PSYCH307 - Lecture 1 Recording

Lecturer Background

Lecturer originally studied psychology in Brazil: BA + MA.
First visited New Zealand in 02/2001 for intensive English.
Returned on a scholarship in 02/2003 for a PhD at University of Auckland.
Post-PhD research fellow: worked on Youth 2000 project (health & well-being of young people).
12 years as staff at Victoria University; moved to University of Waikato during the 2020 pandemic.
Promoted from Associate Professor (RADA) to full Professor.
Encourages 3rd-year students to discuss Honours and research opportunities; main coordinator Oleg on sabbatical.

Course Overview & Philosophy

Paper: Psych Theory 7 / PSYC307 (3rd-year research theory & methods).
Aim: strengthen understanding of psychological research, especially quantitative methods, while remaining methodological “agnostic” – method should follow the research question.
Mix of quantitative focus (stats, SPSS) with invited lectures on qualitative approaches.
Weekly structure: 2-hour Monday lecture (recorded for Hamilton/Tauranga), plus computer labs (start Week 2, sign-up 4 pm today via Moodle, one MS-Teams “online lab” will be recorded in advance).
First-time delivery by current lecturer; patience requested.
Class representatives (Hamilton & Tauranga) to be elected – volunteers email short blurb of experience and suitability.

Key Logistics

Communication & resources via Moodle; use forums rather than private email for Q&A.
Assignments submitted through Moodle and screened by Turnitin/Teradata for plagiarism (serious consequences for 3rd-years).
No prescribed textbook but strong recommendation: Field, Discovering Statistics Using IBM SPSS.
Writing & citation standard: APA 7 (“the bible”).
Additional readings (e.g., recent study where ChatGPT answered psychological scales) will be posted.
Lecture slides to be uploaded (including guest lectures – requested in advance).

Assessment Structure

No final exam.
4 pieces of assessment:
1. Online Test 1 (pre-mid-semester) – $25\%$
2. Online Test 2 (end of course) – $25\%$
3. Lab Report 1 (data entry, description & simple analyses).
4. Lab Report 2 (advanced analyses & interpretation).
Together the two lab reports = $50\%$ .
Comprehensive Lab Manual already on Moodle – start reading early; do not leave work to last minute.
Students will enter both provided and “fake” data to learn full workflow.

Practical Study Advice

Read lab manual early; form study groups but submit independent work.
Ask for help early (tutors in Tauranga + Hamilton; lecturer will alternate campuses).
Create a proactive “to-do” timeline.

Observation → Theory → Data Cycle

“Research is to see what everybody else has seen and to think what nobody else has thought.” (Einstein theme)

Observation of phenomenon ➜
Research Question (“Do speakers feel more confident with small vs large audiences?”) ➜
Theory/Prediction (e.g., confidence declines past a 50-person cutoff) ➜
Hypotheses (specific, testable) ➜
Data Collection (self-report, behavioural codes, physiological indices) ➜
Analysis ➜
Communication (reports, conferences, policy) ➜
Refinement/Replication (self or by other scientists) – science as an iterative loop.

Deduction vs Induction

Deductive (top-down): start with theory → derive hypotheses → collect data. Dominant in quantitative research/experiments.
Inductive (bottom-up): gather observations first → discern patterns → build theory. Dominant in qualitative research.
In practice, research is a continuum blending both.

Four Research Purposes (Stokes’ Quadrant)

Quest for Fundamental Understanding	Consideration of Use	Example
No	No	(empty cell – not science)
Yes	No	“Pure basic” – Bohr’s atomic model
No	Yes	“Pure applied” – Edison’s commercial inventions
Yes	Yes	“Use-inspired basic” – Pasteur’s germ theory

Example of Use-Inspired Basic Research

Oleg’s soundscape studies: natural vs urban sounds as restoration.
• Combines physiological markers (heart-rate variability) + psychological scales (mood, wellbeing).

