Looking to find the best chart to explore your research data? Want to practice digitally creating a new chart type? Want to hear from experts from the field and practicing researchers on how they make effective visualizations? SAGE Research Methods: Data Visualization is a collection of resources that supports the teaching and independent learning about visualizing data. Watch videos of experts discussing the fundamentals of good design, practice creating your own visualizations with provided data with Datasets, choose the right chart for the your data with the Chart Gallery, get tips and design inspiration from our Expert Insights, and explore the range of visualization tools available in the Tools Directory.
Data visualization is a way of exploring complex patterns or large quantities of data that cannot be easily perceived by looking at a table of numbers or reading paragraphs of text. The goal of data visualization is to communicate information more clearly, and it does so by employing our innate ability to recognize visual patterns in our environment.
Some data visualizations are exploratory in that they are created before any analysis is done on the data. Looking at a visual representation of our dataset can give us clues about what to focus on during analysis.
Some data visualizations are communicative in that they are created in order to present our analysis findings to an audience. Using visual patterns to represent patterns in data can be an effective way of explaining complex results.
Ultimately, data visualizations can more effectively answer questions, tell stories and put forth arguments than words alone.
Further reading: Few, S. (2013). Data Visualization for Human Perception. In The Encyclopedia of Human-Computer Interaction, 2nd Ed. Retrieved from https://www.interaction-design.org
The first step in effective data visualization is making sure you're using the right chart/graph type for your specific data.
This overview about choosing the best type of graph for data frames the issue in terms of underlying statistical question. For instance, are you comparing values? Showing composition? Looking for trends? Interested in distribution? What you're trying to understand from your data can and should inform the type of graph you use to visualize and communicate the results.
This guide frames the decision of which chart type in terms of data type: numeric, categorical, mixed numeric and categorical, maps, network, or time series. Click through to read more about caveats associated with each type of chart.
Another important consideration is the colors you choose in order to best present your data and make the main points of a graph or chart easy for viewers to correctly figure out.
A quick overview of what to consider when choosing colors for different type of graphs. One important question is: what do you want to the viewer to take away from the graph?
A helpful resource for understanding how to effectively use color in visualization.