EDPK5002: Semester 2, 2024

Units EDPK5002 Semester 2 2024 [Online]

Unit outline_

EDPK5002: Quantitative Methods

Semester 2, 2024 [Online] - Camperdown/Darlington, Sydney

Overview

This unit introduces students to the major issues underpinning quantitative research in applied fields such as education and social work. It examines the relationship between research questions and appropriate study designs, as well as ethical considerations. Students will develop a broad-based understanding of a range of quantitative methods and their strengths and limitations.

Unit details and rules

Academic unit	Education
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Helen Watt, helen.watt@sydney.edu.au
Lecturer(s)	Nicole Mockler, nicole.mockler@sydney.edu.au
	Paul Ginns, paul.ginns@sydney.edu.au
	David Evans, david.evans@sydney.edu.au
	James Tognolini, jim.tognolini@sydney.edu.au
	Helen Watt, helen.watt@sydney.edu.au
	Tim Powers, tim.powers@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Assignment 1: Critical Appraisal Critical appraisal of quantitative research related to students' research.	40%	Week 07 Due date: 13 Sep 2024 at 23:59	2500 words
Outcomes assessed:
Assignment	Assignment 2: Methdological Overview and Rationale Methodological overview and rationale for proposed research study.	60%	Week 13 Due date: 01 Nov 2024 at 23:59	3500 words
Outcomes assessed:

Assessment summary

Students are required to complete a critical appraisal of self-identified peer reviewed research relevant to their research interests, and to complete a methodological overview and rationale for their proposed study. Detailed information for each assessment task can be found on Canvas.

Assessment criteria

Result name	Mark range	Description
High distinction	85 - 100	Learning outcomes of the unit are met to an exceptional standard.
Distinction	75 - 84	Learning outcomes of the unit are met to a very high standard.
Credit	65 - 74	Learning outcomes of the unit are met to a good standard.
Pass	50 - 64	Learning outcomes of the unit are met to an acceptable standard.
Fail	0 - 49	Learning outcomes of the unit are not met 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.

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.

Support for students

The Support for Students Policy 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. 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

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to quantitative methods in social research (Prof Helen Watt)	Online class (2 hr)
Week 02	Survey design and development (Prof Helen Watt)	Online class (2 hr)
Week 03	Experimental and quasi-experimental design (Assoc prof Paul Ginns)	Online class (2 hr)
Week 04	Introduction to latent change score models for longitudinal data (international guest Dr Gabriela Gniewosz, University of Innsbruck, Germany)	Online class (2 hr)
Week 05	Quantitative analysis of large administrative data sets (Prof Nicole Mockler)	Online class (2 hr)
Week 06	Scale development using: 1) exploratory factor analysis (Assoc prof Paul Ginns); 2) confirmatory factor analysis (Prof Helen Watt)	Online class (2 hr)
Week 07	Introduction to Rasch theory (Prof Jim Tognolini)	Online class (2 hr)
Week 08	Structural equation modelling (Prof Helen Watt)	Online class (2 hr)
Week 09	Person-centred analyses: cluster and latent profile analyses (Prof Helen Watt)	Online class (2 hr)
Week 11	1) Meta-analysis (Assoc prof Paul Ginns) 2) Single-subject design (Prof David Evans)	Online class (2 hr)
Week 12	Making methodological decisions in your research (Prof Helen Watt)	Online class (2 hr)
Week 13	Wrapping it up (Prof Helen Watt)	Online class (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.

Required readings

All readings for this unit can be accessed through the Reading List, available on Canvas.

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. Understand how researchers make critical decisions about research methods, including the role of researchers' ontological and epistemological perspectives, conceptual and theoretical frameworks, and research questions
LO2. Understand the strengths and limitations of a range of different quantitative research methods and approaches
LO3. Understand the ethical implications of different research methods and researchers’ corresponding responsibilities
LO4. Apply a broad appreciation of quantitative methods to your own proposed research study

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.

This unit was completely redesigned for 2020, specifically with the needs and requirements of Higher Degree by Research Students in mind. It has undergone minor scheduling revisions for 2024.

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

Support for students

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links