Jan 152017
 

By Steve Wexler, January 15, 2017

Overview

Before going any further this post assumes you’ve gotten your data “just so”; that is, you’ve reshaped your data and have the responses in both text and numeric form.

If you’re not sure what this means, please review this post.

Taking inventory by finding the universe of all questions and all responses

Anyone who has attended one of my survey data classes or has watched my 2014 Tableau Conference presentation knows the first thing I like to do is assemble a demographics dashboard so I know who took the survey.

The second thing I do arose a few months ago when I had a “why didn’t I do this before” moment with respect to getting a good handle on questions, responses, and seeing if there was anything that was poorly coded.

Here’s how it works.

Note that I’m using the same sample data set that I use for my classes.

Figure 1 -- Screen shot after just having connected to the data.

Figure 1 — Screen shot after just having connected to the data.

  1. Drag Question Grouping onto rows, followed by Wording, and then Question ID, as shown below.

    02_PartialInventory

    Figure 2 — About half-way through taking inventory.

  2. Right-click the measure called Value and select Duplicate.
  3. Rename the newly-created field Value (discrete).
  4. Drag the measure into the Dimensions area. This will make Tableau treat the field as something that is by default discrete.

    Figure 3 -- Turning the measure into a discrete dimension

    Figure 3 — Turning the measure into a discrete dimension

  5. Drag new newly-created dimension to Rows.
  6. Drag Labels to Rows. Your screen should look like the one shown below.
Figure 4 -- All the questions and all the responses, all on one sheet.

Figure 4 — All the questions and all the responses, all on one sheet.

So, just what do we have here?

You can see from the portion of the screen that you have a bunch of questions about “Importance” and can also see that the possible values go from 0 to 4 where 0 maps to “Not at all important”, 1 maps to “Of Little Importance”, etc.

At this point you should be looking for any stray values, say a value of 5.

If you scroll down a little bit (Figure 5) you’ll see a question grouping called “Indicate the degree to which you agree” where you again have values of 0 through 4 but this time 0 maps to “Not at all”, 1 maps to “Small degree”, etc.

We should be pleased as it appears that our Likert questions consistently go from 0 through 4. This means we won’t have to craft multiple sets of calculated fields to deal with different numeric scales (not that having to do that would be a big deal).

05_Further Inventory

Figure 5 — Next set of questions.

At this point it might be useful to add a filter so you can focus on only certain question groups. You can do this by filtering by Question Grouping as shown below.

Figure 6 -- Adding a filter makes it easier to focus on specific question groupings

Figure 6 — Adding a filter makes it easier to focus on specific question groupings

Spotting questions that have coding errors

In case you’re wondering what a coding error looks like, see what happens if we just focus on the “What do you measure” questions, as shown below.

Figure 7 -- An example of a mis-coded check-all-that-apply question

Figure 7 — An example of a mis-coded check-all-that-apply question

So, for all of the check-all-that-apply questions the universe of possible values is 0 and 1. And with the exception of Question Q6 (Breathing), 0 maps to “No” and 1 maps to “Yes.”

The mis-coding of “Ni” instead of “No” will only present a problem if our calculated field for determining the percentage of people that checked an item were to use Labels instead of Values. My preferred formula for this type of calculation is this:

SUM([Value]) / SUM([Number of Records)])

Because we’re using [Value] instead of [Label], the miscoding for this example won’t cause a problem.

Conclusion

Creating a giant text table that maps all Question Groupings, Question IDs, Labels, and Values on a single sheet allows us to quickly take an inventory of all questions and possible responses.  This in turn allows us to see if questions are coded consistently (e.g., do all the Likert Scale questions use the same scale) and to see if there are any coding errors.

I just wish I had started doing this years ago.

 

 Posted by on January 15, 2017 2) Visualizing Survey Data, Blog Tagged with: ,  No Responses »
Nov 282016
 

Note: Major thanks to Nazirah Garrison and Christie Clark at Tableau for suggesting this approach.

Overview

With Tableau 10.x it is in fact possible to get your survey data, “just so” without having to invest in new tools and / or a engage in a time-consuming, error-prone procedure every time you need receive updated survey data.

There’s a lot of upside to this approach — everything is built into Tableau and you’ll just need to refresh the extracts (there will be several of them) as you get new data.  The downside is the setup is a little bit cumbersome and Tableau manifests some “hmm, that doesn’t seem right” behavior along the way.

Before embarking on this I encourage you read this post so you understand what I mean when I refer to the data being “just so.”

Also, if you would like to follow along you can download the source data here.

Three files and three extracts

Anyone that has read my posts or attended my classes know that I want survey responses in both text and numeric formats. Sure, you can in fact manage with one format or the other, but you’re just creating a LOT more work for yourself if you don’t have the data in both formats.

You will also need what I call a “Helper” file — this is just a separate file that maps each question ID into human readable form and groups related questions together. Again, you can certainly get by without it but you’ll be working much harder than you need to, especially if you ever compare “Importance” with “Satisfaction” Likert-scale questions.

For this example we will create a separate extract for each of these three files, then use Tableau 10.x’s ability to join three different data sources.

Let’s start by creating the extracts.

To pivot the data labels and create an extract

  1. Start Tableau 10.x and indicate you want to connect to Excel.
  2. Connect to DR_SurveySampleData_SourceFiles_Fall2016.xlsx and drag the sheet Data Labels into the Drag sheets here area as shown below.
    01_draglabels
  3. Leave the first five columns intact (the Resp ID, demographic stuff, and Weight) and select all the other columns.
  4. Click any of the selected columns and select Pivot from the context menu.
    02_firstpivot
  5. Rename the first pivoted column Question ID and the second column Label as shown below.
    03_rename
  6. Indicate that you want to create an Extract (look for the radio button towards the upper right of the screen) and then click a sheet in your workbook to generate the Extract.
  7. When asked to save, name the file DR_JustSo_Labels.tde (make sure to note where you are saving the file).

