Reply 1
Making sense of facts, numbers, and measurements is a form of art - the art of data visualization. There is a load of data in the sea of noise. To turn your numbers into knowledge, your job is not only to separate noise from the data, but also to present it the right way.
I would like to use Hierarchy charts for data representation. Hierarchy charts visualize a hierarchy, helpin to see patterns in your coding or view the attribute values of cases and files. Hierarchy charts represent your data as aggregated. Even if you have not turned on node aggregation, any node with child nodes will be represented on a hierarchy chart as a parent node including its children.
When you hover over an area of the chart, a tooltip displays information for that item and specifies the number of items coded and number of coding references that relate directly to the parent, as well as an aggregated figure of parent plus children.
Hierarchy charts size a node based on whether or not it has children-therefore any node containing child nodes may appear larger than its actual coding. Use the tooltip information to understand the data underlying an area. Hierarchy charts use size to convey meaning. You can use color to show additional information.
This means you can size items in the chart to compare them by one metric, and then color the items to compare them by a second metric-in the same chart.By default, hierarchy charts are sized by items coded, and colored by hierarchy. Use the Size By and Color
By options on the ribbon to change the way data is displayed on the chart.
What can I show in a hierarchy chart?
Files
Compare the amount of coding of your files-are some files more heavily coded than others?
Identify files with most coding references at specific nodes-for example, which files contain the most coding references to Economy?
Nodes
Compare the amount of coding at your nodes-do some nodes contain more coding references than others?
Visualize prominent themes in your project
Identify areas that need further investigation or research
Attribute values
If you have classified your files or classified your cases, you can:
Check that you have consulted a variety of files-for example, have I relied too heavily on journal articles that are more than ten years old?
View the demographic spread of your survey respondents
Reply 2
If we would like to visualize the data in charts we must make sure the chart we selected is suitable for the selected data. Depending upon the data chosen we may use different chart types. It is essential to know the purpose of making the charts. These charts will help people to understand the difficult data in simple ways when represent in the form of charts.
Categorical:
In this type of charts, we can compare and distribute the data. So, if we would like to do some comparisons with the selected data we can go ahead and use the bar charts.
If we are comparing the single category with values changing over a period, then bar charts are best suitable. It is suitable for large number of categories as well. Graphs are ideal for Comparisons, like a best five rankings, top 10 companies in the world, etc. They are valuable if your information names are extremely long. Maintaining them in order makes understandable.
Either list by esteem or, if that is not the quality, pick a rationale for the names that suits well, such as posting them one after another in order will help audience to understand clearly. The best example for this type of chart is comparing the sales of a company in last five years.