May 102017
 

May 10, 2017

Overview

Most organizations want to wildly exceed customer expectations for all facets of all their products and services, but if your organization is like most, you’re not going to be able to do this. Therefore, how should you allocate money and resources?

First, make sure you are not putting time and attention into things that aren’t important to your customers and make sure you satisfy customers with the things that are important.

One way to do this is to create a survey that contains two parallel sets of questions that ask customers to indicate the importance of certain features / services with how satisfied they are with those products and services.  A snippet of what this might look like to a survey taker is shown in Figure 1.

Figure 1 -- How the importance vs. satisfaction questions might appear to the person taking the survey.

Figure 1 — How the importance vs. satisfaction questions might appear to the person taking the survey.

How to Visualize the Results

I’ve come up with a half dozen ways to show the results and will share three approaches in this blog post.  All three approaches use the concept of “Top 2 Boxes” where we compare the percentage of people who indicated Important or Very Important (the top two possible choices out of five for importance) and Satisfied or Very Satisfied (again, the top two choices for Satisfaction).

Bar-In-Bar Chart

Figure 2 shows a bar-in-bar chart, sorted by the items that are most important.

Figure 2 -- Bar-in-bar chart

Figure 2 — Bar-in-bar chart

This works fine, as would having a bar and a vertical reference line.

It’s easy to see that we are disappointing our customers in everything except the least important category and that the gap between importance and satisfaction is particular pronounced in Ability to Customer UI (we’re not doing so well in Response Time, 24-7 Support, and East of Use, either.)

Scatterplot with 45-degree line

Figure 3 shows a scatterplot that compares the percent top 2 boxes for Importance plotted against the percent top 2 boxes for Satisfaction where each mark is a different attribute in our study.

Figure 3 -- Scatterplot with 45-degree reference line

Figure 3 — Scatterplot with 45-degree reference line

The goal is to be as close to the 45-degree line as possible in that you want to match satisfaction with importance. That is, you don’t want to underserve customers (have marks below the line) but you probably don’t want to overserve, either, as marks above the line suggest you may be putting to many resources into things that are not that important to your customers.

As with the previous example it’s easy to see the one place where we are exceeding expectations and the three places where we’re quite a bit behind.

Dot Plot with Line

Of the half dozen or so approaches the one I like most is the connected dot plot, shown in Figure 4.

Figure 4 -- Connected dot plot. This is the viz I like the most.

Figure 4 — Connected dot plot. This is the viz I like the most.

(I placed “I like most” in italics because all the visualizations I’ve shown “work” and one of them might resonate more with your audience than this one.  Just because I like it doesn’t mean it will be the best for your organization so get feedback before deploying.)

In the connected dot plot the dots show the top 2 boxes for importance compared to the top 2 boxes for satisfaction.  The line between them underscores the gap.

I like this viz because it is sortable and easy to see where the gaps are most pronounced.

But what about a Divergent Stacked Bar Chart?

Yes, this is my “go to” viz for Likert-scale things and I do in fact incorporate such a view in the drill-down dashboard found at the end of this blog post. I did in fact experiment with the view but found that while it worked for comparing one feature at a time it was difficult to understand when comparing all 10 features (See Figure 5.)

Figure 5 -- Divergent stacked bar overload (too much of a good thing).

Figure 5 — Divergent stacked bar overload (too much of a good thing).

How to Build This — Make Sure the Data is Set Up Correctly

As with everything survey related, it’s critical that the data be set up properly. In this case for each Question ID we have something that maps that ID to a human readable question / feature and groups related questions together, as shown in Figure 6.

Figure 6 -- Mapping the question IDs to human readable form and grouping related questions

Figure 6 — Mapping the question IDs to human readable form and grouping related questions

Having the data set up “just so” allows us to quickly build a useful, albeit hard to parse, comparison of Importance vs. Satisfaction, as shown in Figure 7.

Figure 7 -- Quick and dirty comparison of importance vs. satisfaction.

Figure 7 — Quick and dirty comparison of importance vs. satisfaction.

Here we are just showing the questions that pertain to Importance and Satisfaction (1). Note that measure [Percentage Top 2 Boxes] that is on Columns (2) is defined as follows.

Figure 8 -- Calculated field for determining the percentage of people that selected the top 2 boxes.

