bump(s) chart, a/k/a slope graph. If you poke around online, you'll find a variety of examples and names. Some have multiple data points for each line...and some are simpler, like the one on the right.
Typically, the lines are labeled on each end, often with then name of the data series and sometimes with the data value. I do have a version of this one with the lines labeled, but since these represent real data points, it's best to keep things anonymous for this example.
But let me give you a little context here for what I'm showing. Each line represents a teacher---the entire chart shows the entire staff for a school. On the left is each teacher's percent of Ds and Fs assigned to students for the first semester of the 2013 - 14 school year. On the right is the value for the 2014 - 15 school year.
We want to use this chart to look at two things. First of all, what is the general trend within the school? In this case, most of the lines are sloping downward. This may connect to initiatives, such as changes in grading practices, tutorial options, or improvements in instruction. Whatever the story is behind this chart, it's looking positive.
Next, we want to consider the steepness of the slopes we observe. Sure, we could add a trendline, but if you're just using the chart for exploratory purposes, we can eyeball things. In this case, we might note that most of the downward sloping lines, especially for the upper percentages on the 2014 side of the house, have had significant decreases.
Typically, when I present these charts, I include a summary of the data. For example, between the 2014 and 2015 school years, 30 teachers assigned fewer Ds and Fs to students, 7 teachers had very little change in the percentage of Ds and Fs assigned to students, and 3 teachers showed an increase in the percentage of Ds and Fs assigned to students. Because these charts are new to many of the people in my audience, this brief summary is enough to get them oriented to the chart. They can then begin to focus on the details. This might start with the slope of the lines, but then I see them begin to dig into the labels: Are some teachers in new-to-them assignments this year? Are the lines showing little change all in one content area, such as math? What might we see next year---is there a goal around our percentages?
And now, a musical interlude...
We see a lot of increases compared to the other school; but, if you look at the scale on the lefthand side, you'll see that none have a higher percentage than the other school. Generally speaking, teachers in this school assign a lower percentage of Ds and Fs vs. the other school.
The overall changes at this location aren't as dramatic, either. The slopes are more gentle.
What might account for the differences? Again, you'd have to poke further using knowledge specific to the school: Is this a more veteran staff that is has more expertise or are more resistant to change? Are the increases due to an unexpected change in student population---were the enrollment boundaries changed?
You could, with additional information, make some other comparisons between the two schools. What if you built graphs just showing one department, such as math? It would make the charts less busy and comparisons between buildings a lot easier.
The big idea with these charts, of course, is to show the data. Sure, we could just write the summary and do a simple bar chart or line chart to compare totals...but we're missing a lot of the story in doing so. When we go bumpin', we get a much richer picture of what is happening.
Next time, we'll take a look at another way to show the data using cluster charts.
These charts are super-simple to make in Excel. Jon Peltier has an excellent tutorial on his web site. You can also download a template from the article I profiled in the last post.