Let us know what you think!
When the COVID-19 pandemic began a couple of months ago, we at LRS began thinking about how we could help. What skills could we share that might be useful to library staff and our communities?  So many different sources were releasing charts and graphs to help us all understand what was happening, and we were all trying to process a lot of data every day. LRS created the Between a Graph and a Hard Place blog series to provide strategies for looking at all kinds of data with a critical eye—strategies that could be used in a library or in...

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Visualizing Data: Color
I love color. As long as I can remember, I have kept my crayons organized in rainbow order. It makes me happy to see them that way! It’s a little tedious with the magical 64 pack of crayons, but totally worth it. I am an extreme example, but humans in general are visual creatures. Color impacts how we perceive and understand visual information—including graphs, charts, and infographics.  A good data visualization combines a thoughtful display of the data with strong art and design principles, including color. Our brains are wired to pay attention to color, even if some of us perceive...

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Visualizing Data: the logarithmic scale
Welcome to part 2 on data visualizations. If you’re just joining us, we talked last week about how the y-axis can be altered to mislead a reader about the data. You can find that post here. Now, let’s jump right back into another big data visualization misunderstanding.  The goal of data visualizations is to allow readers to easily understand complex data, but sometimes it’s the data visualization that we don’t understand. Certain techniques are utilized because they are the best fit for the data—not the best fit for the reader—and that can cause quite a bit of confusion if we don’t...

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Visualizing Data: a misleading y-axis
With great power comes great responsibility—that’s how I feel about data visualizations. Good ones help readers quickly understand the data and can convey an important message to a lot of people. However, bad data visualizations can intentionally or unintentionally mislead, causing us to come to the wrong conclusions. In this multi-part post, we’ll unpack some of the most common mistakes and give you the tools to spot them.  Omitting the baseline Imagine you’re telling someone the plot of a novel, but you start in the middle. All of the sudden the protagonist slays the dragon, which is a huge jump from introduction...

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The right data for the job – part II
Hello, again! Are you ready to learn more about the right data for the job? We are reviewing the  qualifications of various data to answer different kinds of research questions, just like we would review job candidates’ qualifications for a job. Last week we talked about the importance of what data were collected and how they were collected. This week we’re going to consider the importance of definitions and what it means for data to be representative. I know you have been waiting anxiously to figure out what we were going to do with those hats, so let’s jump back...

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The right data for the job
Imagine that different types of data are different people that you are interviewing for a job. The job is to answer a specific research question. You want to know what their qualifications are—will they do a good job? Are their strengths a good match for the task? Like we’ve mentioned before, if there are issues with the underlying data, then there will be issues with the results. It’s important to consider if the data collected make sense for the question that was asked.  Today, the data “qualifications” we’ll be looking at are what kind of data were collected and how the...

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