Outline

Overview

This unit enables students to acquire skills in a range of quantitative research methods in the social sciences. They learn about censuses and surveys as foundational methods of quantitative research, then move to statistical analyses of quantitative data. No prior university-level mathematical training is assumed, though a basic grasp of simple algebra acquired through upper-level study of maths at high school is expected.

Unit details and rules

Academic unit	Sociology and Criminology
Credit points	6
Prerequisites ?	12 credit points at 2000 level in Sociology or 12 credit points of (HSBH1003 or OCCP2087 or OCCP2088 or OCCP2085 or OCCP1097 or OCCP1096) or 12 credit points at 2000 level in Criminology
Corequisites ?	None
Prohibitions ?	SCLG2632 or SCLG3603
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Salvatore Babones, salvatore.babones@sydney.edu.au

Assessment

The census date for this unit availability is 31 August 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Research analysis	Data Analysis Project Data analysis project to be submitted during the examination period	20%	Formal exam period Due date: 16 Nov 2026 at 23:59	1000 words	AI allowed
Outcomes assessed:
Out-of-class quiz	Week 01 Quiz Multiple-choice quiz taken via Canvas covering lecture and reading material	2%	Week 01 Due date: 06 Aug 2026 at 23:59	4 minutes	AI allowed
Outcomes assessed:
Out-of-class quiz	Week 02 Quiz Multiple-choice quiz taken via Canvas covering lecture and reading material	2%	Week 02 Due date: 13 Aug 2026 at 23:59	4 minutes	AI allowed
Outcomes assessed:
Data analysis	Early Feedback Data Activity Hands-on data analysis task intended to provide early feedback	10%	Week 02 Due date: 14 Aug 2026 at 23:59	500 words	AI allowed
Outcomes assessed:
Out-of-class quiz	Week 03 Quiz Multiple-choice quiz taken via Canvas covering lecture and reading material	2%	Week 03 Due date: 20 Aug 2026 at 23:59	4 minutes	AI allowed
Outcomes assessed:
Out-of-class quiz	Week 04 Quiz Multiple-choice quiz taken via Canvas covering lecture and reading material	2%	Week 04 Due date: 27 Aug 2026 at 23:59	4 minutes	AI allowed
Outcomes assessed:
Out-of-class quiz	Week 05 Quiz Multiple-choice quiz taken via Canvas covering lecture and reading material	2%	Week 05 Due date: 03 Sep 2026 at 23:59	4 minutes	AI allowed
Outcomes assessed:
In-person practical, skills, or performance task or test	Practical Skill Assessment 1 Written practical test given during the LECTURE period. An alternative sitting for students with an academic plan and/or approved special consideration will be held on 10/09/26 at 12:00.	25%	Week 06 Due date: 07 Sep 2026 at 12:00	1 hour	AI prohibited
Outcomes assessed:
Out-of-class quiz	Week 07 Quiz Multiple-choice quiz taken via Canvas covering lecture and reading material	2%	Week 07 Due date: 17 Sep 2026 at 23:59	2 minutes	AI allowed
Outcomes assessed:
Out-of-class quiz	Week 08 Quiz Multiple-choice quiz taken via Canvas covering lecture and reading material	2%	Week 08 Due date: 24 Sep 2026 at 23:59	4 minutes	AI allowed
Outcomes assessed:
Out-of-class quiz	Week 09 Quiz Multiple-choice quiz taken via Canvas covering lecture and reading material	2%	Week 09 Due date: 08 Oct 2026 at 23:59	4 minutes	AI allowed
Outcomes assessed:
Out-of-class quiz	Week 10 Quiz Multiple-choice quiz taken via Canvas covering lecture and reading material	2%	Week 10 Due date: 15 Oct 2026 at 23:59	4 minutes	AI allowed
Outcomes assessed:
Out-of-class quiz	Week 11 Quiz Multiple-choice quiz taken via Canvas covering lecture and reading material	2%	Week 11 Due date: 22 Oct 2026 at 23:59	4 minutes	AI allowed
Outcomes assessed:
In-person practical, skills, or performance task or test	Practical Skill Assessment 2 Written practical test given during the LECTURE period. An alternative sitting for students with an academic plan and/or approved special consideration will be held on 29/10/26 at 12:00.	25%	Week 12 Due date: 26 Oct 2026 at 12:00	1 hour	AI prohibited
Outcomes assessed:

