Normalized value for an assessment

  • Release version: Washingtondc
  • Updated February 1, 2024
  • 2 minutes to read
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    Summary of Normalized Value for an Assessment

    The normalized value is a calculated figure that helps in risk assessment based on a linear equation and the defined scale of a metric. It is essential for understanding how specific metrics perform relative to their defined scales, thus aiding in the evaluation of service capabilities.

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    Key Features

    • Calculation Method: The normalized value is derived from the formula: Normalized value = (Input Value - Min value) / (Max value - Min value) (current metric weight bias / (sum of all valid metric weights)) scalefactor.
    • Bias Calculation: Bias reflects the ratio of total metric weight to the sum of valid metric weights, excluding scripted metrics.
    • Metric Exclusions: Certain metric types such as String, Date, Date/Time, Reference, Attachment, and Ranking are not included in the normalized value calculation.
    • Multiple Selection Metrics: For multiple selection metrics, the normalized value accounts for the weight of each choice using a modified formula.

    Key Outcomes

    By utilizing the normalized value, ServiceNow customers can effectively assess various metrics for risk evaluation, leading to informed decision-making. The calculations help in translating raw assessment responses into meaningful data that reflects performance against established criteria.

    The normalized value is calculated based on a linear equation and the scale definition of the metric. This value can be used for risk assessment.

    Normalized value for any metric

    The normalized value is directly proportional to the scale definition of the metric. If the scale definition is low, that is, the lower scale values are better, then Normalized value = 1.0 – Normalized value.

    For the reporting purpose, use the Metric Result [asmt_metric_result] table.

    Normalized value = (Input Value - Min value defined in metric) / (Max value defined in metric - Min value defined in metric) * (current metric weight * bias / (sum of all metric weight in the metric category)) * scale_factor

    Bias is the ratio of total metric weight in the category and the sum of valid metric weight in the metric category. While calculating bias, the scripted metrics are excluded.
    Note:
    • If a metric is skipped when taking the assessment, its weight is excluded when calculating sum of valid metric weight in the metric category.
    • The following metric types are excluded in the normalized value calculation:
      • String
      • Date
      • Date/Time
      • Reference
      • Attachment
      • Ranking

    For example, consider the following scenario.

    Calculate the normalized value for the Please rate the competency of the technician metric.

    Table 1. Values of the metric
    Input value 3
    Minimum value 1
    Maximum value 6
    Current metric weight 10
    Number of metrics in the metric category 6
    • 4 of type=number
    • 1 of type=yes/no
    • 1 of type=string (invalid data type; value cannot be calculated)
    Valid metric weight of each response 10
    Scale factor 10

    Normalized value = (3 - 1) / (6 - 1) * (10 / (10 + 10 + 10 + 10 + 10)) * 10 = 0.8

    Normalized value for a multiple selection metric

    The normalized value for a multiple selection metric is calculated by using the weight of the metric and the score for each choice of the metric.

    In a multiple selection metric, for each choice that should be used for the normalization calculation, define the normalization input value.

    Normalized value = (Score of all choices) * (current metric weight / (sum of valid metric weight in the metric category)) * scale_factor

    Here, score of all choices of the metric is the sum of individual scores of each choice.
    • Score of each choice in a multiple selection metric= Normalization input of the choice / max value of the metric
    • max value of the metric = Sum of the normalization input for all choices of the metric
    • min value of the metric is always 0

    For example, consider the following scenario.

    Calculate the normalized value for the multiple selection metric, Please rate the competency of the technician, with three choices, A, B, and C.

    Table 2. Values of the metric
    Choice A Normalization input is 1
    Choice B Normalization input is 1
    Choice C Normalization input is 2
    Minimum value 0
    Maximum value 4
    Current metric weight 10
    Number of metrics in the metric category 5
    Valid metric weight of each metric 10
    Scale factor 10

    If Choice A and B are selected, Normalized value = ((1 / 4) + (1 / 4)) * (10 / (10 + 10 + 10 + 10 + 10)) * 10 = 1

    Weighted value for a risk assessment

    For a risk assessment, the weighted value from metric results table is calculated as following.

    weighted_value = metric.weight * result.actual_value