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# Normalize scores for metrics

# Normalize the scores for metrics {#ariaid-title1}

* Release version: Australia
* 
* Updated March 12, 2026
* 
* ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 minutes to read

Summarize  
![AI sparkle icon](https://servicenow.com/docs/portal-asset/ai-sparkle-icon) Summarized using AI  
This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete.  

## Summary of Normalize the scores for metrics

This document outlines how to use Maximum normalization input settings in ServiceNow to normalize scores for assessment metrics.
This is particularly relevant for Choice and Multiple Selection questions where unique values must be assigned to each metric definition.
Show full answer Show less  

## Key Features

* **Normalization Methods:** Two approaches are provided depending on whether Maximum normalization input is selected or not.
* **Choice Type Questions:**
  * Without Maximum normalization: Scores are calculated using the formula: (\[Value of the response\] -- \[Lowest value\]) / (\[Highest value\] -- \[Lowest value\]) 100.
  * With Maximum normalization: Scores are calculated as: (\[Value of the response\] / \[Highest value\]) 100.
* **Multiple Selection Type Questions:**
  * Without Maximum normalization: The formula used is (\[Sum of the normalization input values for the selected responses\] / \[Sum of all normalization input values\]) 100.
  * With Maximum normalization: The lowest value response is assigned a score of 0, and the score is calculated based on the highest normalization input values.

## Key Outcomes

Using Maximum normalization input allows for a standardized scoring method that can help assess responses more accurately based on their relative performance. This ensures that scores reflect the significance of each response in the context of all possible answers, providing clearer insights for decision-making.  
You can use the Maximum normalization input setting to use normalized values to calculate assessment scores for questions (metrics).

## When Maximum normalization input applies {#set-max-norm-input__section_pnh_fqg_lzb}

The Maximum normalization input field appears only when:

* The data type of the question is either Choice or Multiple Selection.
* The Scored check box is not selected.
{#set-max-norm-input__ul_v2m_ryg_lzb}

To use normalized scoring, the value assigned to each metric definition (possible answer) for each metric (question) must be unique.

In the following examples, the Scale definition is High (larger numerical values are good).

## Choice type questions {#set-max-norm-input__section_hcc_yvn_cyb}

When Maximum normalization input is not selected

:

    Formula: (\[Value of the response\] -- \[Lowest value\] ) / ( \[Highest value\] -- \[Lowest value\]) \*
    100.
    In this example, the question allows a choice among answers with values of `1`, `2`, and `4`.{#set-max-norm-input__table_bnt_flc_mzb__entry__3}

    | Response | Calculation | Score |
    |-|-|-|
    | Dog | \[(1-1)/(4-1)\] \* 100 | 0 |
    | Cat | \[(2-1)/(4-1)\] \* 100 | 33 |
    | Goldfish | \[(4-1)/(4-1)\] \* 100 | 100 |
    [Table 1. Scores]

    {#set-max-norm-input__table_bnt_flc_mzb}

When Maximum normalization input is selected

:

    Formula: `([Value of the response]/ [Highest value]) * 100`.

    In this example, the question allows a choice among answers with values of `1`, `2`, and `4` and normalization input values of `3`, `5`, and
    `9` respectively.
    {#set-max-norm-input__table_fqp_nlc_mzb__entry__3}

    | Response | Calculation | Score |
    |-|-|-|
    | Dog | (1 / 4) \* 100 | 25 |
    | Cat | (2 / 4) \* 100 | 50 |
    | Goldfish | (4 / 4) \* 100 | 100 |
    [Table 2. Scores]

    {#set-max-norm-input__table_fqp_nlc_mzb}

## Multiple selection type questions {#set-max-norm-input__section_r21_vlh_lzb}

When Maximum normalization input is not selected

:

    Formula: `([Sum of the normalization input values for the selected responses] / [Sum of all the normalization input values]) * 100`.

    In this example, the question allows for multiple selections among answers with values of `1`, `2`, and `4` and normalization input values of `3`,
    `5`, and `9` respectively.
    {#set-max-norm-input__table_bl2_tnc_mzb__entry__3}

    | Response | Calculation | Score |
    |-|-|-|
    | Dog and Cat | (\[3+5\] / 17) \* 100 | 47 |
    | Cat | (5 / 17) \* 100 | 29 |
    | Cat and Goldfish | (\[5+9\] / 17) \* 100 | 82 |
    | Goldfish | (9 / 17) \* 100 | 53 |
    [Table 3. Scores]

    {#set-max-norm-input__table_bl2_tnc_mzb}

When Maximum normalization input is selected

:

    * The system uses the value `0` for the response that has the lowest value. In this example, the Dog response is assigned the value `0`.
    * Formula for each selection: `([Highest normalization input value for the selected responses] / [Maximum of the normalization input values]) * 100`.
    * The score for the metric (question) is the maximum calculated score among all responses.
    {#set-max-norm-input__ul_wmd_3pp_lzb}  
    In this example, the user selects Dog and Cat.

    * The score for the Dog response is `(0 / 9) * 100 = 0`.
    * The score for the Cat response is `(5 / 9) * 100 = 55.5`.
    * The score for the overall metric is `55.5`.
    {#set-max-norm-input__ul_ds1_ypp_lzb}

    Formula: `(Highest of the normalization input values for the selected responses / Highest of all the normalization input values) * 100`.

    In this example, the question allows for multiple selections among answers with values of `1`, `2`, and `4` and normalization input values of `3`,
    `5`, and `9` respectively.
    {#set-max-norm-input__table_zqh_24c_mzb__entry__3}

    | Response | Calculation | Score |
    |-|-|-|
    | Dog and Cat | (5 / 9) \* 100 | 56 |
    | Cat | (5 / 9) \* 100 | 56 |
    | Cat and Goldfish | (9 / 9) \* 100 | 100 |
    | Goldfish | (9 / 9) \* 100 | 100 |
    [Table 4. Scores]

    {#set-max-norm-input__table_zqh_24c_mzb}