Figure 8 — Calculated field for determining the percentage of people that selected the top 2 boxes.

Why >=3?  It turns out that the Likert scale for this data went from 0 to 4, so here we just want to add up everyone who selected a 3 or a 4.

Not Quite Ready to Rock and Roll

This calculated field will work for many of the visualizations we might want to create, but it won’t work for the scatterplot and it will give us some headaches when we attempt to add some discrete measures to the header that surrounds our chart (the % Diff text that appears to the left of the dot plot in Figure 4.) So, instead of having a single calculation I created two separate calculations to compute % top 2 boxes Importance and % top 2 boxes Satisfaction. The calculation for Importance is shown in Figure 9.

Figure 9 -- Calculated field for determining the percentage of folks that selected the top two boxes for Importance.

Figure 9 — Calculated field for determining the percentage of folks that selected the top two boxes for Importance.

Notice that we have all the rows associated with both the Importance questions and Satisfaction “in play”, as it were, but we’re only tabulating results for the Importance questions so we’re dividing by half of the total number of records.

We’ll need to create a similar calculated field for the Satisfaction questions.

Ready to Rock and Roll

Understanding the Dot Plot

Figure 10 shows what drives the Dot Plot (we’ll add the connecting line in a moment.)

Figure 10 -- Dissecting the Dot Plot.

Figure 10 — Dissecting the Dot Plot.

Here we see that we have a Shape chart (1) that will display two different Measure Values (2) and that Measure Names (3) is controlling Shape and Color.

Creating the Connecting Line Chart

Figure 11 shows how the Line chart that connects the shapes are built.

Figure 11 -- Dissecting the Line chart

Figure 11 — Dissecting the Line chart.

Notice that Measure Values is on Rows a second time (1) but the second instance the mark type is a Line (2) and that the end points are connected using the Measure Names on the Path (3).  Also notice that there is no longer anything controlling the Color as we want a line that is only one color.

Combining the Two Charts

The only thing we need to do now is combine the two charts into one by making a dual axis chart, to synchronize the secondary axis, and hide the secondary header (Figure 12.)

Figure 12 -- the Completed connected Dot Plot.

Figure 12 — the Completed connected Dot Plot.

What to Look for in the Dashboard

Any chart that answers a question usually fosters more questions. Consider the really big gap in Ability to Customize UI. Did all respondents indicate this, or only some?

And if one group was considerably more pronounced than others, what were the actual responses across the board (vs. just looking at the percent top 2 boxes)?

Figure 13 -- Getting the details on how one group responded

Figure 13 — Getting the details on how one group responded

The dashboard embedded below shows how you can answer these questions.

Got another approach that you think works better?  Let me know.

Jan 112016
 

Overview

I spend a lot of time with survey data and much of this data revolves around gauging people’s sentiments and tendencies using either a Likert Scale or a Net Promoter Score (NPS) type of thing.

Examples

Here’s an example of gauging sentiment using a 5-point Likert scale.

Indicate how satisfied you are with the following:

00_Grid1

Here’s an example of measuring tendencies, using a 4-point Likert scale.

How often do you use the following learning modalities?

00_Grid2

So, what’s a good way to visualize responses to these types of questions?

Over the past ten years I’ve spent thousands of hours working on the best ways to show how opinion and tendencies skew one way or another.  I have found that in most cases a divergent stacked bar chart helps me (and more importantly, my clients) best see what’s going on with the survey responses.

In this blog posts we’ll

  • See an example of a divergent stacked bar chart (also called a staggered stacked bar chart)
  • Work through a data visualization improvement process
  • Show how to visualize different scales (e.g., NPS, Top 3/Bottom 3, 5-point Likert, etc.)
  • Show sentiment and tendencies over time
  • Present a dashboard that will allow you to experiment with different visualization approaches

Note: for step-by-step instructions on how to build a Likert-scale divergent stacked bar chart in Tableau, click here.

Divergent Stacked Bar vs. 100% Stacked Bar

Readers of my newsletter and folks visiting the web site may have seen my redesign of a New York Times infographic that showed the tendencies of politicians to lie or tell the truth.  Here’s the 100% Stacked Bar chart that appeared in the New York Times.

Figure 1 -- 100% stacked bar chart.

