I’ll begin this blog article by answering the question that appears in the title. I’ve found that 100% bar graphs, designed in the conventional way, are only useful for a limited set of circumstances. Unlike normal stacked bars, the lengths of 100% stacked bars never vary, for they always add up to 100%. Consequently, when multiple 100% stacked bars appear in a graph, they only provide information about the parts of some whole, never about the wholes and how they differ. Therefore, they would never be appropriate when information about totals and the parts of which they are made are both of interest, though normal stacked bars often work well in this scenario. I’ve found that 100% stacked bar graphs are only useful in three specific situations, which I’ll describe in a moment.

I was prompted to write about this when I recently read the book titled “Storytelling with Data” by Cole Nussbaumer Knafic. Cole likes 100% stacked bars. Several appear in her book. When Cole and I met for lunch last week, shortly before departing I asked if she would be interested in discussing matters on which we apparently disagree and suggested 100% stacked bar graphs as our opening topic. She graciously welcomed the opportunity, so I began the discussion via email later in the week. Our discussion focused primarily on the following graph that appears in her book as an exemplar of graphical communication.

This graph displays a part-to-whole relationship between projects for which the goals were missed, met, or exceeded by quarter. A 100% stacked bar graph never serves as the best solution for a time series. Stacked segments of bars do not display patterns of change through time as clearly as lines. In this particular example, only the bottom bar segments, representing missed goals, do a decent job of showing the quarterly pattern of change. The top segments, representing exceeded goals, invert the pattern of change (i.e., the lower the segment extends, the higher the value is that it represents), which is confusing. The middle segments, representing met goals, encode the quarterly values as the heights of the segments, not their tops, which makes the pattern of change impossible to see.

The following line graph displays the data more effectively in every respect.

Despite the perceptual problems that I identified in Cole’s 100% stacked bar graph, she feels that it is superior to the line graph above. Her preference is rooted in the fact that the stacked bar graph intuitively indicates the part-to-whole nature of the relationship between missed, met, and exceeded goals. While it is true that a line graph does not by itself state, “these are parts of a whole,” this can be easily made clear in the title, as I did above. For Cole, the stacked bar graph’s ability to declare the parts of a whole nature of the relationship without having to clarify this in the title overcomes its perceptual problems.

Let’s move on to the three occasions when I believe 100% stacked bars are useful:

When the bars consist of only two segments (e.g., male and female) When we need to compare the sum of multiple parts among multiple bars When we need to compare the percentages of responses to Likert scales

Here’s an example of the first situation:

Because the bars are divided into two segments only (i.e., women and men), it is easy to read the values of each segment and to compare a specific segment through the entire set of bars. This comparison can be easily made because each segment is aligned through the entire set of bars (women to the left and men to the right). If a third segment were added, however, the segment in the middle would not be aligned to the left or right, which would make comparisons difficult.

I can illustrate the other occasion when 100% stacked bars are useful with the following example from Cole’s book:

The primary purpose of this graph is to compare the sum of customer segments 3, 4 and 5 in the “US Population” versus the sum of the same three customer segments in among “Our Customers.” Assuming that no other comparisons are important, the two 100% stacked bars do the job effectively. If I were creating this graph myself, however, I would be tempted to make a few minor adjustments. Assuming that the customer segments have actual names rather than numbers, which is usually the case, and that the specific order in which the segments appear above is not necessary, I would place the highlighted segments at the bottom of the stacked bars, as I’ve done below.

This gives the featured segments a common baseline, which makes the comparison of their heights easier. Although it isn’t necessary, I also placed the segment names next to both bars because the vertical positions of the segments are not aligned, which makes it easier to identify the segments on the right.

The final occasion involves the comparison of Likert scale responses (e.g., Very Dissatisfied, Dissatisfied, Neutral, Satisfied, Very Satisfied). Cole feels that a conventional 100% stacked bar handles this well, illustrated by the following example from her book:

This particular design does work well for the following purposes:

Comparing Strongly Disagree percentages Comparing the combination of Strong Disagree and Disagree percentages Comparing Strongly Agree percentages Comparing the combination of Agree and Strongly Agree percentages Reading the percentage values for Strongly Disagree Reading the percentage values for the sum of Strongly Disagree and Disagree

However, it does not work well for the following purposes:

Comparing Disagree percentages Comparing Neutral percentages Comparing Agree percentages Reading percentage values of the individual segments Disagree, Neutral, Agree, or Strongly Agree, because mental math is required Reading the percentage values for the sum of Agree and Strongly Agree, because mental math is required

Given these particular strengths and weaknesses, a 100% stacked bar graph of this design would work well to the degree that the audience only needs to access its strengths.

Variations on the design of 100% stacked bar graphs usually work better. Most of these variations display negative results (e.g., Strongly Disagree and Disagree) as negative values running left from zero and positive results (e.g., Agree and Strongly Agree) as positive values running right from zero. Here’s an example:

Designed in this way, differences between positive and negative results now stand out a bit more, the sum of Agree and Strongly Agree are easier to read, and the Neutral values are both easier to read and compare.

For some purposes, the Neutral results may be eliminated altogether, and for some it may be appropriate to split the Neutral results down the middle, displaying half of them as negative and half as positive, as follows:

In cases when it’s important to compare each individual segment from bar to bar rather than the sum of negative results (Disagree and Strongly Disagree) or positive results (Agree and Strongly Agree), a separate column of bars for each item on the Likert scale would work best, illustrated below.

Other than these few occasions when 100% stacked bar graphs are effective, I’m not aware of any other appropriate uses of them. If you’re aware of other good uses, please post and describe your examples in my discussion forum.

Take care,