UWA Handbook 2024

Unit Overview

Description

We are currently experiencing a new quantitative revolution, with big data and computationally intensive methodologies transforming social science theory and practice. This unit provides students with the reasoning skills and tools with which to engage with, and take advantage of, the potentials of the increased capacity of data to describe and explain the social world. Students will learn introductory skills in turning experiences into data and evidence, fact checking and critiquing claims in the social sciences, framing theories about society in relation to evidence, and sharing their ideas using supporting data.

This unit is suited for social science or humanities students with no experience of studying mathematics, statistics, or data analysis. Fundamental concepts of statistical thinking will be introduced in a non-mathematical and intuitive manner. The unit will focus on embedding students in a problem-solving framework (problem-plan-data-analysis-conclusions (PPDAC)) with which to interrogate real world issues within a social science context. Topics will include organising numbers, understanding numerical summaries, visualising data, transforming data, and using data in critical thinking, as well as hypothesis testing and examining correlation and causality.

Students will practice their data analysis skills, including using data software programs and connecting data to social science theory, in regular workshop exercises.

Credit

6 points

Offering
(see Timetable)

Availability	Location	Mode
Semester 2	UWA (Perth)	Face to face

Details for undergraduate courses

• Level 1 core unit in the Social and Environmental Sustainability major sequence

Outcomes

Students are able to (1) demonstrate understanding of introductory statistical & data analysis concepts; (2) explain the role of statistics and data analysis within the social sciences; (3) conduct basic data-based social science research projects; and (4) apply introductory statistical concepts and data analysis skills to the theory and practice of social science.

Assessment

Indicative assessments in this unit are as follows: (1) workshop exercises; (2) policy briefing; and (3) team research project. Further information is available in the unit outline.

Student may be offered supplementary assessment in this unit if they meet the eligibility criteria.

Unit Coordinator(s)

Professor Paul Plummer

Unit rules

Prerequisites

Enrolment in

any major
or minor from the School of Social Sciences
or School of Humanities,
or permission from the unit coordinator

Contact hours

2 hours lectures per week and 2 hours workshop per week.

Note

This unit is not suitable for students who have attempted or completed Mathematics Methods ATAR or equivalent or higher, and is not considered equivalent to any mathematics, statistics, or data analysis unit offered by the School of Physics, Mathematics and Computing. It is not considered a mathematics unit for students pursuing a degree in Teaching.

Students interested in further study in mathematics, statistics, or data analysis are encouraged to explore units such as Mathematics Fundamentals (MATH1720), Science, Society and Data Analysis (SCIE1104), or Computational Thinking with Python (CITS1401), or consider enrolling in the Data Science minor.

Texts

Background resources:

Spiegelhalter, D (2019) The Art of Statistics: Learning from Data.  Pelican: Australia

Erickson, B., Nosanchuk, T (2002) Understanding Data. Open University Press. England.

Wickham, H., Grolemund, G. (2022) R for Data Science (https://r4ds.had.co.nz/index.html)

Ismay, C., Kim, A.Y (2022) Statistical Inference via Data Science ( https://moderndive.com/index.html)