Variables & Manipulation

Independent Variable (IV): manipulated cause (e.g., audience size).
Dependent Variable (DV): measured outcome (e.g., speaker confidence).
Random assignment ensures groups differ only on IV.
Within-subjects designs expose each participant to all IV levels; order is counter-balanced.

Correlation vs Causation & Third-Variable Problem

Correlation $r$ simply quantifies association.
Causation implies $\Delta \text{IV} \Rightarrow \Delta \text{DV}$ .
Third-Variable types:
1. Third Variable (broad) – causes both IV & DV; e.g., temperature → ice-cream sales & drownings.
2. Confounder – extraneous variable linked to both IV & DV, biasing estimates; e.g., smoking ↔ coffee $\&$ heart disease.
3. Mediator/Intervening – mechanism through which IV affects DV; e.g., education → job skills → income.
Beware spurious correlations (e.g., “number of pirates” vs global warming).

Research Designs

Non-Experimental (Observational/Correlational)

Measure variables as they exist; useful when manipulation is impossible, unethical or for real-world context (e.g., pandemic behaviours).
Cannot confirm causality; can suggest associations, mediation, moderation, developmental trends.

Experimental

Researcher manipulates IV and randomly assigns participants to conditions.
Includes control group (placebo or standard treatment) vs experimental group (new treatment/manipulation).
Blinding:
• Single-blind – participants unaware of group.
• Double-blind – both participants and researchers unaware (reduces confirmation bias).
Open-Science Practices: preregistration, registered reports, public protocols to curb researcher degrees of freedom.
Only experimental design with proper controls can justify causal claims.

Validity

Measurement-Specific

Face Validity – does it look like it measures the construct? (e.g., people diagnosed with depression judge items).
Construct Validity – umbrella term
• Convergent: correlates with related measures.
• Predictive: forecasts relevant future outcomes (e.g., intelligence test predicts GPA).

Experimental-Specific

Internal Validity – confidence that observed DV changes are caused by IV manipulation (no confounds).
External Validity – generalisability beyond study sample/lab (other populations, settings, tasks).
• Often trades off with internal validity (e.g., highly controlled lab ≠ real world).

Reliability

Consistency or stability of measurement; error model $X<em>{observed}=T</em>{true}+E_{error}$ .
Test–Retest Reliability – correlate scores across time.
Split-Half – correlate two random halves of items.
Internal Consistency – Cronbach $\alpha$ , McDonald $\omega$ .
Traits (personality) should yield higher reliability than transient states (mood).

Measurement Modalities

Direct: height, weight, reaction time, temperature.
Indirect: self-reports, peer reports, behavioural coding, physiological proxies (heart rate, skin conductance, pupil dilation).
Studies often mix modalities for richer evidence (e.g., soundscape study: HRV + mood questionnaire).

Equations & Notation Quick Reference

Observed score model: $X = T + E$ .
Pearson correlation: $r = \frac{\sum (x - \bar{x})(y - \bar{y})}{\sqrt{\sum (x - \bar{x})^{2}\, \sum (y - \bar{y})^{2}}}$ .
Cronbach’s alpha (simplified): $\alpha = \frac{k}{k-1}\left(1-\frac{\sum \sigma^{2}<em>{i}}{\sigma^{2}</em>{total}}\right)$ where $k$ = number of items.

Ethical & Administrative Reminders

Plagiarism checked via Turnitin; severe penalties for 3rd-years.
Follow APA 7 for all written work.
Data, code, lab instructions provided; nevertheless, uphold integrity.

Next Steps

Check Moodle for slide uploads, lab manual, reading links, sign-up.
Volunteer for class rep roles.
Lecturer onsite Hamilton next Monday – ensure correct room booking.
Begin reading Field (SPSS) text and lab manual; install SPSS.

Take-Home Messages

Let research questions dictate method; be “methodologically agnostic.”
Understand full cycle: observation → theory ↔ data.
Distinguish correlation from causation; design studies to rule out third-variables.
Prioritise both validity and reliability when measuring psychological constructs.
Experiments provide strongest causal evidence but require careful control, randomisation, and open-science safeguards.