One down, two to go.

To pivot the data numbers and create an extract

  1. Click the New Data Source icon and indicate you want to connect to an Excel file.
  2. As before, connect to DR_SurveySampleData_SourceFiles_Fall2016.xlsx, but this time drag Data Numbers to the Drag sheet here area.
  3. Hide the columns labeled Gender, Location, Generation, and Weight — we already have them in the other data source and don’t need them twice.
  4. Leaving Resp ID in place, select the second through the last columns.
  5. Click the down arrow on any of the selected columns and select Pivot from the context menu.
  6. Rename the first pivoted column Question ID and the second column Value, as shown below.
    04_secondpivot
  7. Indicate that you want to create an Extract (look for the radio button towards the upper right of the screen) and then click a sheet in your workbook to generate the Extract.
  8. When asked to save, name the file DR_JustSo_Numbers.tde.

Two down, one to go.

To create the question helper extract

  1. Click the New Data Source icon and indicate you want to connect to an Excel file.
  2. As before, connect to DR_SurveySampleData_SourceFiles_Fall2016.xlsx, but this time drag Question Helper to the Drag sheet here area.
  3. Indicate that you want to create an Extract (look for the radio button towards the upper right of the screen) and then click a sheet in your workbook to generate the Extract.
  4. When asked to save, name the file DR_QuestionHelper.tde.

All three data sources are now ready.

Joining the three data sources together

We now have our three data sources as separate Tableau extract files. We’ll combine these three files (and create an extract from the joined files) using Tableau 10.x’s ability to join files from different data sources.

Note: This is where we’ll encounter Tableau’s “head-scratching” behavior.

To join the three data sources

Note: In first trying this Tableau presented a lot of warning messages about not being able to materialize a temporary table. While I could ignore these warnings and muddle through, you may not be so lucky. It turns out the culprit was my anti-virus software. I temporarily disabled it and everything worked without a hitch.  See http://kb.tableau.com/articles/issue/error-unable-to-materialize-temporary-table-joining-data-sources.

  1. Click the New Data Source icon and indicate you want to connect to More, as shown below.
    05_more
  2. Select DR_JustSo_Labels.tde and click Open.
  3. Click Add, as shown below.
    06_add
  4. Click More and then select DR_JustSo_Numbers.tde. Do not be fooled, the correct fields have NOT yet been joined.
  5. Click the overlapping Venn diagram to display the Join dialog box, as shown below.
    07_join1
  6. Click Number of Records and then click the X that appears in the row to indicate you do NOT want to join these two data sources using this field.
  7. From the left data source indicate you want to join using Resp ID, as shown below.
    08_join2
  8. From the right data source indicate that you want to join using RespID (Extract1). I have no idea why the field is named this way. More on this in a moment.
  9. From the left data source indicate that you want to join using Question ID. Yes, you need to join on more than one field.
  10. From the right data source indicate you want to join using Pivot Field Names (Extract1). Okay… THIS is the thing that has me scratching my head and as of this writing (November 27, 2016) I have no idea why the field isn’t also called Question ID.

    January 4, 2017 — Now I know why this is happening. When using data extracts (.TDE files) Tableau is only able to keep track of the alias names for fields in the first .TDE file.  All of that info gets stripped out from the second .TDE file and Tableau just sees the original field name (Pivot Field Names). A little off-putting, yes, easy to address and it won’t matter a lick once we’re building our visualizations.

  11. Click Add again, select More, and select DR_QuestionHelper.tde.
  12. Click the second Venn overlapping circle and remove any of the joins that may be in there (most likely again using Number of Records).
  13. From the left data source select Question ID and from the right data source select QuestionID as shown below.
    09_join3
  14. Hide the fields RespID (Extract1), both Number of Records fields, the second Question ID, and Pivot Field Names, and rename Pivot Field Values to Value. Your screen should look like this.
    10_joinall
  15. Indicate that you want to create an Extract (look for the radio button towards the upper right of the screen) and then click a sheet in your workbook to generate the Extract.
  16. When asked to save, name the file DR_JustSoAll.tde. After creating the extract Tableau will show all the field names grouped by data source, as shown below.
    11_datasource
  17. Click the down arrow and select Group by Folder.
    Okay, you don’t have to do this but I see no reason to group the fields by data source.

Congratulations, you now have your data “just so” and you did it all in Tableau.

So, how do you get the extracts to refresh?

Good question.  If you are using either Tableau Server or Online you can create shared data sources and program the extract to refresh on a regular or as-needed basis.

If you are using desktop — and you have all four data sources in in one workbook — you can just click the  Data menu and select Refresh All Extracts.

Conclusion

While I find the process outlined here both cumbersome and confusing (what is up with those renamed fields NOT staying renamed?) this approach does appear to work and you only need to set it up once. The same cannot be said of The Tableau add-in for Excel which requires a lot of manual intervention every time you want to update the data.

Will this replace Alteryx as my tool of choice? No, but it does work and you can’t beat the price.

 Posted by on November 28, 2016 2) Visualizing Survey Data, Blog Tagged with: , , , ,  36 Responses »
May 102016
 

Note: Special thanks to Joe Mako who came up with a considerably more elegant and extensible solution than I had first built. I describe Joe’s approach in this post.

