control → may sacrifice realism.

• High external validity (field settings) → harder to rule out confounds.

Reliability

Definition

• Consistency or repeatability of a measurement.

• A measure can be reliable but not valid, yet cannot be valid without being reliable.

Measurement Error Conceptualised

$\text{Observed Score} = \text{True Score} + \text{Error}$

• Greater error → lower reliability.

Scale Example

• True weight $=70.0\,\text{kg}$.
  Readings: $69.5$, $70.3$, $69.8$ indicate small random error.

Assessing Reliability

• Test–Retest Reliability: Correlation between scores at Time 1 and Time 2.

• Internal Consistency
  • Split-Half: Correlate odd vs even items.
  • Cronbach’s $\alpha$ / McDonald’s $\omega$: Average inter-item correlation—most widely reported.

Measurement in Experimental Research

Objective Quantification

• Assign numbers or categories systematically to represent constructs.

Direct Measurement

• Concrete variables: height, weight, speed.

Indirect (Psychometric) Measurement

• Abstract constructs: stress, depression, coping; rely on questionnaires, ratings, behavioural indices.

Variables & Levels of Measurement

Discrete vs Continuous

• Discrete Quantitative (Counts): Whole numbers (e.g., goals scored).

• Qualitative/Categorical: Labels (gender, ethnicity) – numbers are mere codes.

• Continuous Quantitative (Measures): Infinite gradations (height, reaction time).

Four Measurement Scales

• SPSS merges Interval and Ratio into a single “Scale” variable type because analyses treat them similarly.

No Scale is “Better”

• Choice depends on research design and operational definition.

Ethical, Practical & Real-World Connections

• Ethical guidance (APA 7th ed.) underpins report writing, citation, and study conduct.

• Understanding validity/reliability ensures fair assessment in applied settings (clinical, educational, organisational).

• Differentiating correlation from causation prevents harmful policy decisions based on spurious links (e.g., violent games legislation).

Numerical & Statistical References

• Assessment weighting: $2 \times 25\% \text{ (Tests)} + 2 \times 25\% \text{ (Lab Reports)} = 100\%$.

• Reliability coefficients: Cronbach’s $\alpha$, McDonald’s $\omega$ (interpretation guidelines: $\ge 0.70$ acceptable, $\ge 0.80$ good, $\ge 0.90$ excellent).