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Unit outline_

PSYC4125: Advanced Psychometrics

Semester 1, 2021 [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 Damian Birney, damian.birney@sydney.edu.au
Lecturer(s) Carolyn MacCann, carolyn.maccann@sydney.edu.au
Evan Livesey, evan.livesey@sydney.edu.au
Daniel Costa, daniel.costa@sydney.edu.au
Damian Birney, damian.birney@sydney.edu.au
Haryana Dhillon, haryana.dhillon@sydney.edu.au
Sabina Kleitman, sabina.kleitman@sydney.edu.au
Type Description Weight Due Length
Online task Part A Quizzes
Part A quizzes (best of two from three)
10% Multiple weeks 3 x 15 mins
Outcomes assessed: LO2 LO1 LO12 LO11 LO7 LO6 LO5 LO4 LO3
Online task Part B Quizzes
Part B quizzes (best of two from three)
10% Multiple weeks 3 x 15 mins
Outcomes assessed: LO1 LO12 LO11 LO10 LO9 LO8 LO6
Online task Exam A
Written Examination through Canvas
35% Week 06 3 hours
Outcomes assessed: LO1 LO12 LO11 LO7 LO6 LO5 LO4 LO3 LO2
Assignment Report
Report on statistical analysis of data
15% Week 10 750 words
Outcomes assessed: LO9 LO10 LO11
Online task Exam B
Written Examination through Canvas
30% Week 12 3 hours
Outcomes assessed: LO1 LO12 LO11 LO10 LO9 LO8 LO6

Assessment summary

  • Exam A: will cover material from Lectures 1-9 and Tutorials 1-3
  • Exam B: will cover material from Lectures 10-15 and Tutorials 4-6 
  • Quizzes: will cover material relevant to the prior week(s) 
  • Report: Will require analyses and then interpretation and reporting of results

 

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.

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.

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

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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.

WK Topic Learning activity Learning outcomes
Week 01 1. Data Science (DC) Lecture (2 hr) LO1
2. Qualitative Methods (HD) Lecture (2 hr) LO1 LO4
Week 02 3. EFA/CFA and Reliability A (SK) Lecture (2 hr) LO1 LO5 LO6 LO7 LO11 LO12
4. EFA/CFA and Reliability B and Data screening (SK) Lecture (2 hr) LO1 LO5 LO6 LO7 LO11 LO12
1. Qualitative Methods (TBD) Tutorial (2 hr) LO1 LO4
Week 03 5. Measurement: Conceptual Issues (DB) Lecture (2 hr) LO1
6. Bayesian Analyses 1 (EL) Lecture (2 hr) LO1 LO2 LO3
2. EFA & reliability (DC) Tutorial (2 hr) LO1 LO5 LO6 LO7 LO11 LO12
Week 04 7. Bayesian Analyses 2 (EL) Lecture (2 hr) LO1 LO2 LO3
8. Structural Equation Modeling: Path analysis & Mediation (CM) Lecture (2 hr) LO1 LO5 LO6 LO7 LO11 LO12
Week 05 9. Test Theory: Item Response Theory (CM) Lecture (2 hr) LO1 LO2
3. Mediation with SPSS & JASP (DC) Tutorial (2 hr) LO1 LO6 LO7 LO11 LO12
Week 07 10. Multifactor designs, Power & Effect Sizes (DB) Lecture (2 hr) LO1 LO8 LO9 LO11 LO12
11. GLM: Hypothesis testing, Contrasts (DB) Lecture (2 hr) LO1 LO6 LO8 LO9 LO10
Week 08 12. Interactions: Categorical & Continuous Variables (DB) Lecture (2 hr) LO1 LO9 LO10 LO11 LO12
13. Interactions: Mixed designs (DB) Lecture (2 hr) LO1 LO8 LO9 LO10 LO11 LO12
4. ANOVA (OT) Tutorial (2 hr) LO1 LO8 LO9 LO10 LO11 LO12
Week 09 14. Multi-level regression models A (DB) Lecture (2 hr) LO1 LO8 LO9 LO10 LO11 LO12
15. Multi-level regression models B (DB) Lecture (2 hr) LO1 LO8 LO9 LO10 LO11 LO12
5. Interactions in GLM (OT) Tutorial (2 hr) LO1 LO8 LO9 LO10 LO11 LO12
Week 10 Revision (DB) Lecture (2 hr) LO1 LO8 LO9 LO10 LO11 LO12
6. MLM (DB) Tutorial (2 hr) LO1 LO8 LO9 LO10 LO11 LO12

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.

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 Bayesian methods
  • LO4. Critically and analytically appraise qualitative methods
  • LO5. Evaluate the methods, instruments used and data gathered from non-experimental research and undertake item analysis as part of scale development
  • LO6. Apply the concepts of validity and reliability concepts to practical applications of testing
  • LO7. Interpret exploratory and confirmatory factor analytic techniques
  • LO8. Identify and deduce experimental design issues including control of variability, confounding and bias, power and effect sizes
  • LO9. Use multivariate analyses, analysis of variance, multiple regression analyses, logistic regression and multi-level modelling
  • LO10. Use effect and contrast coding to test statistical hypotheses using general linear model
  • LO11. Analyse data and interpret output in a scientifically meaningful way
  • LO12. 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, content on qualitative and Bayesian methods have been added to the course

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.

General Laboratory Safety Rules

  • No eating or drinking is allowed in any laboratory under any circumstances
  • A laboratory coat and closed-toe shoes are mandatory
  • Follow safety instructions in your manual and posted in laboratories
  • In case of fire, follow instructions posted outside the laboratory door
  • First aid kits, eye wash and fire extinguishers are located in or immediately outside each laboratory
  • As a precautionary measure, it is recommended that you have a current tetanus immunisation. This can be obtained from University Health Service: unihealth.usyd.edu.au/

Disclaimer

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

To help you understand common terms that we use at the University, we offer an online glossary.