Overview

I’m fortunate that when I have to work with survey data that is not well-coded I can take out my trusty copy of Alteryx Designer and get my data “just so.”

But what if you don’t have a copy of Alteryx? Or what if you want to use Qualtrics’ new Web Data Connector and don’t have the ability to insert a data prep tool within your workflow?

In this blog post we’ll see how you can get Tableau to address the problem.  Specifically, we’ll see how to work with check-all-that-apply questions where the data has been coded as 1s and blanks instead of 1s and 0s.

Note: While the example shown focuses on Qualtrics data the approach will work with any tool that leaves blanks for respondents that did not select an item (e.g., Survey Gizmo).

So, What’s Wrong with Blanks?

Consider the data snippet below that shows responses to a group of check-all-that-apply questions. This is what I consider to be badly-coded data.

Figure 1 -- Check all that apply questions as they might appear in a CSV file downloaded from Qualtrics

Figure 1 — Check all that apply questions as they might appear in a CSV file downloaded from Qualtrics

Now here’s how I would prefer the data to be coded.

Figure 2 -- Correctly-coded check all that apply questions

Figure 2 — Correctly-coded check all that apply questions

So, why is this better?

By having 1s and 0s we can see, easily, if some respondents didn’t answer the question group at all. In the figure below we can see that respondents 1002, 1006, 1011, and 1016 did not make any selection for any of the check-all-that-apply questions.  More importantly, with data coded with 1s and 0s, Tableau we can just filter out the Nulls and we’ll get accurate results.

 Figure 3 -- Respondents that didn't answer this question group are highlighted in yellow


Figure 3 — Respondents that didn’t answer this question group are highlighted in yellow

IMPORTANT: Before going any further, make note that respondents 1002, 1006, 1011, and 1016 did not make any selections and that of the 13 people that actually responded to Q1_4,  12 selected that option.  There will be a test.

So, given our constraints, how are we going to come up with a way to distinguish between a blank that means a respondent didn’t select a particular option and a blank that, when combined with three other blanks, means the person didn’t respond to the group of questions at all?

Before we plow into this I want to make sure people realize that we will soon pivot / reshape the data so that it is tall. Specifically, instead of dealing with this…

Figure 4 -- Data before reshaping (as you would get it if you downloaded a .CSV file.)

Figure 4 — Data before reshaping (as you would get it if you downloaded a .CSV file.)

… we’re going to be working with this:

Figure 5 -- Reshaped data using Tableau's built-in Pivoting feature. If you're using the Qualtrics web data connector you don't need to use this.

Figure 5 — Reshaped data using Tableau’s built-in Pivoting feature. If you’re using the Qualtrics web data connector you don’t need to use this.

Once we pivot the data we’ll see the following collection of dimensions and measures in Tableau.

Figure 6 -- Reshaped data as it appears in Tableau

Figure 6 — Reshaped data as it appears in Tableau

Note: For this example I’m not going to the trouble of creating a data set that has the survey responses in both numeric and text format. The technique we explore will work equally well for when you have both as when you just have numeric values.

Grouping the related questions

To be able to come up with an elegant, extensible way to tell Tableau “don’t take into account questions where people didn’t make any selections” we’re going to need a way to group related questions together.

Note that the technique I’m about to propose is different from the ad-hoc grouping approach I describe in Using Qualtrics’ Web Data Connector and Reshaping Survey Data with Tableau 9.0.  The programmatic “group” we create here needs to be referenced in a calculated field, and you can’t do that with an ad-hoc group.

Here we’re going to take advantage of Qualtrics’ Question ID naming convention and how related questions all look like this:

Q(N)_

That is, a group of related check-all-that-apply questions might be named like this:

Q1_1
Q1_2

Q1_8

And a group of related Likert scale questions might be named like this:

Q7_1
Q7_2

Q7_13

Given this standardization, we need a mechanism that will group everything to the left of the underscore. There are several ways we can achieve this, but for this example we’ll create a new field called “Question Prefix” and define it as follows:

Figure 7 -- Calculated field to that will group related questions based on whatever comes before the underscore in the Question ID field.

Figure 7 — Calculated field to that will group related questions based on whatever comes before the underscore in the Question ID field.

Now let’s use this field and the Resp ID field to see who did not respond at all to a particular group of questions.

Figure 8 -- This collection of fields show us who did not respond at all to a particular group of questions.

Figure 8 — This collection of fields show us who did not respond at all to a particular group of questions.

So, have a look at results in the image above where we place MAX(Value) on the text button. What’s happening is that for each respondent we’re looking at the answers to Q1_1, Q1_2, Q1_3, and Q1_4 and taking the maximum value across all those questions.  Notice that there are four blanks / Nulls for 1002, 1006, 1011, and 1016 — these are the four respondents that didn’t answer the question group at all (see, I told you there would be a test.)

We do NOT want to include these people when we calculate the percentage of people that selected a particular choice, so we can place MAX(Value) on the Filters shelf and indicate that we should not include Null values, as shown below.

Figure 9 -- Filter that will exclude Null values.

Figure 9 — Filter that will exclude Null values.

Applying this filter will show us responses from people that did NOT skip the question, as shown below.

Figure 10 -- Respondents that selected at least one option.

Figure 10 — Respondents that selected at least one option.

Okay, this is great, but the visualization we want to create shouldn’t look anything like this. Indeed, we have too many things on the level of detail at this point.

So what are we going to do?

Level-of-Detail Calculations to the Rescue

Anytime you think “I need Tableau to consider some dimensions when making a calculation, but those dimensions can’t be in the visualization” a giant alarm bell should go off in your head — this is precisely the use case for a LoD calculation!