Figure 1 — 100% stacked bar chart.

Here’s the redesign using a divergent stacked bar chart.

Figure 2 -- Divergent stacked bar chart.

Figure 2 — Divergent stacked bar chart.

With both the 100% stacked bar chart and the divergent stacked bar charts the overall length of the bars is the same, but with the divergent approach the bars are shifted left or right to show which way a candidate leans. I, and others I’ve polled, find that shifting the bars makes the chart easier to understand.

How We Got Here — Likert Scale Improvement Process

Consider the table below that shows the results from a fictitious poll on the use of various learning modalities.

Figure 3 -- Table with survey results.

Figure 3 — Survey results in a table.

I can’t glean anything meaningful from this.

What about a bar chart?

Figure 4 -- Likert scale questions using a bar chart. Yikes.

Figure 4 — Likert scale questions using a bar chart. Yikes.

Wow, that’s really bad.

What about a 100% stacked bar chart?

Figure 5 -- 100% stacked bar chart using default colors.

Figure 5 — 100% stacked bar chart using default colors.

Okay, that’s better, but it’s still pretty bad as Tableau’s default colors do nothing to help us see tendencies that are adjacent. That is, “Often” and “Sometimes” should have similar colors, as should “Rarely” and “Never.”

So, let’s try using better colors…

(…and don’t even think about using red and green.)

Figure 6 -- 100% stacked bar chart using a more appropriate color scheme.

Figure 6 — 100% stacked bar chart using a more appropriate color scheme.

This is certainly an improvement, but the modalities are listed alphabetically and not by how often they’re used. Let’s see what happens when we sort the bars.

Figure 7 -- Sorted 100% stacked bar chart with good colors.

Figure 7 — Sorted 100% stacked bar chart with good colors.

It’s taken us several tries, but it’s now easier to see which modalities are more popular.

But we can do better.

Here’s the same data rendered as a divergent stacked bar chart.

Figure 8 -- Sorted divergent stacked bar chart with good colors.

Figure 8 — Sorted divergent stacked bar chart with good colors.

Of course, we can also look take a coarser view and just compare Sometimes/Often with Rarely/Never, as shown here.

Figure 9 – Divergent stacked bar chart with only two levels of sentiment.

Figure 9 – Divergent stacked bar chart with only two levels of sentiment.

I find that the divergent approach “speaks” to me and it resonates with my colleagues and clients.

Experiments using Different Scales

A while back Helen Lindsey was kind enough to send me some data that contained responses to some Net Promoter Score questions.  Specifically, folks were asked to rate companies/products on a 0 to 10 or 1 to 10 scale.

Figure 10 -- The classic Net Promoter Score (NPS) question

Figure 10 — The classic Net Promoter Score (NPS) question

We compute NPS by subtracting the percentage of folks that are promoters (i.e., people who responded with a 9 or a 10), subtracting the percentage of folks that are detractors (i.e., people who responded with a 0 through 6) and multiplying by 100.

But sometimes my clients have questions that are on a 10 or 11-point scale but instead want to compute the percentage of folks that responded with one of the top three boxes minus the percentage of folks that responded with the bottom three boxes.

I realized that the Lindsey data set could provide a type of “sandbox” where we could experiment with different sentiment scales including NPS, Top 3 minus Bottom 3, 5-point Likert, 3-point Likert, and 2-point Likert.

Let’s look at the results of some of these experiments.

NPS

Here are two ways we can visualize NPS data.  The first shows the percentages of people that fall into the three categories.

Figure 11 -- NPS showing percentages

Figure 11 — NPS showing percentages

Here’s the same view, but with the NPS score superimposed over the divergent stacked bars.

Figure 12 -- NPS with score superimposed

Figure 12 — NPS with score superimposed

NPS over Time

It turns out that divergent stacked bars are great at showing NPS trends over time.  Here’s a view using percentages.

Figure 13 -- Divergent stacked bar showing NPS over time with percentages

Figure 13 — Divergent stacked bar showing NPS over time with percentages

Here’s the same view but with the score superimposed.

Figure 14 -- Divergent stacked bar showing NPS over time with scores

Figure 14 — Divergent stacked bar showing NPS over time with scores

Note – for some other interesting treatments of showing sentiment over time, see Joe Mako’s visualization on banker honesty.

