PSYC4125: Semester 1, 2025
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Unit outline_

PSYC4125: Advanced Psychometrics

Semester 1, 2025 [Normal day] - Camperdown/Darlington, Sydney

Studying psychology means posing problems, generating hypotheses, designing experiments observing human behaviour, testing your hypotheses, interpreting and evaluating data, and determining how to follow up on your findings. This unit of study will expand your knowledge of core conceptual, statistical and analytical approaches available to the broad research areas in psychology and related disciplines. You will investigate fundamental issues surrounding psychological measurement and hypothesis testing. Hands on experience will be gained in the application of statistics and psychometrics to the types of data commonly collected in psychological research. By undertaking this unit you will develop a critical and analytical approach towards measurement and psychometric theories, and an understanding of statistical techniques based on the general linear model.

Unit details and rules

Academic unit Psychology Academic Operations
Credit points 6
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

None

Available to study abroad and exchange students

No

Teaching staff

Coordinator Steson Lo, steson.lo@sydney.edu.au
Lecturer(s) Carolyn MacCann, carolyn.maccann@sydney.edu.au
Steson Lo, steson.lo@sydney.edu.au
Haryana Dhillon, haryana.dhillon@sydney.edu.au
Damian Birney, damian.birney@sydney.edu.au
Daniel Costa, daniel.costa@sydney.edu.au
The census date for this unit availability is 31 March 2025
Type Description Weight Due Length
Supervised exam
? 
Exam B
See Canvas for details
37.5% Formal exam period
Due date: 10 Jun 2025 at 15:00
1.5 hours
Outcomes assessed: LO1 LO5 LO7 LO8 LO9 LO10 LO11
Online task AI Allowed Part A Quizzes
See Canvas for details
10% Multiple weeks See Canvas for details
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO10 LO11
Online task AI Allowed Part B Quizzes
See Canvas for details
10% Multiple weeks See Canvas for details
Outcomes assessed: LO1 LO5 LO7 LO8 LO9 LO10 LO11
Participation AI Allowed Advanced Workshops
See Canvas for details
5% Multiple weeks See Canvas for details
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10 LO11
Supervised test
? 
In-semester test (Exam A)
See Canvas for details
37.5% Week 07
Due date: 08 Apr 2025 at 15:00
1.5 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO10 LO11
AI allowed = AI allowed ?

Assessment summary

  • Exam A: will cover material from Lectures 1-10 and Tutorials 1-3
  • Exam B: will cover material from Lectures 11-18 and Tutorials 4-7 
  • Quizzes: will cover material relevant to the prior week(s) 

 

Assessment criteria

Result Mark Range Description
HD 85-100 Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard
DI 75-84 Awarded when you demonstrate the learning outcomes for the unit at a very high standard
CR 65-74 Awarded when you demonstrate the learning outcomes for the unit at a good standard
PS 50-64 Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard
FA 0-49 When you don’t meet the learning outcomes of the unit to a satisfactory standard

 

For more information see guide to grades.

Use of generative artificial intelligence (AI) and automated writing tools

Except for supervised exams or in-semester tests, you may use generative AI and automated writing tools in assessments unless expressly prohibited by your unit coordinator. 

For exams and in-semester tests, the use of AI and automated writing tools is not allowed unless expressly permitted in the assessment instructions. 

The icons in the assessment table above indicate whether AI is allowed – whether full AI, or only some AI (the latter is referred to as “AI restricted”). If no icon is shown, AI use is not permitted at all for the task. Refer to Canvas for full instructions on assessment tasks for this unit. 

Your final submission must be your own, original work. You must acknowledge any use of automated writing tools or generative AI, and any material generated that you include in your final submission must be properly referenced. You may be required to submit generative AI inputs and outputs that you used during your assessment process, or drafts of your original work. Inappropriate use of generative AI is considered a breach of the Academic Integrity Policy and penalties may apply. 