Here’s the LoD calc, that when placed on the Filters shelf, will do the trick:

Figure 11 -- LoD calc that allows us to exclude folks that skipped answering a question group.

Figure 11 — LoD calc that allows us to exclude folks that skipped answering a question group.

A translation into English would be:

Even though [Question Prefix] and [Resp ID] may not be on the viz detail, take that combination of these two fields and find the MAX([Value]). Then find only the Null values from this and exclude them.

Note that we are using FIXED instead of INCLUDE as the former will automatically create a dimension and will work no matter what other filter settings may be in place.

Putting This Altogether

First we need to create a calculation that gives us the percentage of people that selected an option from the group of check-all-that-apply questions. For this instance the following calculation will work well.

Figure 12 -- How to determine the percentage of people that selected an option.

Figure 12 — How to determine the percentage of people that selected an option.

A quick translation into English would be:

Add up everybody that answered “1” (meaning they selected an option) and divide by the number of people that answered the question.

Let’s see if this works. Consider the visualization shown below.

Figure 13 -- First attempt at showing the percentage of respondents that selected an option. This is NOT showing the correct percentage.

Figure 13 — First attempt at showing the percentage of respondents that selected an option. This is NOT showing the correct percentage.

Does this pass the “smell” test? Look at Q1_4. You may recall that we saw that 12 out of 13 people selected this option — but 12 out of 13 is 92% not, 71%.  Indeed, if we place SUM([Number of Records]) as a discrete measure on the rows shelf we see that Tableau believes 17 people responded to the question.

Figure 14 -- Without a filter Tableau believes 17 people responded to this group of questions

Figure 14 — Without a filter Tableau believes 17 people responded to this group of questions

We need to put our LoD calc on the Filters shelf and set it to True.

Figure 15 -- Removing "All of these are Null" from the respondent pool gives us the correct count of 13 and the correct percentage (12 out of 13).

Figure 15 — Removing “All of these are Null” from the respondent pool gives us the correct count of 13 and the correct percentage (12 out of 13).

Conclusion

How check-all-that-apply questions are coded is a problem with many survey tools including Qualtrics and Survey Gizmo. If you have a good data preparation tools like Alteryx you can fix the problem, but if you don’t have such a tool or don’t want to insert something into your workflow then you can get Tableau to address the problem with a Level-of-Detail calculation.

Feb 112016
 

Overview

As I’m mentioned before the number one impediment to success with visualizing survey data in Tableau is getting the data in the right format so you can do all the dazzling things you want to do.

Up until very recently none of the survey tools vendors had done anything to make things easy so you had to pursue one of the following options:

  • Use the Tableau add-in for Microsoft Excel (only works on a PC and can be very cumbersome if you have data that needs to be updated.)
  • Use the pivot feature in Tableau 9.x (works great but is missing some important functionality, especially for larger surveys.)
  • Use Alteryx (Best-in-class but it has its own learning curve and is pricey.)
  • Get somebody to do this for you (like that’s going to happen.)

But now there is another choice and it warrants its own blog post.

Say hello to the Qualtrics web connector

Survey vendor Qualtrics has developed a web data connector so that you can connect directly to your Qualtrics surveys from inside Tableau. While the connector does not have everything I would want (more on this in a moment) this offering presents a great step forward for Qualtrics users that want to better see and understand their data using Tableau.

In this blog post we’ll look at the following:

  • Wide vs. tall data (and why we need it to be tall)
  • How to connect to Qualtrics survey data using Tableau
  • How you can get the data “just so” using data blending
  • Dealing with null values
  • Benefits and possible drawbacks

Wide vs. Tall Data

Most survey tools present the data in a one-column-for-each-question format that looks something like this.

Figure 1 -- One row per respondent, one column for each question

Figure 1 — One row per respondent, one column for each question

We will be absolutely miserable working with the data in this format so we need something that can reshape the data so that we have a separate row for each question, as shown below.

Figure 2 -- Each respondent is listed many times (one row for each question)

Figure 2 — Each respondent is listed many times (one row for each question)

Actually, we want to do more than just reshape the data.  Ideally we want to get something that looks like this.

Figure 3 -- Ideal format for survey data

Figure 3 — Ideal format for survey data

Qualtrics can get us most of the way there, and with some simple data blending it can get us all of the way there.

How to connect to Qualtrics survey data

Let’s assume you have a Qualtrics ID and at least one survey with some data.

  1. Log into Qualtrics.com and access the survey to which you will want to connect.
  2. Click the Responses tab and click the Export Data button, as shown here.
    04_InsideQ
  3. When the Export Responses dialog box appears, click Tableau, then copy the URL, as shown below.
    05_QDialog
  4. Close the dialog box and open Tableau.
  5. From within Tableau indicate you want to connect to a new data source and select Web Data Connector.
    06_WebConnector
  6. When the Web Data Connector dialog box appears paste the survey URL that you copied from Qualtrics.
  7. When asked, enter your Qualtrics username and password and click Log In.
    Qualtrics will then ask you which fields you want from your survey and which fields you will want to transpose
  8. Start by indicating that you want to select all fields and transpose all the fields, as shown below.
    07_Which fields
  9. Indicate which fields you won’t need and which you will not need transposed. In this example we will want Response ID, Q1: Gender, Q2: Region and Q3: Age to be separate columns, but all other selected fields need  to be transposed (pivoted).
    08_JustThese
  10. Click Import Survey Responses and wait a little bit as Tableau imports the data and create a Table Data Extract file.

So, just what do we have here?

After specifying that we want the vast majority of questions (fields) to be transposed, here’s what we’ll see from inside Tableau.

