PSY4210 · Statistics and data science for psychology

Overview

In this unit you will be provided with an applied understanding of statistics and data science methods commonly used in Psychological Sciences. The unit has an applied focus and emphasises learning both the theory and interpretation of statistics as well as the practical application of statistics and data management through programming and software (data science). You will engage in weekly workshops that scaffold your skills and gain feedback from other students. Afterwards, you will complete independent assessments to showcase your: development of technical data management and analysis skills; interpretation of data; writing and criticism; and understanding and application of statistical theory as it relates to complex real-world psychological problems. The content is contextualised through applied problems and examples from Psychological Science, including a diverse range of psychosocial and health-related variables, to help you understand how the tools can be used to support professional psychologists and research.

Areas of study: Psychology

Offerings

Assessment

The Handbook lists an examination for this unit.

Assessment in this unit includes hurdle assessment tasks. Failure of any hurdle assessment task may result in failure of the unit

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

The Handbook lists no prerequisite, corequisite or prohibition for this unit.

Learning outcomes

1. Critique psychological science research data presentation and analysis steps in accordance with the scientific method.
2. Ascertain commonly accepted advanced appropriate analysis techniques to answer questions in psychological science.
3. Apply modern software tools from current best practices in data science to manage and explore diverse psychological data and variables.
4. Interpret results from advanced statistical analyses.
5. Collaborate effectively and provide peer feedback to learn best data science practices.

Workload

2 hour workshop per week covering theory and software applications. The expected undirected/private study to be completed by students will be approximately 9 hours a week. This will incorporate students completing regular exercises to gain familiarity with software tools, readings, and preparation and completion of the assessments.

Student experience, not official rules. Nothing here changes what the Handbook says.