Net = Top 3 minus Bottom 3

Let’s take the same data but divide it into the following buckets:

  • Positive = Top 3 Boxes
  • Neutral = Middle 4 Boxes
  • Negative = Bottom 3 Boxes

Here are the associated visualizations.

Figure 15 -- Top 3 / Bottom 3 showing with percentages

Figure 15 — Top 3/Bottom 3 showing with percentages

Figure 16 -- Top 3 / Bottom 3 with scores

Figure 16 — Top 3/Bottom 3 with scores

Five, Three, and Two-Point Likert Scale Renderings

Let’s suppose that instead of asking a questions on a 1 through 10 scale we instead asked folks to select one of the following five responses:

  • Strongly disagree
  • Disagree
  • Neutral
  • Agree
  • Strongly agree

Here’s the same NPS data but rendered using a five-point Likert scale.

Figure 17 -- Divergent stacked bar chart showing all responses

Figure 17 — Divergent stacked bar chart showing all responses

And here’s the same data, but divided into positive, neutral, and negative sentiments (3-point Likert).

Figure 18 -- Divergent stacked bar showing positive, neutral, and negative

Figure 18 — Divergent stacked bar showing positive, neutral, and negative

Finally, here’s the same data, but only showing positive and negative sentiments (2-point Likert).

Figure 19 -- Divergent stacked bar showing just positive and negative

Figure 19 — Divergent stacked bar showing just positive and negative

Try it yourself

Below you will find a dashboard that allows you to explore different combinations of the 1 to 10 scale.

I strongly recommend you do NOT give your audience all these scaling options;  these are here for you to experiment and see how the visualizations and ranking change based on what scales you use.  The only option I would present to your audience is the ability to toggle back and forth between percentages and scores.

May 112015
 

Much thanks to Susan Ferrari for exposing me to the concept of Net Promoter Score, Susan Baier for encouraging me to blog about it, and Helen Lindsey for providing anonymized NPS data.

Overview

My wife and I recently went out to a restaurant to celebrate our anniversary.  Accompanying the check was a survey card with three questions, one of which looked like this.

Figure 1 -- The classic Net Promoter Score question

Figure 1 — The classic Net Promoter Score question

We both agreed that the restaurant was very good, if not excellent, and that we would indeed recommend it to friends.  My wife suggested we circle the “8”.

I told her that if we were enthusiastic about recommending the restaurant we should give it a “9” as a 7 or 8 would be tabulated as a “neutral” or “passive” response.

She looked at me quizzically and asked why an “8” would be considered neutral.

I then explained how the Net Promoter Score works.

Understanding the Score

Respondents are presented with the question “Using a scale from 0 to 10, would you recommend this product / service to a friend or colleague?”

  • Anyone that responds with a 0 through 6 is considered a Detractor.
  • Anyone that responds with a 7 or 8 is considered a Passive (or Neutral).
  • Anyone that responds with a 9 or 10 is considered a Promoter.

The Net Promoter Score (NPS) is computed by taking the percentage of people that are Promoters, subtracting the percentage of people that at Detractors, and multiplying that number by 100.

How to compute NPS, courtesy B2B International.

Figure 2 — How to compute NPS, courtesy B2B International.

If you are like me (and my wife) you’re probably thinking that a “6” is a pretty good score and that it shouldn’t be bunched among the detractors.

I’m not going to get into a debate about NPS methodology and its usefulness, but I do want to show you some good ways to visualize NPS data.

The Problem with the Traditional Presentation

Consider this snippet of NPS survey data with responses about different companies from people in different roles.

Figure 3 -- Raw NPS data about different companies from people with different occupations.

Figure 3 — Raw NPS data about different companies from people with different occupations.

If we just focus on the NPS and not the components that comprise the NPS we can produce an easy-to-sort bar chart like the one shown here.

Figure 4 -- Traditional way to show NPS

Figure 4 — Traditional way to show NPS

Yes, it’s easy to see the company D has a much higher NPS than company H, but by not showing the individual components – and in particular the Neutrals / Passives –  we’re missing an important part of the story as the Neutrals / Passives are right on the cusp of becoming promoters.

For example, a Net Promoter Score of 40 can come from

  • 70% Promoters and 30% Detractors
  • 45% Promoters, 50% Passives, 5% Detractors

Same score, big difference in makeup.

