Apr 172012


Here’s something you are welcome to try – have a friend or colleague go to a Tableau Public URL that contains multiple tabs; then see how many people realize that there is in fact more than one view / dashboard available for him/her to explore.

I’ve created hundreds of dashboards with multiple tabs and the bottom line is most people will not notice the tabs.  This is why my dashboards have navigation links; i.e., arrows that people can click to move forward and back).

The problem is that crafting these forward / back buttons is very time-consuming; each one has to be “hand-chiseled” as it were.  Indeed, I spend as much time on the cosmetics / usability component of dashboard development as I do building the actual visualizations.

Compounding my frustration with this is that I really have little or no use for the tabs along the top, but the navigation buttons won’t work unless the tabs are visible.

See for yourself.  Go to http://public.tableausoftware.com/views/DataRevelations_CTO_CompEquity_2012/CTOCompensationandEquityBenchmarks2011 (this is a work in progress) and check out the navigation buttons.  I think they are reasonably intuitive and discoverable, but they won’t work unless the tabs (which are not readily discoverable) are visible.

So, I respectfully submit a feature request to the folks at my favorite software company – please make it easy for me to generate a clickable table of contents, and please make it easier to build forward / back buttons.


… this request is a distant second to my top Tableau feature request (let’s all chime in together): Dashboard-level filters.


 Posted by on April 17, 2012 1) General Discussions, Blog 7 Responses »
Apr 162012

Note: While the information in this blog is useful,  I’ve discovered some better ways to perform intra-question analysis; I just haven’t blogged about them yet.  Feel free to nag me.


So, I thought I was all done with this subject (see http://www.datarevelations.com/using-tableau-to-visualize-survey-data-part-2.html) but a couple of blog readers (Matt and Tony) “busted” me and pointed out that my approach did not address a way to conduct intra-question analysis.  That is, we could cut Likert scale and check-all-that-apply questions by gender, location, etc., but there was not an easy mechanism to compare responses for people that answered “yes” to one question with their Likert scale opinions on another question.  To put it another way, we did not have a way to see whether or not folks that answered “no” to voting in the next election strongly agree with the statement that a candidate is good at playing jazz.

I’ll confess that I don’t perform this type of analysis that often, but when I do, an addition to drinking Dos Equis, I find that the most interesting insights usually come from a scatterplot analysis where, for example, we compare the responses for annual household income with how much respondents are willing to pay per month for Internet service.

Note: If you have not already done so, please read Part 1 and Part 2.

Further note: click here to download the sample survey data.

Final note: The interactive visualization may be found at the end of this blog post (you’ll find the download link there as well).


What it Looks Like When it’s Done

Consider the example below where I compare responses to the question “Will you vote in the upcoming election?” with respondents’ Likert scale opinions on a candidates’ ability to play jazz, their grace under pressure, consistency in eating all his/her vegetables, and intelligence.

Comparing plans to vote in next election with several Likert-scale questions

Notice that I’m using a staggered Likert scale visualization to offset negative from positive responses.  Joe Mako busted my chops last time for not doing this in my survey analysis visualization.

That said, I am going to build a simplified visualization (the one below) as I want our focus to be on intra-question analysis, not the best way to fashion Likert-scale visualizations.

A simpler visualization


Getting Two Sets of Questions by Joining a Table to Itself

(Or joining a table that’s really similar to the primary table)

If you downloaded the data source workbook you will see there is a tab called “Use This One” that contains data that looks like this:

Data from the “Use This One” table

There’s another tab called “Reshaped Questions” and it contains the exact same information, but without the demographic columns:

Data from “Reshaped Questions” table.

To achieve our goals we will need to join these two sheets (or tables) using ID as a common field.


Editing the Data Connection and Joining the Tables

We need to edit the Tableau data connection, indicate that we want to use multiple tables, and then add a table (in this case the table called “Reshaped Questions”).  We then need to fashion an inner join that looks like this:

Joining the two tables

Note: This approach will generate A LOT of additional rows (180K vs 9K).  Once you figure out just which questions you want to include in your intra-question analysis you may want to refine your extract and exclude questions you don’t need.

Tableau will add two new dimensions to the mix, as shown below.

New dimensions giving us two sets of data


So, Let’s See What We Got

If we place ‘Reshaped Questions$’_Question and ‘Reshaped Questions$”_Reponse on the columns shelf, Question and Response on the rows shelf, and CNT(Number of Records) on the Text mark shelf, we’ll see a complete cross tab of all questions and responses plotted against all questions and responses.

Cross tab of all questions

Now we can refine our view by looking at just one question from the Reshaped Questions table and a handful of questions from the “Use This One” table, as shown here.

Comparing results from one questions with responses from several Likert-scale questions

Next, by moving things around a little bit and by changing the CNT formula to show a percentage of the total, we start to glean some interesting insights.

Refining the cross tab

And finally, if we apply some of the Likert scale visualization techniques we explored in several earlier blog posts, we start to see some interesting patterns (e.g., folks that don’t know if they will vote in the upcoming election offer more positive responses to the Likert-scale questions.)

Further refinement combining stacked bars and circle marks


So, Why is this Part 2 ½ and not Part 3?

There is so much more we could explore with respect to intra-question analysis, but I have a backlog of posts I need to write.  I did, however, want to get people heading in the right direction.

One thing I will encourage people to explore is using parameters to allow consumers to select which questions they want to see displayed, especially if you build scatterplots.

Matt and Tony, I hope this addresses some of the missing pieces in visualizing survey responses in Tableau.

 Posted by on April 16, 2012 2) Visualizing Survey Data, Blog 9 Responses »