Tips on Interpreting Data Visualizations

Categories: Data Analysis, MentorSpace, SAGE Posts

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Collecting, analyzing, and reporting with data can be daunting. The person that SAGE Publishing — the parent of MethodSpace — turns to when it has questions is Diana Aleman – editor extraordinaire for SAGE Stats and U.S. Political Stats. And now she is bringing her trials, tribulations, and expertise with data to you in a monthly blog, Tips with Diana. Stay tuned for Diana’s experiences, tips, and tricks with finding, analyzing and visualizing data. View Diana’s blog HERE.

Previously, I’ve discussed best practices in creating data visualizations and explained how a visual representation of data simplifies the information you want to convey. These are great concepts to keep in mind when creating data visualizations, but what about when you are on the receiving end of a data visualization? Your ability to interpret the visualization may vary depending on the data used, how well created the visualization is, and even your own familiarity with data or data visualizations.

As a reader, your goal is to understand, interpret, and reflect on the information represented in a data visualization and then infer new information based on that assessment. However, this can be difficult to accomplish if you are not familiar with data or statistics. To that end, below are some tips on how to interpret a data visualization including questions and information to consider.

The breakdown: Six tips on reading a data visualization

Data visualizations can take on multiple formats and can represent an infinite number of information types and combinations. Because of this wide variability of possibilities, my suggestions are broad enough to apply to any kind of scenario.

  1. Establish what idea or claim the data visualization is trying to reinforce. Visualizations are not created for the fun of it (some enthusiasts might disagree) and are created with the purpose to use it as evidence. For instance, one visualization might aim to demonstrate that homeless populations are decreasing instead of increasing.
  2. Make explicit observations of the visualization. Quite literally, what do you see? Do you see any highs or lows? Is the map or chart coloring darker in some places than others? Things like that.
  3. What patterns can you discern? Patterns can present themselves as clusters, steady increases/decreases, consistent coloring on parts of a map, and so on. Patterns like these are usually where the takeaway of the data visualization lies.
  4. Consider other factors that may have shaped the data and therefore the visualization. What factors not measured in the data set could have affected how the data is represented? For instance, comparing homeless populations across countries may be affected by different definitions of what constitutes a homeless person.
  5. Reflect and interpret. Based on these patterns and other factors, what is the takeaway of the visualization and how does it support or undermine the claim being made? For example, if a trend line on homeless populations is rising year-to-year, does that support the claim that homelessness is no longer an issue?
  6. Infer further. What other information can you reason based on this interpretation? If homelessness is rising, then I can probably infer that the economy and employment are not doing so well.

Should I follow this thinking every time I come across a data visualization?

I mean, it can’t hurt! Of course, not every data visualization will require a step-by-step thought process like this – some visualizations are self-explanatory and the best visualizations are often the simplest. However, it’s always helpful to have an idea of where to start if you’re not too familiar with data or statistics. Nowadays, data visualizations are everywhere and because of that the ability to thoughtfully interpret them has become a critical skill to learn.

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