An Alternative Approach to Displaying NPS Results

Consider the dashboard below which presents the data as a divergent stacked bar chart.

Figure 5 -- NPS dashboard with toggle to show percentages and score.

Figure 5 — NPS dashboard with toggle to show percentages and score.

The chart is easy to sort and you can also see that Company B and Company F have a relatively large group of Neutrals.

That said, being able to see the NPS score is very useful so the dashboard (see working version at the end of this post) has a toggle that switches between percentages and the score, as shown below.

Figure 6 -- Divergent stacked bar chart with NPS overlay.

Figure 6 — Divergent stacked bar chart with NPS overlay.

Note that the NPS divergent stacked bar chart is just a variation on a Likert scale divergent stacked bar chart.  You can find an explanation of how to build this type of visualization here.

What’s Next?

We now have what I think is a more insightful way to visualize Net Promoter Score data.

But clients and readers of my blog have asked me to address some of these questions as well:

  • How do you show the difference in NPS, or just the difference in percentage of promoters, between this quarter and the previous quarter?
  • If there is a difference, is the difference statistically significant?
  • What’s a good way to visualize and analyze NPS over time?

I will be addressing these issues in an upcoming post.  Stay tuned.

Mar 302015
 

Overview

Tableau 9.0 includes a built-in data prepping tool that makes reshaping survey data so it plays nicely with Tableau a much smoother experience than using the Tableau Excel add-in.  While this new feature won’t replace by trusty copy of Alteryx (for reasons that I explain later in this post) there are many occasions where Tableau’s new pivot feature will be more than adequate.

In this post I will walk through using the new pivot feature along with “temporary” blending to create a solid framework for using survey data with Tableau.

Special thanks to Susan Baier for bringing this to my attention and Jonathan Drummey for showing me Tableau’s Create Primary Group feature.

So, what do we have here?

Note: if you want to follow along you can download the Excel file here.

Consider an Excel workbook that contains two sheets.  The first sheet has the survey results, a snippet of which is shown here.

Figure 1 -- Some raw survey data

Figure 1 — Some raw survey data

Notice the format: one row for each survey respondent and a separate column for each question in the survey where each question is identified with a Question ID (e.g.,  Q0, Q1, Q2, Q134a, etc.).

Column A contains a unique ID for each survey taker, Columns B through D contain demographic information, and Column E contains a weight for each survey respondent.

The second sheet maps each Question ID to a human-readable version of the question and groups related questions into logical buckets.

Figure 2 -- Helper file that maps each Question ID to the wording of the question from the survey

Figure 2 — Helper file that maps each Question ID to the wording of the question from the survey

Note that when I first blogged about survey stuff I didn’t use a helper file but now I won’t take on a project without creating one as I don’t want to spend time aliasing hundreds of question IDs.  The Grouping column also makes is much easier to select related questions and visualize them together.

The data wants to be tall and thin

Anyone who has read up on the subject know that life with survey data and Tableau is a lot easier when the data is reshaped so let’s see how to do this with Tableau 9.0.

  1. In Tableau, connect to the data source and the sheet that contains the data you want to reshape and visualize. This is what it looks like on my screen.

    Figure 3 -- Survey data prior to pivoting (reshaping)

    Figure 3 — Survey data prior to pivoting (reshaping)

  2. Select the fields you want to merge / pivot / reshape, in this case everything except the Resp ID, demographic fields, and Weight field.
  3. Click in any of the highlighted fields and select Pivot. Tableau will combine the 20+ fields into two fields, as shown here.

    Figure 4 -- Data after it has been pivoted

    Figure 4 — Data after it has been pivoted

  4. Rename the first field Question ID and the second field Value.

    Figure 5 – Pivoted fields renamed

    Figure 5 – Pivoted fields renamed

  5. Indicate whether you want an extract (a good idea when Excel is the data source) and go to the Tableau worksheet.
  6. Drag Question ID onto the rows shelf. Your screen should look like this.

    Figure 6 -- Reshaped data in Tableau.  Instead of 20 measures for each question we have only one measure.

    Figure 6 — Reshaped data in Tableau. Instead of 20 measures for each question we have only one measure.

