SSPS6001: Semester 1, 2026

Units SSPS6001 Semester 1 2026 [Normal day]

Unit outline_

SSPS6001: Quantitative Methods in Social Sciences

Semester 1, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Quantitative methods are vital to social science. This unit introduces students to commonly used techniques for collecting and analysing numerical data to answer empirical questions about social, cultural, and political phenomena. It addresses the description of data with graphs and tables, descriptive statistics, statistical models, hypothesis testing, and other topics. The unit is appropriate for beginners, who will gain perspective and confidence conducting their own quantitative research and critically understanding that of others. It is taught in a computer lab, giving students practical experience with statistical software.

Unit details and rules

Academic unit	Sociology and Criminology
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
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 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Research analysis	Final analytical paper Multivariate causal analysis of WVS data using SPSS programming tools	45%	Formal exam period Due date: 09 Jun 2026 at 23:59	2000 words	AI allowed
Outcomes assessed:
Data analysis	Marked tutorial task Early feedback task evaluating use of SPSS software to analyze WVS data	10%	Week 03 Due date: 09 Mar 2026 at 17:00 Closing date: 09 Mar 2026	500 words equivalent	AI allowed
Outcomes assessed:
Research analysis	Data analysis paper Regression analysis and written evaluation using SPSS to analyze WVS data	45%	Week 05 Due date: 27 Mar 2026 at 23:59	2000 words	AI allowed
Outcomes assessed:
= early feedback task ?

Assessment summary

There are three major written assignments, each requiring a mix of graphical and textual material.

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	Introducing the World Values Survey	Lecture (2 hr)
Week 01	Introducing the World Values Survey	Tutorial (1 hr)
Week 02	Means and medians; introduction to SPSS	Lecture (2 hr)
Week 02	Means and medians; introduction to SPSS	Tutorial (1 hr)
Week 03	Variance, Standard Deviation, and Z-scores	Lecture (2 hr)
Week 03	Variance, Standard Deviation, and Z-scores	Tutorial (1 hr)
Week 04	Simple linear regression: Correlation and R-squared	Lecture (2 hr)
Week 04	Simple linear regression: Correlation and R-squared	Tutorial (1 hr)
Week 05	Simple linear regression: Expected and predicted values	Lecture (2 hr)
Week 05	Simple linear regression: Correlation and R-squared	Tutorial (1 hr)
Week 06	Multiple linear regression	Lecture (2 hr)
Week 06	Multiple linear regression	Tutorial (1 hr)
Week 07	Statistical significance	Lecture (2 hr)
Week 07	Statistical significance	Tutorial (1 hr)
Week 08	Regression model building	Lecture (2 hr)
Week 08	Regression model building	Tutorial (1 hr)
Week 09	ANOVA and mixed models	Lecture (2 hr)
Week 09	ANOVA and mixed models	Tutorial (1 hr)
Week 10	Introduction to statistical programming	Lecture (2 hr)
Week 10	Introduction to statistical programming	Tutorial (1 hr)
Week 11	Logistic regression and introduction to limited dependent variable models	Lecture (2 hr)
Week 11	Logistic regression and introduction to limited dependent variable models	Tutorial (1 hr)
Week 12	Advanced limited dependent variable models	Lecture (2 hr)
Week 12	Advanced limited dependent variable models	Tutorial (1 hr)
Week 13	Interaction models and project work	Lecture (2 hr)
Week 13	Interaction models and project work	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. IMPORTANT: This unit is taught as a hands-on seminar / workshop and thus only limited recorded lessons are available. Attendance in-person is absolutely essential.
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

The supplemental textbook for this class, Social Statistics, is available free online at:

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

Necessary documentation for the class data is available online at:

https://www.worldvaluessurvey.org/wvs.jsp

Additional readings (both set and self-directed) will be assigned weekly throughout the semester.

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. Obtain a general grounding in the basic principles of quantitative research in the social sciences.
LO2. Achieve competence in the graphical and tabular presentation of quantitative social science data.
LO3. Achieve competence in analyzing social science data using linear statistical models.
LO4. Achieve confidence in reading and evaluating statistical claims made in published social science results.
LO5. Become familiar with basic principles of statistical programming.

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 restructured into a lecture + tutorial model with 3 hours' weekly teaching. This is in response to consistent student feedback that additional lecture time was required.

Additional information

Additional costs

Students are strongly encouraged to purchase a 6-month personal subscription to SPSS software for approximately $49 plus any processing fees. Alternatively, students may access SPSS for free in university 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

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