### Overview

I’ve been looking at various examples over at Stephen Few’s Perpetual Edge web site and one example (this one) really resonates with me as it deals with how to visualize Likert Scale questions.

For the past five years I’ve used Tableau to analyze literally thousands of survey questions, but I’ve never really been happy with how I presented Likert Scale questions results.

That said, a new version of Tableau has brought new capabilities…

But first, just what is a Likert Scale question?

You’re probably familiar with it, you just may not have known that it had a particular name.  The scale, developed by Rensis Likert (pronounced Lick-ert, not Lie-kert), applies quantitative measures to qualitative answers.  Consider the example below.

The “scale” part of the “Likert Scale” comes from applying values to each of the answers. Here’s a typical application:

Poor = 1
Fair = 2
Average = 3
Good = 4
Excellent = 5

I’ve purposely set up an example that asks ten questions of the survey taker (only six are shown above).  Our challenge will be to come up with a way to sort how the company / employee / product ranks based on responses to the survey.

Note: An interactive dashboard that presents all the different approaches may be found at the bottom of this blog post.

### Approach One – Pie Charts

Yuck.  One pie is bad, but ten pies?

In addition to not being able to fit ten pies on the screen, there isn’t an easy way to visually rank these things.  Sure, we can either label the pie slices or use color highlighting to reveal the percentages, but meaningful revelations will remain hidden.

There are very few cases where a pie chart will be your best bet for conveying information.  If you need convincing, I implore you to read Stephen Few’s Save Pie Charts for Dessert and this most excellent example from the Wikipedia entry on pie charts.

### Approach Two – Stacked Bars

This is somewhat of an improvement over pies in that I can fit everything neatly onto one screen and it’s easy to see really obvious places where the company / person / product is strong and weak. The downside is that we still need a legend and that it’s hard to sort the categories from strongest to weakest.

### Approach Three – Adjacent Bars

This one is easier on the eyes (at least my eyes) than the stacked bars and there’s no need for a color legend, but I can’t fit this all on one screen (even if you change the orientation to vertical) and it’s still not easy to rank from strongest to weakest.

### Approach Four – Percent Indicating Good or Excellent

I admit that I have used this approach (probably too often) as it fits on one screen, is easy to sort, and is easy to, well, “grok”.  It also answers the question “what is the percentage of people that have a favorable impression” of something.

Unfortunately, this presentation is incomplete bordering on dishonest as not only are we combining “Good” and “Excellent” into one measure, we’re ignoring any important variations that might exist on the low end of the scale (Poor, Fair, and Average.)

### Approach Five – Likert Values

Here we take advantage of the “scale” part of “Likert Scale” and apply numeric values to each of the five possible responses, and then take the average of all responses.

This approach fits on one screen, is easy to sort, and easy to understand.  It is essential, however, to include the scale values in the presentation (“Poor = 1”, “Fair = 2”, etc.)

But even with that, I think too much that is important to understanding the data is missing.

### Approach Six – Stacked Bars / Likert Values Combo

Here we show both the details and the roll-up scores. The chart fits on one screen, is easy to rank, and you can explore the details.

For example, if you use the color highlighting capability you can easily see how many people indicated “Excellent” or “Good”.

### Why Do Likert Scales Need to Have Adjacent or Linear Values?

Good question.  It turns out that you may reveal a lot more if you reconsider using values like 1, 2, 3, 4 and 5.  Maybe positive attributes (Excellent and Good) should have positive values and negative attributes (Poor and Fair) should have negative values?  Using Tableau’s way cool Parameter control feature we can set the values as we see fit.  The example below ditches the traditional approach and substitutes the following values:

Excellent = 5
Good = 3
Average = 0
Fair = -3
Poor = -5

Spreading out the scale this way really accentuates the differences.

### Where to Go From Here

An interactive dashboard with each of the visualizations may be found below.  Tableau users are welcome to download the source workbook and see how the visualizations were built.

Stephen Few's Likert example

Note: I know that Stephen Few has built some examples that I like even more than the combo chart.  The problem is that they are difficult (but not impossible) to render using my visualization tool of choice.  I’ve chosen instead to only show things that are easy to build using Tableau.

I’d be delighted to see any and all alternatives people come up with.  Please share!

### Background

My older brother is a founder of The National Coalition for Child Protection Reform (NCCPR), an organization working to help America’s vulnerable children by changing public policy concerning child abuse, foster care, and family preservation.

While NCCPR believes that far too many children are in foster care there are some cases where children really do need to be placed in substitute care. In those cases, not all “placement settings” are created equal.  In the overwhelming majority of cases, the least detrimental alternative, the one most likely to “cushion the blow” of foster care placement is placement with a relative, typically a grandparent.  This is commonly known as kinship foster care.

At the other extreme, the worst form of care is “congregate care” – a group home or an institution – where the child is denied any family at all.

Every year, the federal government asks the states for a “snapshot” of how many children they have in these and other “placement settings” on September 30, the last day of the federal fiscal year.  But the federal government publishes only the national totals.  Using the federal Freedom of Information Act, NCCPR has obtained the state-by-state totals for the most recent year available, 2009, and my brother asked if I could present this information in a visual, interactive format (click here to explore the visualization.)

### Goals

One of our goals was to show which states are the best in providing kinship care and which are the worst in overusing congregate care.

The chart below is map showing the proportion of children in kinship care.

It’s easy to glean that states such as Florida, Washington, and New Jersey excel in kinship placement while Tennessee, Virginia, and South Carolina lag (note that Hawaii, not shown, ranks first in this category).

By hovering over a circle you can see data for a particular state, in this case, Florida.

And clicking on a circle (or circles) presents a more details breakdown of information.

The dashboard also provides a simple mechanism that allows the viewer to switch gears and focus instead on congregate care.

By clicking the Rank Type filter (below)

The map display changes and we can see that Wyoming, Colorado, and Rhode Island are the worst culprits and that Washington, Oregon, and New Mexico should be chastised the least.

### Ranking

My brother tells me that journalists covering this topic love to know where certain states rank with respect to kinship care and congregate care.  By clicking the Placement Rankings tab at the top of the dashboard we see a list of 52 “states” (50 states, plus District of Columbia and Puerto Rico) ranked by Relative Placement (kinship foster care).

By clicking the “Group Home, Institution” column and clicking the Sort button at the bottom of the screen

We can see Congregate Care rankings, as shown below.

I encourage you to explore the interactive visualization yourself (click here) and also to visit the NCCPR web site as I’m very proud of what my brother is doing.