Welcome to the PSYC 434 lecture website

Accessing Lectures and Readings

Seminar Time/Location: Wednesdays, 14:10–17:00 Easterfield Building Room: EA120
Course Outline: find a detailed schedule of topics, readings, and assignments in the Course Outline tab.
Readings: links to readings are directly within the Course Outline tab, essential for lecture preparation.
Lecture Materials: access slides, video recordings, and more under the Content tab, organised by week for ease of use.
Tests: in the same room as the seminar

Class times and locations

Test/Quiz Location IN CLASS

Contents Tab

The Contents tab offers direct access to weekly seminar and lab materials, including lecture outlines and lab resources.

Access it from the top right of the course platform by selecting the appropriate week.
Lab materials are available one week before the lecture; seminar review materials post-seminar.

Names and contact details

Course Coordinator Prof Joseph Bulbulia joseph.bulbulia@vuw.ac.nz

Course Coordinator’s Office EA324

R Help from Dr.Inkuk Kim inkuk.kim@vuw.ac.nz

Assignments and due dates

Assessment	CLOs	Percent	Due
Lab diaries (8 × 1.25%)	1,2,3	10	Weekly (satisfactory/not)
In-class test 1	2	15	22 April (w7)
In-class test 2	2,3	15	20 May (w11)
In-class presentation	1,2,3	10	27 May (w12)
Research report	1,2,3	50	30 May (Fri, end of w12)

Course Description

The official description:

This course will focus on theoretical and practical challenges for conducting research involving individuals from more than one cultural background or ethnicity. Topics are likely to include defining and measuring culture; developing culture-sensitive studies, choice of language and translation; communication styles and bias; questionnaire and interview design; qualitative and quantitative data analysis for cultural and cross-cultural research; minorities, power and ethics in cross-cultural research; and ethno-methodologies and indigenous research methodologies. Appropriate background for this course: PSYC 338.

Course Learning Objectives

Preamble: in this advanced course, students will develop foundational skills in cross-cultural psychological research with a strong emphasis on causal inference, a new and critical methodological approach.

Programming in R students will learn the basics of programming in the statistical language R, gaining essential computational tools for psychological research. The skills you acquire will lay the foundation for applying data analysis techniques in a causal inference framework and beyond.
Understanding Causal Inference. students will develop a robust understanding of causal inference concepts and approaches, with particular emphasis on how they mitigate common pitfalls in cross-cultural research. We will focus on designing studies, analysing data, and drawing strong conclusions about cause-and-effect relationships across cultures.
Understanding Measurement in Comparative Settings. students will learn techniques for constructing and validating psychometrically sound measures across diverse cultures. We will examine how to ensure measurements are reliable, cross-culturally valid, and aligned with theoretical constructs while focusing strongly on causal reasoning.

Assignments and due dates

Assessment	CLOs	Percent	Due
Lab diaries (8 × 1.25%)	1,2,3	10	Weekly (satisfactory/not)
In-class test 1	2	15	22 April (w7)
In-class test 2	2,3	15	20 May (w11)
In-class presentation	1,2,3	10	27 May (w12)
Research report	1,2,3	50	30 May (Fri, end of w12)

Weekly schedule (2026)

Week	Date (Wed)	Content
w1	25 Feb	Course introduction / Intro to R
w2	4 Mar	Causal diagrams: elementary structures
w3	11 Mar	Causal diagrams: confounding bias
w4	18 Mar	Interaction, measurement bias, selection bias
w5	25 Mar	Causal inference: average treatment effects
w6	2 Apr	Effect modification / CATE
—	8 Apr	Mid-trimester break
—	15 Apr	Mid-trimester break
w7	22 Apr	In-class test 1 (15%)
w8	29 Apr	Machine learning / IPTW
w9	6 May	Hands-on analysis
w10	13 May	Quarto / Measurement
w11	20 May	In-class test 2 (15%)
w12	27 May	Student presentations (10%)

Labs run in the final 60–90 minutes of the seminar during teaching weeks. Eight labs across weeks 1–6 and 8–10 (one of those nine teaching weeks is lab-free; to be confirmed).

Assessment 1: Lab diaries (10%)

Eight weekly lab diaries submitted across the teaching weeks.
Each diary is marked satisfactory/not satisfactory (1.25% each, full credit for satisfactory).
Labs take place in the final 60–90 minutes of the seminar.
Coding itself is not marked; the diary records progress and engagement.

Assessment 2: In-class test 1 (15%) — 22 April

Covers material from weeks 1–6 (causal diagrams, confounding, ATE, effect modification).
Test duration is 50 minutes. The allocated time is 1 hour 50 minutes.
Required: pen/pencil. No devices permitted.

Important

THE TEST IS IN CLASS (i.e. come to class with a writing instrument).

Assessment 3: In-class test 2 (15%) — 20 May

Covers material from weeks 8–10 (machine learning, IPTW, measurement, Quarto workflows).
Same format and conditions as test 1.

Assessment 4: In-class presentation (10%) — 27 May

10-minute presentation summarising the student’s study.
Assessment criteria: clarity, efficiency, and quality of presentation.

Assessment 5: Research report (50%) — due 30 May

Research report instructions

We will supply the data.
Lab sessions are designed to support you in this assignment.
We assume no statistical background.

Students choose one of two formats and must declare their choice by a date TBD (suggest by end of w6, 2 April).