Assessment summary

The early feedback activity consists of a simple data analysis task based on the first two weeks' tutorials. Students who have difficulty with this (marked) task will be invited for additional support. The ten weekly quizzes will reinforce the readings and lecture material in each non-assessment week. There will be no quiz in Week 13. The two practical skill assessments are scheduled for one hour in the two-hour lecture time block and are invigilated by the unit coordinator. In each practical skill assessment, students will be required to interpret real-world statistical results of the kinds they are likely to encounter in their professional careers. Answers will be recorded via pencil-and-paper in traditional exam booklets. Students with an academic plan and/or approved special consideration who require personal administrations of the practical skill assessment will be offered an alternative assessment on the Thursday following each of the Monday assessments. The final project will give students an opportunity to apply the skills they have learned in class to a research project in an open (but structured) environment.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	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)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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 University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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	Challenges for quantitative social science	Lecture (2 hr)
Week 01	Unpacking quantitative methods	Tutorial (1 hr)
Week 02	Censuses and formal demography	Lecture (2 hr)
Week 02	Census data and the EARLY FEEDBACK TASK	Tutorial (1 hr)
Week 03	Foundations of social demography	Lecture (2 hr)
Week 03	Calculating fertility and life expectancy	Tutorial (1 hr)
Week 04	Sampling and survey design	Lecture (2 hr)
Week 04	Survey design activity	Tutorial (1 hr)
Week 05	Measurement	Lecture (2 hr)
Week 05	Question writing activity	Tutorial (1 hr)
Week 06	IN-CLASS ASSESSMENT and enrichment lecture	Lecture (2 hr)
Week 06	Administrative data: "Closing the Gap"	Tutorial (1 hr)
Week 07	Research design and existing data	Lecture (2 hr)
Week 07	World Values Survey online analyses	Tutorial (1 hr)
Week 08	Data structures and univariate statistics	Lecture (2 hr)
Week 08	Charts and tables in SPSS	Tutorial (1 hr)
Week 09	LABOUR DAY HOLIDAY and enrichment lecture	Lecture (2 hr)
Week 09	Countries as units of analysis	Tutorial (1 hr)
Week 10	Correlation and scatterplots	Lecture (2 hr)
Week 10	Bivariate analyses in SPSS	Tutorial (1 hr)
Week 11	The logic of multiple regression	Lecture (2 hr)
Week 11	Multiple regression in SPSS	Tutorial (1 hr)
Week 12	IN-CLASS ASSESSMENT and enrichment lecture	Lecture (2 hr)
Week 12	Studying remote Australia using ABS data	Tutorial (1 hr)
Week 13	Statistical significance and reading statistical tables	Lecture (2 hr)
Week 13	Preparation for the FINAL PROJECT	Tutorial (1 hr)

Attendance and class requirements

Attendance: According to Faculty Board Resolutions, students in the Faculty of Arts and Social Sciences are expected to attend 90% of their classes. If you attend less than 50% of classes, regardless of the reasons, you may be referred to the Examiner’s Board. The Examiner’s Board will decide whether you should pass or fail the unit of study if your attendance falls below this threshold.
Lecture recording: Most lectures (in recording-equipped venues) will be recorded and may be made available to students on the LMS. However, you should not rely on lecture recording to substitute your classroom learning experience.
Preparation: Students should commit to spend approximately three hours’ preparation time (reading, studying, homework, essays, etc.) for every hour of scheduled instruction.

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 of the required readings for this class are available in ebook format from the library:

Mark Mather, Linda A. Jacobsen, and Paola Scommegna. 2021. Population: An Introduction to Demography. Population Research Bureau.
Department of Economic and Social Affairs of the United Nations. 2021. Handbook on the Management of Population and Housing Censuses, Revision 2. United Nations.
W. Lawrence Neuman. 2014. Basics of Social Research, 7th edition. Pearson.
David De Vaus. 2014. Surveys in Social Research, 6th edition. Routledge.

Additional support on social statistics can be sourced from the unit coordinator’s free Wikibook at:

https://en.wikibooks.org/wiki/Social_Statistics

Additional readings and videos will be set weekly in 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 social science data is created
LO2. utilise social science data to make arguments
LO3. understand how social scientists use data in applied settings
LO4. become self-directed in the acquisition and use of data
LO5. apply social science data to real-world social problems.

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.

The unit has been completely redesigned this semester after a review of curriculum needs and student performance in 2025.

Additional information

Additional costs

Students are encouraged (but not required) to purchase a 6-month SPSS Grad Pack Base license for approximately $49. This will allow students to practice class activities at home. Full software access is, however, available in student computer labs.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

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

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Additional costs

Disclaimer

On this page

Useful links

SCLG3702: Social Inquiry: Quantitative Methods

Semester 2, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Additional costs

Disclaimer

On this page

Useful links