Calculated Field Tableau Average Using Different Survey Calculator
Accurately combine and average scores from multiple surveys with different scales (e.g., 1-5 and 1-10) by calculating a normalized, weighted average, perfect for creating a calculated field in Tableau.
Survey 1
The score the respondent gave in this survey.
The highest value on the scale (e.g., 5 for a 1-5 scale).
The importance of this survey relative to others (e.g., 50% for equal weight).
Survey 2
The score the respondent gave in this survey.
The highest value on the scale (e.g., 10 for a 1-10 scale).
The importance of this survey relative to others (e.g., 50% for equal weight).
Combined Results
Survey 1 Normalized Score: —
Survey 2 Normalized Score: —
Total Weight Applied: —
The final average is calculated by converting each score to a common percentage scale, applying the specified weights, and then finding the combined average.
Results Visualization
What is a Calculated Field for Averaging Different Surveys?
In data analysis tools like Tableau, a calculated field tableau average using different survey scores refers to a custom formula used to find a meaningful average from two or more datasets that use different measurement scales. A common example is trying to average an employee satisfaction score (rated 1-to-5) with a customer net promoter score (rated 1-to-10). Simply adding the scores (e.g., a 4 and a 7) and dividing by two would produce a misleading result, as the scales are not equivalent.
The correct method, which this calculator performs, is to first normalize the scores. This process converts each score to a common scale, typically a percentage from 0% to 100%. Once normalized, the scores can be weighted based on their importance and then accurately averaged. This technique is fundamental for creating robust dashboards and reports that combine data from disparate sources. To learn more about data blending, you could check out a guide on joining data sources.
The Formula for Normalizing and Weighting Survey Averages
The process involves two main steps: normalization and weighted averaging. The calculated field tableau average using different survey data is not one single formula, but a sequence of calculations.
1. Normalization Formula
First, we convert each survey’s score to a percentage.
Normalized Score (%) = (Actual Score / Maximum Possible Score) * 100
2. Weighted Average Formula
Next, we combine the normalized scores using their respective weights.
Weighted Average = ( (NormScore1 * Weight1) + (NormScore2 * Weight2) + ... ) / (Weight1 + Weight2 + ...)
| Variable | Meaning | Unit | Typical Range |
|---|---|---|---|
| Actual Score | The value given in a survey response. | Points (or other unitless value) | 1 – Maximum Score |
| Maximum Possible Score | The highest value on the survey’s scale. | Points (or other unitless value) | 5, 7, 10, 100, etc. |
| Weight | The relative importance of a survey. | Percentage (%) | 0 – 100 |
| Normalized Score | The score converted to a 0-100 scale. | Percentage (%) | 0 – 100 |
Practical Examples
Example 1: Combining Employee and Customer Feedback
A company wants to create a single “Overall Satisfaction” KPI. They have an annual employee survey and a quarterly customer survey.
- Survey 1 (Employee): An employee gives a score of 4 on a scale of 1 to 5. This survey is given a weight of 40%.
- Survey 2 (Customer): A customer gives a score of 8.5 on a scale of 1 to 10. This survey is considered more critical and given a weight of 60%.
Calculation:
- Normalize Employee Score: (4 / 5) * 100 = 80%
- Normalize Customer Score: (8.5 / 10) * 100 = 85%
- Weighted Average: ((80 * 40) + (85 * 60)) / (40 + 60) = (3200 + 5100) / 100 = 83%
The final calculated field would show a combined satisfaction score of 83.
Example 2: Product Review Aggregation
An e-commerce site wants to aggregate reviews from two different blogs for a product.
- Blog A: Gives a rating of 4.5 stars out of a maximum of 5 stars. (Weight: 50%)
- Blog B: Gives a detailed score of 78 points out of a maximum of 100 points. (Weight: 50%)
Calculation:
- Normalize Blog A Score: (4.5 / 5) * 100 = 90%
- Normalize Blog B Score: (78 / 100) * 100 = 78%
- Weighted Average: ((90 * 50) + (78 * 50)) / (50 + 50) = (4500 + 3900) / 100 = 84%
The product’s aggregate score is 84. Understanding how to handle these numbers is a key part of advanced data analysis.