09_Fields In Tableau

Figure 4 — Dimensions and Measures generated by the Qualtrics Web Data Connector

It may not look like much but the fact that we get both text and numeric responses to the survey questions will be a huge time saver.

Here’s what we get if we place Question ID, Question Description, Textual Answer, and Numeric Answer on the Rows shelf.  Please note that Numeric Answer has been converted to discrete so we can easily see how the text responses line up with the numeric responses.

10_Whatdowehavehere

Figure 5 — “What do we have here?” viz that shows Question ID, human-readable translation of the Question ID, all possible text responses, and all possible numeric responses.

Here I can see how each Question ID maps to a human-readable translation of the Question ID (i.e., the Question Description) as well as all possible text responses and all possible numeric responses.  Yes, there are some null values here, but we’ll deal with them in a moment.

I wish I can express just how beautiful this is! This has almost everything I could possibly want and I barely had to lift a finger.

Okay, what did I mean by “almost”?

This particular survey does not contain many questions so I could work with what I have right now without too many problems.  But if I had a survey with hundreds of questions then I would benefit greatly from having some way to group related questions together.

Fortunately I can do this easily using Tableau’s blending feature.

How you can get the data “just so” using data blending

I need to create a separate Excel file that groups the Question IDs so that I can more easily find and manipulate the data in Tableau.

11_Helpfer File

Figure 6 — Example of a “Question Helper” file.

Note:  In some cases it isn’t just convenience that warrants creating this “helper” file.  NPS analysis and Importance vs. Satisfaction analysis will be very difficult without building a scaffolding file like the one shown above.

Let’s see how to create the blend.

  1. On a new worksheet drag Question ID onto rows.
  2. Click the Add New Data Source
  3. Connect to the Helper File indicate whether or not you want to create an Extract (of course you do!)
    Notice that there is a link field on Question ID in what will be the secondary data source.
    12_LinkField
  4. Drag Grouping the right of Question ID and then Qtype to the right of Grouping as shown below.
    13_TempBlend
  5. Right-click the Grouping pill and select Create Primary Group.
  6. Rename the field Question Grouping as shown below.
    14_QuesitonGrouping
  7. Right-click the Qtype pill and select Create Primary Group.
  8. Rename the field Qtype and click OK.
    At this point we no longer need the secondary data source as we’ve created some very useful groups in the primary data source.
  9. Remove the Grouping and Qtype pills from the Rows shelf.
  10. Close the secondary data source.
  11. Drag Question Grouping and QType to the left of Question ID as shown below.
    15_PerfectMapping

Putting this to use

Let’s see how to put all this to good use; specifically, let’s see how to visualize a check-all-that-apply question.

  1. In a new sheet drag Question Grouping to the Filters shelf and select What do you measure, as shown below.
    16_Filters
  2. Drag Question Description to rows.
  3. Create a new calculated field called CheckAll_Percent and define is as follows:
    17_CheckAll_Percent
  4. Right-click the newly created measure and indicate you want to change its default numeric format to Percentage with 0 decimal places.
  5. Drag CheckAll_Percent to the Columns shelf, turn on mark labels, and sort in descending order.

    Figure 8 -- Percentage of respondents that measure Pulse Rate, Adrenaline Production, and so on.

    Figure 8 — Percentage of respondents that measure Pulse Rate, Adrenaline Production, and so on.

Dealing with nulls

As we saw earlier we have some nulls and we need to make sure they don’t muck up the visualization.

As of this writing the Qualtrics Web Connector writes a 0 as the numeric answer and a blank as a textual answer for Nulls.  I would have expected either a “99” or an actual NULL value as we cannot simply filter the 0 values as sometimes a 0 means “no”, sometimes it may mean “strongly disagree,” and so on.

Fortunately, the Qualtrics Web Connector does write an unambiguous blank that we can use as our filter.

Let’s see how to remove the null values in the visualization above.

  1. Drag Textual Answer to the Filters shelf.
  2. When the Filter dialog box appears indicate that you want to see Fewer values, as shown below.
    19_Fewer Values
  3. Indicate that you want to Exclude blanks, as shown below.
    20_Exclude

Benefits and possible drawbacks

So, just what are the benefits and potential drawbacks of using the Qualtrics web connector?

Benefits

There are two clear benefits to using the Qualtrics web connector:

  1. You can get the data the way you need it with very little effort – there’s practically no learning curve.
  2. As you get more survey responses you just need to update the Extract. The workflow is seamless.

Possible drawbacks

  1. What happens when there are many questions? It’s unclear how well this will work when you have many questions, let alone many survey responses. Navigating the connection dialog to find the fields you want and don’t want to transpose may prove cumbersome.
  2. Weighted data. For those that need it, there needs to be some mechanism in the workflow to weigh survey responses. This is something that can be automated using a tool such as Alteryx.
  3. The Qualtrics web connector is not free and the pricing will vary based on a variety of factors. Given this, there may be cases where it makes sense to sacrifice Qulatrics’ ease and seamlessness for a more powerful and flexible ETL solution.

Conclusion

If you are Qualtrics user who wants to explore and visualize data using Tableau you should definitely take the Qualtrics web connector for a test drive as Qualtrics has made the task of getting survey data into Tableau easy and seamless.

If you’re using a different survey tool and want to visualize your data with Tableau, you may want to consider switching to Qualtrics.

 

 

 

 

 Posted by on February 11, 2016 2) Visualizing Survey Data, Blog Tagged with: ,  12 Responses »
Nov 222015
 

Overview

I received an e-mail inquiry about weighted data recently and realized that while I cover this in my survey data class I had not yet posted anything about it here.  Time to remedy that.

