Your Ruby Slippers: five key data takeaways

Hi there, readers! We have so enjoyed having you on this data journey with us. The posts we’ve shared since March are an introduction to data literacy, and we’re wrapping up that theme today. Fear not! This series—Between a Graph & a Hard Place—isn’t going anywhere. We’re just starting a new theme, like the next chapter in a book. (We’re data people, but who can resist a book metaphor?)  

We hope that you’ve learned something—preferably lots of things—and will join us on the next leg of our journey. Based on surveying you, our readers, the new direction we’re taking is to share how you can actually do research and evaluation in the library. After today’s post, we’re going to post every other week. We love writing these, and they take time to write well. If you’re worried you’ll forget when we’re posting again, it’s easy to sign up here to get notified when we have a new post.

We’d like to give a good send off to this chapter and show you how all the posts tie together. As we review each post today, I want you to keep five big themes in mind. These key ideas apply to every area of data literacy and each post from the series connects to them. 

Five themes in data literacy:

  • The quality of research varies. Details matter, so take the time to think about them. 
  • Your common sense will take you far. Does what you’re reading make sense?
  • Our human brain has feelings, biases, and preferences. Stay aware of yours.
  • Researchers are also human. They have feelings, biases, and preferences too.
  • When considering what data mean, err on the cautious side. What do we know from these data? What is more of a guess?

These themes are your data literacy ruby slippers. You have them with you all the time, and if you start to feel lost or confused, they can show you the way home. You just have to remember you have them! With these big themes in mind, we’re ready to review data literacy.

How to compare apples to oranges

  • When data are compared, think carefully about what two things are being compared and if they are truly similar to each other.
  • One way to make things more comparable is to use per capita, or per person, data.
  • Comparisons can be messy. Keep your thinking cap on.

Habits of mind for working with data

  • Give yourself permission to struggle and get help.
  • Acknowledge your feelings about the topic.
  • Whether you like the data or not, that information gives you an opportunity to learn.

Do the data have an alibi?

  • The quality of the data matters.
  • Where were the data published and when were they collected?
  • Who the authors are is also important. What is their area of expertise? Why did they publish this?

What’s typical and why does it matter?

  • Means and medians are measures of what’s typical.
  • Knowing what’s typical can be very helpful for comparisons.
  • The mean (average of a data set) is impacted by extreme values, so sometimes the median (middle value in a data set) is more representative. 

Correlation doesn’t equal causation

  • Correlation is one way that two variables relate to each other.
  • A strong correlation is when we can predict with a high level of accuracy the values of one variable based on the values of the other. They co-occur. 
  • Causation is different because it’s a cause and effect relationship: we know that A leads to B. 

The right data for the job – part 1

  • Do the data collected make sense based on the research question?
  • What data were collected and how they were collected are both important.

The right data for the job – part 2

  • Definitions impact what data are collected and how they are interpreted. 
  • The data collected for research are usually a sample of a larger population.
  • To be representative, the sample needs to reflect the population in key ways.

Visualizing Data: a misleading y-axis

  • The y-axis (vertical axis) does not always begin at zero on a chart.
  • The y-axis may be shown on a larger or smaller scale (zoomed in or out).
  • Depending on how the y-axis is displayed, the data will look different—which can highlight or obscure differences between groups or changes over time.

Visualizing Data: the logarithmic scale

  • Logarithmic (or log) scales are another way to display the y-axis. 
  • On a log scale, the distances between intervals increase by a percentage: multiplying by x each time.
  • Log scales are useful because they show rates of change—the percent something increases or decreases.

Visualizing Data: color

  • Color can help you understand visual information, but it can also confuse or mislead you.
  • We have feelings about colors and their meanings, which are not always conscious.
  • Red holds a special place in our brain. It says “pay attention.” 

Visualizing Data: choosing the right chart

  • The best chart for showing change over time is a line or bar chart. 
  • The best chart for showing multiple variables is a bar chart.
  • The best chart for comparing something to the total is a pie chart.

Here we are, at the end of this chapter! We are delighted to have come this far. Knowing that these blog posts have been useful for you all makes us so happy. Please join us on July 29th to continue the journey. We look forward to seeing you then!