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## Contents

## Your lit review

- Research into your domain and a focus topic (a public health intervention) within it
- Broaden your understanding of the domain
- Given a chance to learn more about a specific area
- What you need to do now - is re-focus on your research

## Your data

- Is it cleaned are all the variables ready to analyse?
- Make sure you have described all the section B and C data that is relevant to the final submission
- I.e. all the useful data re your participants
- Use tables and charts (pie charts for small numbers of categories)
- Histogram = continuous data
- Bar charts = categorical data

## Where are you going?

- Final product = BGDB submission - a journal style submission article
- Describe your data and testing hypotheses on your domain
- Research the science behind your hypotheses
- Interpret your data analysis results and discuss these findings in the light of your research - draw from both the science background and the SH project
- Eg do people with less immunisation have more infectious disease?

- Start off with a few research questions (do some brainstorming). Come up with questions that are interesting and relevant.
- Find some good journal articles about infectious disease
- Can use journal article referencing, tables etc, not APA

## What should you do next?

- Review your SH lit review submission and research any errors highlighted by your marker
- Review the formative feedback on your test analyses
- Focus on one/two interesting...

## Planning

- List all the tasks you need to do
- Set some key dates for key stages - e.g. starting/finishing of the: analysis, interpretation, research, drafting, editing, submitting
- Plan your group work carefully
- Use the strengths of your team and support each other!

## Trouble-shooting

Feedback from the formative and advice for this stage

- Some groups missed out the interpretation altogether!
- Correlation done very well on the whole - just make sure you mention r (direction and magnitude) as well as r squared. A fitted line is required.
- Forcing an unsuitable variable into a calculation or test (e.g. calculating the mean of diet type!)
- Poor choice of test for t-test/chi square. Basically mixing up the type of variable for the test required.
- Mixing up T-statistic and Levene's test results
- Poor interpretation of chi square test results
- Using terms incorrectly e.g. incidence, prevalence, rate, causal relationship (can't show incidence - not showing rates over time)
- Use association often

## Data analysis

- New QMP portal = the UNSW ResMap is available to help with your research process.
- Understanding your research question and/or hypotheses that you are developing is vital for your sensible actions here - we will discuss in class - but bring along some ideas re: research questions and hypotheses that you wish to test!
- Also be aware of:
- The data types that you have
- The numbers of variables that you wish to analyse
- And can you assume normality holds for your data?

## New QMP website gadget

## Types of data

- Variable
- Quantitative
- Discrete
- Continuous

- Qualitative
- Dichotomous
- Nominal
- Ordinal (strongly agree -> strongly disagree)

- Quantitative

## Analysis of the data

- The instructions ask for at least 4 variables - which variables should you use?
- The dataset has 162 variables and 533 cases

- Look at your research question, think about this

## Possibly hypothesis

- Are immunisation rates higher now than then? Does having immunisations protect against key diseases?