Option A: Research Report

Quantify the Average Treatment Effect of a specific exposure using the NZAVS longitudinal synthetic dataset.

Introduction: 1,500-word limit.
Conclusion: 1,500-word limit.
Methods/Results: concise, no specific word limit.
APA style. Submit as a single PDF with R code appendix.

Assessment criteria (Option A)

Stating the problem

State your question: is your question clearly stated?
Relevance: have you explained its scientific importance?
Causality: is your question causal?
Subgroup analysis: does your question involve subgroups (e.g., cultural group)? Which?
Explain the framework: have you explained the causal inference framework in a way that is comprehensible to non-specialists?
Ethics/Policy interests: have you explained how this question might practically affect people?
Data source: are your data from the NZAVS simulated dataset? (if not, consult with me)
Data waves: are your data using three waves?

Determining the outcome

Outcome variable: is your outcome variable Y well-defined?
Multiple outcomes: do you assess multiple outcomes and are these well-defined?
Outcome relevance: can you explain how the outcome variable/s relate to your question?
Outcome type: is your outcome binary and rare? … etc.
Outcome timing: does your outcome appear after your exposure?

Determining the exposure

Exposure variable: is your exposure variable A well-defined?
Multiple exposures: are there multiple exposures? (If yes, for this study, reassess.)
Exposure relevance: have you explained how the exposure variable relates to your question?
Positivity: can we intervene on the exposure at all levels of the covariates?
Consistency: can we interpret what it means to intervene on the exposure?
Exchangeability: are there different versions of the exposure conditionally exchangeable given measured baseline confounders?
Exposure type: is the exposure binary or continuous?
Shift intervention: do you contrast static interventions or modified treatment policies?
Exposure timing: does your exposure appear before the outcome? (It should.)

Accounting for confounders

Baseline confounders: have you defined your baseline confounders L?
Justification: can you explain how the baseline confounders could affect both A and Y?
Timing: are the baseline confounders measured before the exposure?
Inclusion: is the baseline measure of the exposure and the baseline outcome included in the set of baseline confounders?
Sufficiency: are the baseline confounders sufficient to ensure balance on the exposure, such that A is independent of Y given L? If not, explain your sensitivity analysis (E-values).
Confounder type: are the confounders continuous or binary? If so, consider converting them to z-scores. If they are categorical with three or more levels, do not convert them to z-scores, but rather use one-hot encoding.

Drawing a causal diagram with unmeasured confounders

Causal diagram: have you drawn a causal diagram (DAG) to highlight both measured and unmeasured sources of confounding?
Measurement error: have you described potential biases from measurement errors?
Temporal order: does your DAG have time indicators to ensure correct temporal order?
Time consistency: is your DAG organised so that time follows in a consistent direction?

Identifying the estimand

What is your causal contrast?
Have you stated your causal contrast clearly?

Understanding source and target populations

Populations identified: have you explained how your sample relates to your target populations?

Set eligibility criteria

Criteria stated: have you stated the eligibility criteria for the study?

Describe sample characteristics

Descriptive statistics: have you provided descriptive statistics for demographic information taken at baseline?
Exposure change: have you described the magnitudes of change in the exposure from baseline to the exposure interval?
References: have you included references for more information about the sample (e.g. the NZAVS website)?
DATA ARE SIMULATED: have you made it clear you are working with simulated data?

Addressing missing data

Missing data checks: have you checked for missing data?
Missing data plan: if there are missing data, have you described how you will address the problem? (IPCW)

Selecting the model approach

Approach decision: G-computation, IPTW, or doubly-robust estimation?
Model specification: model specification?
Machine learning: have you explained how machine learning works?
Outcome specifics: if the outcome is rare and binary, have you specified logistic regression? If it’s continuous, have you considered converting it to z-scores?
Sensitivity analysis: have you described your sensitivity analysis (e.g. E-values)?

Most of these items can be addressed in a sentence or two, but all need to be covered.

Option B: Marsden Fund Expression of Interest (EOI)

Write a first-round Marsden Fund EOI following the RSNZ 2026 guidelines. The research question must use the causal inference framework taught in this course.

Required sections (adapted for student context, CV excluded):

Research Title (max 25 words, plain language)
Research Summary (max 200 words, plain language for non-specialists)
Abstract (one page: aim, importance, methods, key information)
Benefit Statement (one standalone page: why this research benefits New Zealand)
References (max 3 pages)
Roles and Resources (max one page: describe the team and resources a project like this would require)
Vision Mātauranga (200 words: indicate relevance to the four VM themes, or justify why not applicable)

Formatting: 12-point Times, single spacing, 2 cm margins.

Assessment criteria (Option B):

Novelty and ambition of the research question.
Rigour of the proposed methods.
Clarity of the benefit case.
Quality of the causal reasoning.
Adherence to EOI formatting requirements.

Extensions and Penalties

Extensions:
- Negotiate a new due date by writing (email) before the mid-term test.
- Every reasonable request will be accepted (e.g. too many assignments falling in the same week, you want another week to complete.)
Penalties:
- Late submissions incur a one full grade-per-week penalty, e.g. if late by one day, B \to C, one week later, C \to D.
- Over-length assignments will be penalised.
Unforeseeable Events:
- Extensions will require evidence (e.g., medical certificate).

Materials and Equipment

Bring a laptop with R and RStudio installed for data analysis sessions. Contact the instructor if you lack computer access.
For in-class tests, bring a writing utensil. Again, electronic devices are not permitted.

Reuse

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