### 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.

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.

#### 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.

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.).

#### 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.

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])) |

Hi Steve – thanks for posting. Weighted survey data adds another layer of complexity, and I’ve had trouble with it before.

But doesn’t using SUM([Weights]) as the denominator in the percent of total calculations mean we are returning percent of responSES, not percent of respondENTS? In other words, you can have zero, one, or many responses per respondent in a “select all” question, meaning the SUM([Weights]) will be different for each “select all” question and will not be equal to the weighted total number of respondents. Don’t we need to use a sort of “sum distinct” function to only count a respondent in the denominator once? I haven’t figured out how to do that though (Tableau has a Count Distinct function, but no SUM Distinct).

Jeff,

SUM([Weights]) works for the same reason that SUM([Number of Records]) and SUM(1) works for unweighted data. It turns out that you do NOT have find the equivalent of COUNTD([ID]). Because we are only seeing responses for each question and not for the overall survey we don’t need to determine the distinct number of respondents.

You can placate your concern by placing the questions on rows and COUNT(ID), COUNTD(ID), and SUM([Number of Records]) on columns. You’ll get the same results for all three pills.

When you get a chance go to http://www.datarevelations.com/visualizing-survey-data and download the sample packaged workbook. It walks through all the different examples.

The only wrinkle in the whole thing is getting weighted responses for the demographic questions. Here we need to filter the results by whatever question received a response from all survey takers.

Steve

it would be great if you append dashboards

I do have an updated packaged workbook that has both unweighted and weighted examples. You can find it at http://www.datarevelations.com/visualizing-survey-data.

Steve

Hi Steve,

Thanks a lot for the info! Could you share how to create the weight variable in Tableau please? Can it be calculated within the system automatically?

All the best,

Matt

Matt,

Great question and I do not yet have an answer for you.

My client typically use SPSS to weigh the data and then we either export that data to .CSV files or import it into Alteryx. I’ve no doubt there must be a way to avoid the SPSS step and come up with a way, either inside Tableau or certainly inside Alteryx, to do this.

Tackling this is on my to-do list; just haven’t gotten to it yet.

Steve

Hi Steve,

I really enjoyed your working with survey data class and have gotten great help from your blog posts/website. One thing I have yet to find or figure out on my own is how to handle mean scores with weighted data.

I used LikertValue: Float(Left([Label],2)) for unweighted mean scores but can’t figure out how to manipulate that for weighted data.

Any tips would be greatly appreciated.

Thanks,

Mike

Mike,

I gather your FLOAT(LEFT([Label,2)) is giving you a number (probably between 1 and 5) and then you are taking the average of that, as in

AVG(FLOAT(LEFT([Label,2)))

Assuming that each respondent has a weight value, (let’s call it [WEIGHT]) I think this would work:

AVG(

FLOAT(LEFT([Label,2)) * [WEIGHT]

)

Steve

Thanks Steve for the quick reply.

I tried your suggestion but using AVG( FLOAT(LEFT([Label,2)) * [WEIGHT]) didn’t work.

I created a new measure “LikertValue (weighted)” with the following code:

Float(Left([Label],2))*[Weight Alt]

and in the shelf chose this: AVG([LikertValue (weighted)])

Some of the mean scores are outside the range of values so something odd is happening.

Try this:

Weight at the respondent level: [Weight Alt]

Likert Response: [Value]

SUM([Value]*[Weight Alt]) / SUM([Weight Alt])

Yes, I know you have to do the string to float conversion as you don’t have straight-ahead values in your data set. You *should* be able to swap out [Value] with Float(Left([Label],2)) and get what you need.

SUM(Float(Left([Label],2))*[Weight Alt]) / SUM([Weight Alt])

Steve

This worked! Thank you so very much.