Working with data

Data analysis is the process of examining data to find answers to questions. There are many ways that you can analyse and display data. This module will give an overview of some common analysis methods and tools. The data can be read through and manually organised by you, or processed using software.

Get an overview of your data

1. Look through, or listen to, all the data you have gathered to get an overall idea of what you have gathered
2. Transcribe any audio content so it is ready to be organised. You can record directly in Word for web or upload an audio file to get a transcription. 
3. Think about how you can reveal patterns or meaning in the data
4. You may be able to sort the qualitative data in a way that it can be measured.

Descriptive data analysis

Descriptive analysis provides a summary of the data that has been gathered. For quantitative data this involves presenting the data visually in tables and charts, and measuring values such as the mean. For qualitative data the data is interpreted through grouping into categories or coding. Coding is the process of organising and sorting your data.

You can use one method or a mix of methods to do your analysis. Methods include:

• Classification — sorting the data into different kinds of things
• Statistical analysis — for qualitative data this could be counting different elements, such as keywords and phrases, to find out how often something is mentioned or has occured.
• Thematic analysis — looking for recurring themes in the data.

Thematic analysis of qualitative data

Looking for patterns or themes in your data can help to answer your original question or may lead to new questions. Descriptive coding is a useful technique for thematic analysis. Coding can be done by hand or by using software, such as NVivo (Find information on accessing and using NVivo in the table on Data and text analysis software on this page).

Descriptive coding by hand

 What does coding look like? (YouTube, 4m42s)

Statistical analysis of quantitative data

Statistical analysis methods can range from simple to complex. Simple methods include:

• Range — the difference between the highest and lowest value
• Minimum — the smallest value
• Maximum — the largest value
• Frequency — the number of times a certain value appears
• Mean — the total of the values divided by the number of values
• Median — the middle value of any data after they are put in order
• Mode — the most frequently occurring value.

Learn about:

• calculating percentiles
• confidence intervals.

You can use data analysis software to perform the statistical analysis.

Inferential data analysis

Inferential analysis involves using the information from the data to make judgements about a topic or issue. For example, the results from a small group could be used to infer something about a larger group, or the results could be used to predict what might happen. Inferential analysis of qualitative data usually requires more advanced statistical methods

 Choosing which statistical test to use (YouTube, 9m32s)

Which Stats Test is a question tool to help you narrow down the type of statistical test to use. (UQ login required).

Data visualisation

Tables, graphs, maps and charts are used to summarise and display data. Once you have done your analysis you need to think about the best way to present the data. Get tips on good and bad visualisations.

Examples of data visualisations

The Tudor Networks visualisation brings together 123,850 letters connecting 20,424 people from the United Kingdom’s State Papers archive, dating from the accession of Henry VIII to the death of Elizabeth I (1509-1603).

The following visualisation shows the average years of schooling per country since 1950.

Choosing a chart

• Chart Chooser is a tool to help you find the right chart to display your data. After you choose the type of chart, you can download an Excel or PowerPoint template and then insert your own data.
• Chartopedia has a guide for choosing the right chart type.
• How to choose the best charts for your infographic.
• Picking the right chart for your data (LinkedIn Learning, 1h19m) Main topics include getting to the key idea you're trying to communicate; finding the right standard chart for your data type; and brainstorming and experimenting to come up with alternatives to the standards.

Displaying data

• Our Data visualisation guide has information on techniques, tips and tools to display data.
• The Communication Learning In Practice for Scientists (CLIPS) website has guidance on communicating results with scientific graphs.
• The Data visualisation catalogue shows different information visualisation types.
• Look at the Periodic table of visualisation methods for ways to display your data.

Misrepresenting data

Graphs can make data easier to understand but they can also be used to misrepresent data. Check graphs carefully. The graph creator can manipulate the design to innacurately reinforce their own agenda.

Tools for analysing and visualising data

There are many tools available for analysing and visualising data. You may want to use a tool that you have some experience with already, like Excel or Google Sheets, or you may want to try using software that is specifically for data analysis.

Spreadsheet tools

These tools can be used to analyse and visualise data.

Data and text analysis software

These tools can be used to analyse and visualise data.

Geographic information systems (GIS) tools

GIS tools are used to capture, analyse and present spatial or geographical data.

Our Geographic information systems (GIS) guide lists other tools and software to use with spatial or geographical data.

Visualisation tools

These tools are specifically for visualising data. You will need to do your analysis first before using these tools. 

Our Data visualisation guide lists more tools for visualising data.