Welcome to PSYC 434
Conducting Research Across Cultures | Trimester 1, 2026
Prof Joseph Bulbulia | Victoria University of Wellington
Assessments
| Assessment | CLOs | Weight | Due |
|---|---|---|---|
| Lab diaries (8 × 1.25%) | 1, 2, 3 | 10% | Weekly (satisfactory/not) |
| In-class test 1 | 2 | 20% | 22 April (w7) |
| In-class test 2 | 2, 3 | 20% | 20 May (w11) |
| In-class presentation | 1, 2, 3 | 10% | 27 May (w12) |
| Research report (Option A or B) | 1, 2, 3 | 40% | 30 May (Fri) |
Accessing Lectures and Readings
- Seminar: Wednesdays, 14:10–17:00, Easterfield Building EA120
- Schedule: see the Schedule page for topics, readings, and assignments
- Lectures: weekly content pages contain slides, recordings, and lab materials
- Tests: in the same room as the seminar (bring a pen/pencil, no devices)
Contact
| Course coordinator | Prof Joseph Bulbulia, joseph.bulbulia@vuw.ac.nz |
| Office | EA313 |
| Office hours | Tuesday 14:00-15:00 or by appointment |
| R help | Boyang Cao, caoboya@myvuw.ac.nz |
Course Description
From the VUW course catalogue:
This course focuses on theoretical and practical challenges for conducting research involving individuals from more than one cultural background or ethnicity. Topics 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
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Understanding causal inference. Students will develop a clear understanding of causal inference concepts and workflows, with emphasis on how they address common pitfalls in cross-cultural research. We focus first on how to ask causal questions in comparative psychology, and only then on how to answer them: designing studies, analysing data, and drawing appropriately confident conclusions about cause and effect.
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Understanding measurement in comparative settings. A substantial portion of this course is devoted to measurement in psychological research. We cover classical techniques for constructing and psychometrically validating measures across cultures, and clarify why statistical tests alone cannot ensure we are measuring what we intend to measure.
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Statistical programming in R. Students will learn the basics of programming in the statistical language R, gaining computational tools for applying causal inference methods to real data.
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Computing fundamentals: the command line, Git, and GitHub. Students will learn to navigate their computer through the terminal, manage projects with Git, and collaborate through GitHub. These skills matter because the most powerful tools available to researchers today, from LLMs to cloud computing, operate through text-based interfaces. Students who understand their machines will get far more out of them.
Licence
© 2026 Joseph Bulbulia. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Licence.