The good news is that it is not at all difficult to work with weighted survey data in Tableau.  And just what do I mean by weighted data? We use weighting to adjust the results of a study so that the results better reflect what is known about the population. For example, if the subscribers to your magazine are 60% female but the people that take your survey are only 45% female you should weigh the responses from females more heavily than males.

To do this each survey respondent should have a weighting amount associated with their respondent ID, as shown here.

Figure 1 – A snippet of survey data showing a separate column for Weight.

Figure 1 – A snippet of survey data showing a separate column for Weight.

When pivoting / reshaping the data make sure that [Weight] does not get reshaped.  It should remain in its own column like the other demographic data.

Once this is in place we’ll need to modify the formulas for the following questions types:

  • Yes / No / Maybe (single punch)
  • Check-all-that-apply (multi-punch)
  • Sentiment / Likert Scale (simple stacked bar)
  • Sentiment / Likert Scale (divergent stacked bar)

Yes / No / Maybe (single punch)

With this type of question you usually want to determine the percentage of the total.

02_YesNoMaybe

Figure 2 — Visualization of a single-punch question

Unweighted calculation

The table calculation to determine the percentage of total using unweighted data is

   SUM([Number of Records]) / TOTAL(SUM([Number of Records]))

Weighted calculation

The table calculation to determine the percentage of total using weighted data is

   SUM([Weight]) / TOTAL(SUM([Weight]))

Check-all-that-apply (multi punch)

With this type of question you usually want to determine the percentage of people that selected an item.  The total will almost always add up to more than 100% as you are allowing people to select multiple items.

Figure 3 -- Visualization of a multi-punch question

Figure 3 — Visualization of a multi-punch question

Most surveys will code the items that are checked with a “1” and those that are not checked with a “0”.

Unweighted calculation

The calculation to determine the percentage of people selecting an item using unweighted data is

   SUM([Value]) / SUM([Number of Records])

where [Value] is the name of the measure that contains the survey responses.  If the survey responses are coded as labels instead of numbers you can use this formula instead.

   SUM(IF [Label]="Yes" then 1 ELSE 0 END) / SUM([Number of Records])

Weighted calculation

The calculation to determine the percentage of people selecting an item using weighted data is

   SUM(IF [Value]=1 then [Weight] ELSE 0 END) / SUM([Weight])

Sentiment / Likert Scale (simple stacked bar)

This is very similar to the single-punch question but instead we have several questions and compare them using a stacked bar chart.  I am not a big fan of this approach but it can be useful when you superimpose some type of score (e.g., average Likert value, percent top 2 boxes, etc.).

Figure 4 -- Simple Likert Scale visualization

Figure 4 — Simple Likert Scale visualization

Figure 5 -- Simple Likert Scale visualization with Percent Top 2 Boxes combo chart

Figure 5 — Simple Likert Scale visualization with Percent Top 2 Boxes combo chart

Unweighted calculation – Stacked Bar

The table calculation to determine the percentage of total using unweighted data is

   SUM([Number of Records]) / TOTAL(SUM([Number of Records]))

Weighted calculation – Stacked Bar

The table calculation to determine the percentage of total using weighted data is

   SUM([Weight]) / TOTAL(SUM([Weight]))

Unweighted calculation – Percent Top 2 Boxes

Assuming a 1 through 5 Likert scale, the calculation to determine the percentage of people selecting either Very high degree or High Degree (top 2 boxes) using unweighted data is

   SUM(IF [Value]>=4 then 1 ELSE 0) / SUM([Number of Records])

Weighted calculation – Percent Top 2 Boxes

Assuming a 1 through 5 Likert scale, The calculation to determine the percentage of people selecting either Very high degree or High Degree (top 2 boxes) using weighted data is

   SUM(IF [Value]>=4 then [Weight] ELSE 0) / SUM([Weight])

Sentiment / Likert Scale (divergent stacked bar)

Here is what I believe is a preferable way to show how sentiment skews across different questions.

Figure 6 -- A divergent stacked bar chart

Figure 6 — A divergent stacked bar chart

I’ve covered how to build this type of chart using unweighted values here.

There are six fields we need to fashion the visualization, three of which need to be modified to make the visualization work with weighted data.

  • Count Negative
  • Gantt Percent
  • Gantt Start
  • Percentage
  • Total Count
  • Total Count Negative

Count Negative – Unweighted

Assuming a 1 – 5 Likert scale, the calculation to determine the number of negative sentiment responses using unweighted data is

   IF [Score]<3 THEN 1
   ELSEIF [Score]=3 THEN .5
   ELSE 0 END

Count Negative – Weighted

Assuming a 1 – 5 Likert scale, the calculation to determine the number of negative sentiment responses using weighted data is

   IF [Score]<3 THEN [Weight]
   ELSEIF [Score]=3 THEN .5 * [Weight]
   ELSE 0 END

Percentage – Unweighted

The calculation that determines both the size of the Gantt bar and the label for the bar using unweighted data is

   SUM([Number of Records])/[Total Count]

Percentage – Weighted

The calculation that determines both the size of the Gantt bar and the label for the bar using weighted data is

   SUM([Weight])/[Total Count]

Total Count – Unweighted

The calculation that determines the total number of responses for a particular question for unweighted data is

   TOTAL(SUM([Number of Records]))

Total Count – Weighted

The calculation that determines the total number of responses for a particular question for weighted data is

   TOTAL(SUM([Weight]))

Summary

Here’s a summary of all the unweighted calculations and their weighted equivalents