The Current Students website provides information on artificial intelligence in assessments. For help on how to correctly acknowledge the use of AI, please refer to the  AI in Education Canvas site

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

WK Topic Learning activity Learning outcomes
Week 01 1. EFA/CFA and Reliability A (DC) Lecture (1 hr) LO1 LO4 LO5 LO6 LO10 LO11
2. EFA/CFA and Reliability B (DC) Lecture (1 hr) LO1 LO4 LO5 LO6 LO10 LO11
Week 02 3. EFA/CFA and Reliability C (DC) Lecture (1 hr) LO1 LO4 LO5 LO6 LO10 LO11
4. EFA/CFA and Reliability D (DC) Lecture (1 hr) LO1 LO4 LO5 LO6 LO10 LO11
1. EFA & reliability Tutorial (2 hr) LO1 LO4 LO5 LO6 LO10 LO11
Week 03 5. Structural Equation Modeling: Path analysis & Mediation A (CM) Lecture (1 hr) LO1 LO4 LO5 LO6 LO10 LO11
6. Structural Equation Modeling: Path analysis & Mediation B (CM) Lecture (1 hr) LO1 LO4 LO5 LO6 LO10 LO11
Week 04 7. Measurement 1 (DB) Lecture (1 hr) LO1 LO5 LO11
8. Measurement 2 (DB) Lecture (1 hr) LO1 LO5 LO11
2. Mediation with SPSS & JASP Tutorial (2 hr) LO1 LO5 LO6 LO10 LO11
Week 05 9. Qualitative Methods 1 (HD) Lecture (1 hr) LO1 LO3
10. Qualitative Methods 2 (HD) Lecture (1 hr) LO1 LO3
3. Qualitative Methods Tutorial (2 hr) LO1 LO3
Week 09 11. Power & Effect Sizes (SL) Lecture (1 hr) LO1 LO2 LO7 LO11
12. GLM: Hypothesis testing, Contrasts (SL) Lecture (1 hr) LO1 LO7 LO8 LO9 LO10 LO11
Week 10 13. Interactions with Mixed Designs (SL) Lecture (1 hr) LO1 LO7 LO8 LO9 LO10 LO11
14. Interactions: Categorical x Continuous (SL) Lecture (1 hr) LO1 LO7 LO8 LO9 LO10 LO11
4. ANOVA Tutorial (2 hr) LO1 LO7 LO8 LO9 LO10 LO11
Week 11 15. Interactions: Continuous x Continuous (SL) Lecture (1 hr) LO1 LO7 LO8 LO9 LO10 LO11
16. Logistic regression models (SL) Lecture (1 hr) LO1 LO7 LO8 LO9 LO10 LO11
5. Mixed-ANOVA Models Tutorial (2 hr) LO1 LO7 LO8 LO9 LO10 LO11
Week 12 17. Multi-level regression models 1 (SL) Lecture (1 hr) LO1 LO7 LO8 LO9 LO10 LO11
18. Multi-level regression models 2 (SL) Lecture (1 hr) LO1 LO7 LO8 LO9 LO10 LO11
6. Interactions in GLM Tutorial (2 hr) LO1 LO7 LO8 LO9 LO10 LO11
Week 13 7. Multi-Level Models Tutorial (2 hr) LO1 LO7 LO8 LO9 LO10 LO11

Attendance and class requirements

See Canvas for details. 

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

Individual lecturers will provide a list of recommended readings.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. Critically and analytically appraise measurement and psychometrics
  • LO2. Elucidate conceptual issues relating to probability, null hypothesis significance testing, and the empirical meaning of parameters in statistical models
  • LO3. Critically and analytically appraise qualitative methods
  • LO4. Evaluate the methods, instruments used and data gathered from non-experimental research and undertake item analysis as part of scale development
  • LO5. Apply the concepts of validity and reliability concepts to practical applications of testing
  • LO6. Interpret exploratory and confirmatory factor analytic techniques
  • LO7. Identify and deduce experimental design issues including control of variability, confounding and bias, power and effect sizes
  • LO8. Use multivariate analyses, analysis of variance, multiple regression analyses, logistic regression and multi-level modelling
  • LO9. Use effect and contrast coding to test statistical hypotheses using general linear model
  • LO10. Analyse data and interpret output in a scientifically meaningful way
  • LO11. Explain the limitations and shortcomings of psychometric and statistical models, packages and inferences.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

Based on previous student feedback, the WORKSHOPS have been distributed among three periods to accommodate students’ diverse interests and timetables. Also, all lectures are two-hour lectures delivered on the same day (Tuesday at 3-5 pm) instead of having one-hour lectures twice a week. This change was done to facilitate students' engagement.

See Canvas for details.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

This unit of study outline was last modified on 11 Feb 2025.

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