Unit rationale, description and aim
Understanding, using and interpreting statistics is crucial to public health research and practice, particularly in monitoring health outcomes and decision-making processes about interventions. This unit will develop students' knowledge of fundamental statistical concepts, such as descriptive and inferential statistics, common statistical tests and statistical methods commonly used in public health. This will include hypothesis testing, estimation, associations, modelling relationships and prediction using different methods such as logistic regression. Throughout the unit, students will consolidate their understanding of statistical theory through its application to practice. While there are some formulae and computational elements to the unit, the emphasis is on interpretation and concepts. Besides the theoretical material, this unit will also enable students to run basic analyses using common statistical software. Using this software, students will analyse simulated public health data sets and then interpret the results obtained. Statistical understanding and analytical expertise developed by students during the unit will then be applied to practice through critical appraisal of statistical methods used in public health research.
Learning outcomes
To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.
Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.
Explore the graduate capabilities.
Demonstrate specialised knowledge of statistical c...
Learning Outcome 01
Distinguish between different statistical tests, e...
Learning Outcome 02
Perform appropriate statistical analysis using com...
Learning Outcome 03
Develop a sound statistical approach to the analys...
Learning Outcome 04
Critique public health research on the basis of it...
Learning Outcome 05
Content
Topics will include:
Fundamental statistical concepts and methods
- Types and levels of measurement of quantitative data and measures of central tendency and variability
- Probability distributions
- Hypothesis testing
- Statistical confidence: confidence intervals, p-values, statistical vs practical significance
- Common statistical tests: comparison of means (between two or more dependent or independent groups), proportions
- Variability and statistical inference; power and sample size; bias, confounding and adjustment
Application to public health practice
- Key measures of association in public health: relative risk, attributable risk, odds ratios
- Inferential statistics: correlation, linear regression, logistic regression, analysis of variance
- Use of statistical software to analyse quantitative datasets: common statistical tests
- Writing up statistical analyses: interpretation, requirements for expressing statistical results
- Critical appraisal of statistical methods in public health research: common tools and approaches
Assessment strategy and rationale
A range of assessment procedures will be used to meet the unit learning outcomes and develop graduate attributes consistent with University assessment requirements. In order to successfully complete this unit, students need to complete and submit three graded assessment tasks and obtain an aggregate mark of greater than 50%.
PUBH620 involves assessment tasks designed to introduce students to the broad range of activity involved in biostatistics. In Assessment 1 , students are required to demonstrate their understanding by analysing a public health dataset. In Assessment 2, students are required to apply their biostatistical skills by preparing statistical methods and analysis for a peer reviewed journal article. Finally, in Assessment 3, students will assume the role of a statistician and by way of critique demonstrate their understanding and knowledge of statistical concepts through consultation with an academic researcher.
All written assessment tasks will be submitted electronically.
Overview of assessments
Assessment 1: Written task Analysis of simulated ...
Assessment 1: Written task
Analysis of simulated public health dataset. This will enable students to develop an understanding of data analysis by analysing a public
health dataset.
20%
Assessment 2: Preparation of statistical met...
Assessment 2: Preparation of statistical methods and analysis for a peer-reviewed journal article. This will enable students to deepen their knowledge of data analysis by writing the methods and data analysis sections of a journal article.
40%
Assessment 3: Assuming the role of a statistician...
Assessment 3: Assuming the role of a statistician. This will enable students to reflect upon their learning in the unit and test their knowledge and understanding of how statistics can be applied to public health research.
40%
Learning and teaching strategy and rationale
Multi-mode
In multi-mode, students acquire essential biostatistical knowledge via a series of weekly face-to-face tutorials, which are supplemented by asynchronous online lectures that include recorded lecture content; online readings, online videoconferences and self-directed learning activities. The unit uses an active learning approach to support students as they gain knowledge of biostatistics and the applications of biostatistics in public health practice. Activities focus on analysis of simulated public health data sets or other material to provide context to student analysis and interpretation and an authentic learning experience. Students are provided with the opportunity to attend facilitated tutorial classes so as to participate in the development and synthesis of this knowledge with other students and deepen their level of understanding and engage in peer learning while receiving expert support for skill development.
Learning activities are designed to be suitable for both multimode and online deliveries to ensure students are achieving learning outcomes equitably. Learning content will be adapted between modes of delivery to account for current information and communication technologies.
The learning and teaching strategies of this unit are designed to allow students to meet the aims, learning outcomes of the unit, and graduate attributes of the University. Students will be expected to take responsibility for their learning and to engage actively with unit content and learning activities.