Unweighted Weighted
SUM([Number of Records]) / TOTAL(SUM([Number of Records])) SUM([Weight]) / TOTAL(SUM([Weight]))
SUM([Value]) / SUM([Number of Records]) SUM(IF [Value]=1 then [Weight] ELSE 0 END) / SUM([Weight])
SUM(IF [Value]>=4 then 1 ELSE 0) / SUM([Number of Records]) SUM(IF [Value]>=4 then [Weight] ELSE 0) / SUM([Weight])
IF [Score]<3 THEN 1 ELSEIF [Score]=3 THEN .5 ELSE 0 END IF [Score]<3 THEN [Weight] ELSEIF [Score]=3 THEN .5 * [Weight] ELSE 0 END
SUM([Number of Records])/[Total Count] SUM([Weight])/[Total Count]
TOTAL(SUM([Number of Records])) TOTAL(SUM([Weight]))

 

Oct 132015
 

Overview

You can download a trial version of Alteryx here.

You can find the Excel source data here.

You can download the completed Alteryx module from here.

Understanding the workflow

Here’s the Alteryx workflow that will get the data in the format we want.

Figure 9 -- Alteryx workflow for getting the survey data in an optimal format for analysis in Tableau

Figure 9 — Alteryx workflow for getting the survey data in an optimal format for analysis in Tableau

If this is the first time you’ve seen an Alteryx workflow it may look a little complicated (shades of Rube Goldberg) but think about how complicated the flow and decision tree is for what you do when you get up in the morning and get ready for work – it’s way more complicated.

Let’s start by focusing on the three data sources that we need (labeled 1, 2, and 3 in the diagram above).

  1. Data with numeric responses (we’ll use this for our demographic data, too)
  2. Data with text responses
  3. The Question Helper file (contains our meta data).

A note about SPSS files

For this example we’re using Excel data but the process would be identical for survey data saved in an SPSS format (.SAV file).  SPSS encodes both the text and numeric data in a single file, but you need to input values from that same data source twice – once for text and once for numeric values.

When you specify an SPSS file as a data source you’ll see the following check box option.

Figure 10 -- Options that appear when you bring in data from an SPSS file.

Figure 10 — Options that appear when you bring in data from an SPSS file.

Selecting “Expand Factors” will bring in the data as labels.  Unchecking this box will bring in the data as numbers.

Demographics

Figure 11 -- Selecting the demographic components from the data source

Figure 11 — Selecting the demographic components from the data source

Clicking the Select tool reveals the following settings.

Figure 12 -- Specifying which fields we want to keep

Figure 12 — Specifying which fields we want to keep

A translation into English would be “open this Excel file and select these fields”.  Here we keep RespID, Gender, Location, Generation, and Weight.

Numeric values

Figure 13 -- Numeric values section of the Alteryx workflow with the Reshape tool selected

Figure 13 — Numeric values section of the Alteryx workflow with the Transpose tool selected

Using the same data source, selecting the Transpose tool reveals the following settings.

Figure 14 – Transpose tool settings

Figure 14 – Transpose tool settings

Here we indicate that we want to keep RespID and pivot all the other checked fields.  Notice that the demographic components are not checked.

Looking back to the workflow diagram, we next follow this pivot with a Select tool which we use to rename the pivoted fields as shown here.

Figure 15 -- The Select tool allows us to name the pivoted fields

Figure 15 — The Select tool allows us to name the pivoted fields

Here we are naming the first pivoted field Question ID and the second pivoted field Numeric value.

Dealing with nulls

Figure 16 -- The Filter tool allows us to eliminate null values before they hit Tableau.

Figure 16 — The Filter tool allows us to eliminate null values before they hit Tableau.

While not critical, Alteryx allows us to eliminate null values before they come into Tableau.  Here’s the setting for this tool.

Figure 17 -- Setting to eliminate nulls from the Numeric value field

Figure 17 — Setting to eliminate nulls from the Numeric value field

Joining the demographic with the transposed (pivoted) numeric data

Figure 18 -- Joining the demographic and transposed numeric data

Figure 18 — Joining the demographic and transposed numeric data

We’re now ready to join the demographic data with the pivoted numeric survey data. We do this with Alteryx’s Join tool.  Here are the settings.

Figure 19 -- Joining demographics and numeric data using the common field, RespID. Notice that we don't include the redundant version of RespID that is coming from the right table.

Figure 19 — Joining demographics and numeric data using the common field, RespID. Notice that we don’t include the redundant version of RespID that is coming from the right table.

Checking our progress

If we add a Browse tool after the Join we’ll see the following results.

Figure 20 -- Results of the initial Join

Figure 20 — Results of the initial Join

Note that we could, in fact, get by with using just this in Tableau, but the whole point is to make things faster and easier, so we’re going to add text responses and the “Helper” data.

Text Values

Figure 21 -- Workflow for setting the text values

Figure 21 — Workflow for setting the text values

This is identical to the numeric values except that we specify a different data source (one with label responses instead of numeric) and we rename the second pivoted field differently, as shown here.

Figure 22 -- The Select tool allows us to name the pivoted fields

Figure 22 — The Select tool allows us to name the pivoted fields

Here we are naming the first pivoted field Question ID and the second Text value.

Tying Demographic, Numeric and Text Values Together

We need to combine the demographic / numeric values with the text values.  We will again use Alteryx’s Join tool, but we’ll need to join on both RespID and Question ID.

Figure 23 -- Joining demographic, numeric, and text data together

Figure 23 — Joining demographic, numeric, and text data together

The setting for the Join is shown below.

Figure 24 -- Join settings for combining demographics / numeric with text data

Figure 24 — Join settings for combining demographics / numeric with text data

Adding the question meta data and outputting the results

We’re almost done; we just need to add the information that tells us about each Question ID so we can map the ID to its human-readable form, group related IDs together, and so on.

