PUBH620 - Biostatistics

Year

Credit points

Campus offering

Prerequisites

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 regression analyses. 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 datasets 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 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.

On successful completion of this unit, students should be able to:

LO1 - Demonstrate specialised knowledge of statistical concepts and their application to public health practice (GA: 5)   

LO2 - Distinguish between different statistical tests, especially in terms of application and interpretation (GA: 4, 5, 6) 

LO3 - Perform appropriate statistical analysis using common statistical software and interpret the results (GA: 6, 8, 10) 

LO4 - Develop a sound statistical approach to the analysis and interpretation of public health data and communicate findings in an academic-standard output (GA 4, 8, 9) 

LO5 - Critique public health research on the basis of its statistical methods, analysis and interpretation (GA: 4, 5, 8)

Graduate attributes

GA4 - think critically and reflectively 

GA5 - demonstrate values, knowledge, skills and attitudes appropriate to the discipline and/or profession 

GA6 - solve problems in a variety of settings taking local and international perspectives into account

GA8 - locate, organise, analyse, synthesise and evaluate information 

GA9 - demonstrate effective communication in oral and written English language and visual media 

GA10 - utilise information and communication and other relevant technologies effectively.

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 

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 datasets 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.  

 Online mode  

In online mode, students acquire essential theoretical knowledge in biostatistics via a series of synchronous or asynchronous online lessons which include: recorded lecture content, online readings, online discussion forums and self-directed learning modules. Students are given the opportunity to attend facilitated synchronous or asynchronous online tutorial classes (virtual classroom) to participate in the construction and synthesis of this knowledge with other students to develop their ability to apply biostatistics principles and approaches to contemporary public health issues. 

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.

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 Task 1 , students are required to demonstrate their understanding by analysing a public health dataset. In Assessment Task 2, students are required to apply their biostatistical skills by preparing statistical methods and analysis for a peer reviewed journal article. Finally, in Assessment Task 3 students will critique and interpret the statistics in current published journal articles and reflect on how biostatistical knowledge will shape their practice in public health.  

All assessment tasks will be submitted electronically.  

Overview of assessments

Representative texts and references

Kirkwook, B.R., & Sterne, J.A.C. (2003). Essential medical statistics 2nd ed. Malden, Massachusetts: Blackwell Science [ACU ebook] 

Bush, H.M. (2012). Biostatistics: An applied introduction for the public health practitioner. Clifton Park, NY: Delmar Cengage Learning.  

Cook, A., Netuveli, G., & Sheikh, A. (2004). Basic skills in statistics: A guide for healthcare professionals. London: Class Publishing. 

Cresswell, J.W., & Plano Clark, V.L. (2011). Designing and conducting mixed methods research  (3rd ed.).  Thousand Oaks: Sage Publications. 

Gordis, L. (2014). Epidemiology (5th ed.). Philadelphia, PA: Elsevier/Saunders. 

Munro, B. (2005). Statistical methods for health care research (5th ed.). Philadelphia, Pa: Lippincott. 

Neuman, W. L. (2011). Social research methods: Qualitative and quantitative approaches (7th ed.). Boston: Pearson, Allyn and Bacon. 

Newell, R., & Burnard, R. (2011). Research for evidence-based practice in health care (2nd ed.). Chichester, England: Wiley-Blackwell. 

Pallant, J. F. (2016). SPSS survival manual: A step by step guide to data analysis using IBM SPSS (6th ed.). Crows Nest: Allen & Unwin. 

Rugg, G. (2007). Using statistics: a gentle introduction. Maidenhead, Berks: Open University Press.