Creating the temporary blend

Now we need to connect and relate the Helper file to our pivoted survey data.  We will do this with a blend, but then use a very slick feature of data blending that will allow us to ditch the secondary data source. Here are the steps.

  1. Click the Add New Data source tool.8_dataSource
  2. Connect to the Helper File sheet from the same Excel workbook and indicate whether or not you want to create an Extract (of course you do!)

    Figure 7 -- The secondary data source

    Figure 7 — The secondary data source

  3. Return to the Tableau worksheet.
  4. Drag the Grouping field to the left of Question ID on the rows shelf, and Wording to the right, as shown below.  Note that you don’t *have* to do this but it’s always useful to see if the hierarchy is working correctly.

    Figure 8 -- Blended Data

    Figure 8 — Blended Data

  5. Right-click the Grouping pill on the Rows shelf and select Create Primary Group.
  6. Rename the group Grouping as shown below.

    Figure 9 -- Leveraging the blend to create an ad-hoc group based on Question ID fields.

    Figure 9 — Leveraging the blend to create an ad-hoc group based on Question ID fields.

  7. Click OK.
  8. Right-click the Wording pill on the Rows shelf and select Create Primary Group.
  9. Rename the group Wording and click OK.
  10. Click the primary data source (the one from which we initially selected Question ID). Notice the two groups that Tableau generated for us.

    Figure 10 -- Tableau-generated groups

    Figure 10 — Tableau-generated groups

At this point we no longer need the secondary data source as the primary source now has groups that map and alias the Question IDs.  Very slick.

Seeing this in action

Now that we have the groups it’s easy for us to do some very quick analysis.  For example, let’s suppose we want to see the average Likert scale score for the collection of Likert scale questions.

  1. Create a new worksheet.
  2. Drag Grouping into the Filters shelf and select the collection of questions you want to view, in this case Likert Set 1.

    Figure 11 -- The Grouping group makes is easy to indicate which sets of related questions you want to examine.

    Figure 11 — The Grouping group makes is easy to indicate which sets of related questions you want to examine.

  3. Drag Wording to Rows.
  4. Right-Drag Value to Columns and select AVG(Value).
  5. Sort in descending order.

Isn’t this great?  We didn’t have to go groping around for the right Question IDs and we didn’t have to alias anything.

So, are there any shortcomings?  Is this blend approach as good as being able to join the pivoted data with the helper file?

Yes, there are shortcomings

There are several things that a join will give us that we can’t get with a blend.

You cannot refer to the group in a calculated field

You can’t refer to a group in a calculated field, so something like this won’t be available:

IF [Grouping] =”Things you Measure” then [Value] END

You need to update the group members if you add new questions

Tableau’s generation of the primary group is much like populating the members of a parameter with the members of a field.  Tableau will do it when you click a button, as it were, but it won’t update the list automatically.

If you end up adding new questions to your survey or reorganizing how questions are categorized in your helper file will either need to regenerate the primary data source groups or manually edit them.

You cannot combine text results with numeric results

This is one of the major “gotchas” for me, at least for larger surveys.  With most commercial survey systems you can download the data in a label format or a numeric format.  For example, when downloaded as labels survey responses might look like this:

Strongly disagree
Disagree
Neutral
Agree
Strongly agree

When downloaded as numbers the same responses would look like this:

1
2
3
4
5

I find I like to have both label and numeric responses, so I pivot / reshape both sets of data and then join them together using Question ID and Response ID.  Using Alteryx I can perform the join but I cannot do it with Tableau 9 and pivoted data.

Conclusion

For complex surveys where I need to do a fair amount of data cleanup and need both next and numeric values I’ll continue to use Alteryx.  For shorter surveys where I don’t need to do a lot of prep work and where either labels or numeric values will suffice, Tableau 9.0’s new pivot feature suits me just fine.  It’s a great addition to a great product.

 Posted by on March 30, 2015 2) Visualizing Survey Data, Blog Tagged with: , , ,  29 Responses »
Sep 162014
 

Finally, a good use for packed bubbles!

The Problem

I recently received a query from a client on how to compare responses to one question with responses to another question when both questions have possible LIkert values of 1, 2, 3, 4, and 5.  That is, if you have a collection of questions like this:

01_LIkert

How would you show response clusters when you compare “Good Job Skills” against “Likes the Beatles”?