Figure 25 -- Joining the "Helper" file data

Figure 25 — Joining the “Helper” file data

The setting for this Join is shown below.

Figure 26 -- Marrying the question meta data to the demographic and survey data

Figure 26 — Marrying the question meta data to the demographic and survey data

The only thing left to do is output this to a new data source.  In the sample workflow I output to an Excel file but you can just as easily write directly to a Tableau Data Extract file.

But Wait!  There’s More!

I intentionally used data that was well behaved, but what happens if the data is “messy”?  For example, suppose there’s an extra row below the field names?  Or suppose that for a check-all-that-apply question, instead of 1s and 0s we instead have 1s and blanks (which would mess things up in Tableau)?  Alteryx handles both of these scenarios (and dozens more) easily.

Is there a Downside?

If there’s any downside it would be the cost as Alteryx starts at around $4,000 per year.  If all you are doing is some occasional survey data reshaping then this fee would probably be prohibitively expensive. If, however, you need to corral a lot of data – or if you need advanced data blending, geospatial analysis, or predictive modeling – then Alteryx is a huge bargain.

 Posted by on October 13, 2015 2) Visualizing Survey Data, Blog Tagged with: , , , , ,  2 Responses »
Oct 132015
 

Overview

In writing about visualizing survey data using Tableau I’ve found that the number one impediment to success is getting the data in the right format. In accompanying posts I’ll explain how to get this done using Alteryx, Tableau 10.x, the Tableau Excel add-in, and Tableau 9.0 pivot feature (you can come close with 9.x, but can’t get it perfect).

What do I mean by “just so”?

When I deal with survey data there are usually four different elements that need to fit together:

  1. The demographic information (e.g., age of respondents, gender, etc.)
  2. Survey responses in text format
  3. Survey responses in numeric format
  4. Meta data that describes the survey data.

Let’s see what the four elements look like using an Excel sample data set (click here to download).

Demographic data

Here’s what the demographic data looks like.

Figure 1 -- Demographic data

Figure 1 — Demographic data

Survey responses in text format

Here are several columns of survey responses in text format.  Column F contains data for a Yes / No / Don’t know question.  Column G contains responses for a question about salary.  Columns H through P are responses for check-all-that apply questions and columns Q and R contain Likert scale responses.

Figure 2 -- Survey responses in text format

Figure 2 — Survey responses in text format

Survey responses in numeric format

Here are the same responses but in numeric format.

Figure 3 -- Survey responses in numeric format

Figure 3 — Survey responses in numeric format

I’ll explain why it’s so useful to have the survey responses in both text and numeric format in a bit.

Meta Data (the “helper” file)

Here’s some data that I usually prepare by hand as most survey tools won’t produce it for me automatically.  Having this helps me understand the data and will  greatly streamline my work in Tableau.

Figure 4 -- Survey data meta data. This doesn’t take long to create and will be a huge time saver once we get the data into Tableau.

Figure 4 — Survey data meta data. This doesn’t take long to create and will be a huge time saver once we get the data into Tableau.

What does “just so” look like?

Our goal is to combine and reshape the various elements so that they look like this.

Figure 5 -- Reshaped data joined with meta data. Survey data in this format is very easy to use with Tableau.

Figure 5 — Reshaped data joined with meta data. Survey data in this format is very easy to use with Tableau.

As I’ve written previously, the key thing is that I no longer have a separate column for each survey response.  Indeed, I’ve reduced the number of columns from 45 to just 11, but I’ve also increased the number of rows from 845 to over 25,000. That is a good thing.

Why this works so well with Tableau

Our goal is to see how to get Alteryx to get the data in this format, not to actually use the data, but if you need convincing on why the meta data is so helpful, consider the following example.

Let’s say that in your survey you ask people to indicate the importance and satisfaction about certain services, as shown here.

Figure 6 -- Question comparing importance with satisfaction

Figure 6 — Question comparing importance with satisfaction

With the data set up “just so” conducting this comparison in Tableau becomes easy.  First we can drag Question Grouping into Filters and indicate that we just want to look at Importance and Satisfaction questions.

Figure 7 -- Using the Question Grouping field to just focus on Importance and Satisfaction questions

Figure 7 — Using the Question Grouping field to just focus on Importance and Satisfaction questions

Then we can drag Wording and Question Grouping onto the Rows shelf which gives us the framework for comparing importance and satisfaction across ten different questions.  No more having to “look up” which questions we want to explore and no more having to alias question IDs.  I love this!

Figure 8 – The helper file meta data provides the framework for comparing questions and building visualizations.

Figure 8 – The helper file meta data provides the framework for comparing questions and building visualizations.

Why do we need both text and numeric results?

We don’t really need them, but I know I certainly want them.

Consider all of the Likert scale question results.  The universe of possible values are

1
2
3
4
5

Suppose we want to know just what each of the values (1, 2, 3, 4 and 5) stand for?  The problem is that it depends on the question being asked as sometimes a 5 means “Strongly agree”, for other questions it  means “Critical” and for others it means “Extremely satisfied”.

Without having both numeric and text results we will have to write A LOT of IF / CASE statements and I, for one, do not want to do that.

So, now that we understand how and why we want the data “just so” we’ll see how to get it that way using Alteryx, Tableau 10.x, the Tableau-add-in for Excel, and Tableau 9.x.

 Posted by on October 13, 2015 2) Visualizing Survey Data, Blog Tagged with: , , , ,  3 Responses »
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: , , ,  28 Responses »