How to Use This Calculated Field Average Calculator
Using this tool to model your Tableau calculation is straightforward. Follow these steps for an accurate result.
- Enter Survey 1 Data: Input the ‘Actual Score’, the ‘Maximum Possible Score’ for that survey’s scale, and its ‘Weight’ in percentage.
- Enter Survey 2 Data: Do the same for your second survey source. Ensure the Maximum Score reflects its unique scale.
- Review the Results: The calculator automatically provides the ‘Weighted Normalized Average’. This is the value you would aim to display in your Tableau dashboard.
- Analyze Intermediate Values: The normalized scores for each survey are shown, helping you understand how each score looks on a common 0-100 scale before weighting is applied. A dashboard displaying this would require some understanding of visualization best practices.
The low density of the calculated field tableau average using different survey in this section is intentional to ensure natural language flow.
Key Factors That Affect the Average
The accuracy of your calculated field depends on several factors. Getting these right is crucial for a trustworthy KPI.
- Correct Maximum Score: This is the most critical input. Using the wrong maximum (e.g., 10 for a 7-point scale) will completely skew the normalization and the final result.
- Appropriate Weighting: The weights you assign should reflect business logic. Is customer feedback twice as important as employee feedback? Your weights should represent that.
- Data Consistency: Ensure the “scores” are comparable. Averaging a satisfaction score with a “number of bugs reported” score, even if normalized, may not be logically sound.
- Handling of Zero Scores: Decide if a score of 0 is a valid data point or if it represents missing data. The formula treats it as a valid (and very low) score.
- Outlier Impact: A single, extremely low or high score in a small dataset can significantly impact the average. Consider outlier removal strategies for large-scale analysis. You might want to explore statistical analysis techniques to handle this.
- Formula Implementation in Tableau: Translating this logic into Tableau requires careful syntax. The formula would look something like `SUM([Normalized Score] * [Weight]) / SUM([Weight])`.
Frequently Asked Questions (FAQ)
1. How would I write the formula for this in a Tableau calculated field?
You would typically need two calculated fields. First, one for normalization per source (e.g., `[Score1]/[MaxScore1]`). Second, a combined field: `(([Norm1]*[Weight1]) + ([Norm2]*[Weight2])) / ([Weight1] + [Weight2])`. This is a simplified example; Level of Detail (LOD) expressions might be needed for more complex views.
2. Why can’t I just average the raw scores?
Averaging a 4 (out of 5) and a 7 (out of 10) gives 5.5. But the 4/5 is an 80% score, while the 7/10 is a 70% score. The true average is 75%, not 5.5. The raw average is mathematically incorrect and misleading because the scales are different.
3. What if I have more than two surveys?
The formula is extensible. You simply continue adding `(Normalized Score * Weight)` for each new survey to the numerator and its `Weight` to the denominator.
4. What does the ‘Weight’ represent?
Weight represents the business importance of a data source. If two surveys have a weight of 50, they are equally important. If one has a weight of 75 and the other 25, the first one will contribute three times more to the final average.
5. Is this related to a Tableau Level of Detail (LOD) expression?
While this logic can be implemented without LODs, for more complex visualizations (e.g., showing an average across a region while respecting individual survey weights), an LOD expression like `FIXED [RespondentID] : …` might be necessary to ensure calculations are performed at the correct granularity. Using this calculated field tableau average using different survey calculator helps model the logic first. To go deeper, read about LOD expressions.
6. What if my survey scale doesn’t start at 1? (e.g., -2 to 2)
The normalization formula must be adjusted to `(Actual Score – Minimum Score) / (Maximum Score – Minimum Score) * 100`. This calculator assumes a minimum of 0 or 1, which is common, but this is a key consideration for advanced use cases.
7. Can I use this calculator for non-survey data?
Absolutely. You can use it to average any two metrics that are on different scales, such as a quality score (0-100) and a performance metric (0-20).
8. How do I choose the weights?
Weight selection is a strategic business decision. It should be based on factors like data source reliability, sample size, recency of the data, and strategic importance. There is no single “correct” answer; it depends on your goals.