This question is particularly applicable if you are a provider of goods and services and you want to see if there is alignment or misalignment between “how important is this feature” and “how satisfied are you with this feature”.

Note: There’s a Tableau forum thread that has been looking into this issue as well.  Please see http://community.tableausoftware.com/thread/137719.

So, how can we fashion something that helps us understand the data?

Before we get into the nitty gritty here’s a screen shot of one of the approaches I favor.  Have a look to determine if reading the rest of the blog post is worth the effort.

02_PreviewResults

Still reading?  Well, I guess it’s worth the effort.

The Traditional Scatterplot Approach

Consider the set up below where we see how Tableau would present the Likert vs. Likert results in a standard scatterplot.

03_TradScatterPlot

So, what is going on here?

There are a total of nine Likert questions available from the X-Question and Y-Question parameter drop down list boxes.  Our desire here is to allow us to compare any two of the nine at any time.

The “meat” of the visualization comes from the SUM(X-Value) on the columns shelf and SUM(Y-Values) on the rows shelf where X-Value and Y-Value are both defined as

IF [Wording]=[X-Question] then [Value]+1 END

This translates into “if the selected item from the list is the same as one of the questions you want to analyze, use the [Value] for that question”. Note that [Wording] is the same as [QuestionID] but with human readable values (e.g., “Likes the Beatles” instead of “Q52”)

We use [Value]+1 is because the Likert values are set to go from 0 to 4 instead of 1 to 5, and most people expect 1 to 5.

We can use SUM(X-Value) and SUM(Y-Value) because we have Resp ID on the Details shelf.  This forces Tableau to draw a circle for every respondent.  The problem is that we have overlapping circles and even with transparency you don’t get a sense of where responses cluster. Yes, it is possible with a table calculation to change the size of the circle based on count but we’ll I’ll provide what I think is a better approach below.

A note about the filters: The Question filter is there to constrain our view so that we only concern ourselves with Likert Scale questions.  It isn’t necessary but is useful should we be experimenting with different approaches.  The SUM(X-Value) and SUM(Y-Value) filters remove nulls from the view.

Packed Bubbles to the Rescue

I’m not a big fan of packed bubbles (see this post) but for this situation we can use them and get some great results, as shown below.

04_BubbleScatterPlot

I’ve made a couple of changes to the traditional scatterplot visualization the most important being SUM(X-Value) and SUM(Y-Value) are now discrete and we get a trellised visualization instead of a continuous axis.  Note that I had to change the sort order of the Y-axis elements so that they appear in reverse order (5 down to 1).

I got the packed bubbles by placing CNTD(Resp ID) on the size button. This assures that each bubble is the same size and triggers Tableau’s packing algorithm.

Note that I also added an on-demand “Drill down” so that you can color the circles by different demographic dimensions.

I’ve experimented with this with some large data sets and Tableau does a great job with packing the bubbles intelligently.

What About Trend Lines?

Since we are using discrete measures on the rows and columns shelves we cannot produce trend lines.  When I first started this project I experimented with more traditional jittering and was able, with a fair amount of fuss and bother, to produce this.

05_TrendLineExample

A special thanks to Jeffrey Shaffer who provided a link on how to create pseudo-random numbers in Tableau (thank you, Josh Milligan).

I prefer the example that doesn’t require the jittering, but if you need to trend lines or if you prefer the jittered look I’ve included the example in the downloaded packaged workbook (see below).

It also occurred to me that the trend line would be based on the jittered values and not the actual values.  The same workbook contains a “home grown” trend line based on the actual values (courtesy of Joe Mako). It turns out the jittered trend line is almost identical to the non-jittered trend line so I suspect you won’t need to take the “home grown” approach.


Update

I received a number of comments here and on LinkedIn about the “drill down / break down” capability and that it is hard to see the percentage of dots by category.  For example, if you break down by generation do the dots for one generation cluster more in one part of the trellis than in others?

I thought that in this case having a different-colored bubble per category where the size of the bubble was proportionate to the percentage of responses within that category made sense.

Size by Category

I thought building this would be easy, but I needed to call in the heavy artillery (Joe Mako).

I’ll blog about the solution later. In the meantime the packaged workbook below contains this additional approach.