PSYC4125: Semester 1, 2021

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Units PSYC4125 Semester 1 2021 [Normal day]

Unit of study_

PSYC4125: Advanced Psychometrics

Semester 1, 2021 [Normal day] - Camperdown/Darlington, Sydney

Overview

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

Unit code	PSYC4125
Academic unit	Psychology Academic Operations
Credit points	6
Prohibitions ?	None
Prerequisites ?	None
Corequisites ?	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
	Sabina Kleitman, sabina.kleitman@sydney.edu.au
	Daniel Costa, daniel.costa@sydney.edu.au
	Evan Livesey, evan.livesey@sydney.edu.au
	Damian Birney, damian.birney@sydney.edu.au
	Haryana Dhillon, haryana.dhillon@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:
Online task	Part B Quizzes Part B quizzes (best of two from three)	10%	Multiple weeks	3 x 15 mins
Outcomes assessed:
Online task	Exam A Written Examination through Canvas	35%	Week 06	3 hours
Outcomes assessed:
Assignment	Report Report on statistical analysis of data	15%	Week 10	750 words
Outcomes assessed:
Online task	Exam B Written Examination through Canvas	30%	Week 12	3 hours
Outcomes assessed:

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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Learning support

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.

Weekly schedule

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

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

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.

Outcomes
Graduate qualities

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
Learning outcomes

Responding to student feedback

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

Additional information

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.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

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