PHCA9511A Social Statistics in Public Health – Part A

2020

4.5

On Campus
5 x 1-hour lectures per semester
4 x 2-hour computer labs per semester
20-hours on-line exercises per semester

Distance Online
5 x 1-hour online lectures per semester
28-hours on-line exercises per semester

1 of PHCA8511, PHCA8927, PHCA9511 has been successfully completed

Basic understanding of both the social determinants of health and epidemiological research design

Available to students enrolled in any graduate award

Assignment(s), Test(s)

Within public health, there is a need for researchers, practitioners and policy makers to understand and interpret statistical findings from research. This topic focuses on social statistics in contrast to more traditionally taught bio-statistics, with an underlying aim around exploring the social determinants of health. This topic uses publicly available public health datasets to introduce descriptive and inferential bivariate analyses. The learning will consist of a balance between acquiring an early understanding of social statistical techniques by applying such knowledge in practice, with a statistical software package (SPSS). In this way, students get the hands-on experience of working with a real public health dataset in order to manage data, present data descriptively, and to develop and test bivariate hypotheses.

The aims of the topic are to enable students to:

1. Understand the scope of the SPSS software package in social statistical analysis
2. Undertake appropriate data manipulation to undertake a range of descriptive and bivariate statistical analyses based on nominal, ordinal and interval level data, critically interpret the results of statistical analyses, and to gain confidence in relating them to relevant literature in their academic disciplines.

On completion of this topic, students will be expected to be able to:

1. Critically evaluate and demonstrate an informed understanding of the statistical outputs from hypothesis testing
2. Justify the use of descriptive and bivariate statistical techniques, including making informed decisions in choosing appropriate statistical approaches
3. Demonstrate conceptual understanding of a range of statistical techniques applicable to nominal, ordinal and interval level variables
4. Interpret the use of statistical techniques in published work.