Contents Now Platform Capabilities Previous Topic Next Topic Example: calculate the normalized value for a survey metric Subscribe Log in to subscribe to topics and get notified when content changes. ... SAVE AS PDF Selected Topic Topic & Subtopics All Topics in Contents Share Example: calculate the normalized value for a survey metric The normalized value is calculated based on a linear equation and the scale definition of the metric. Equation used to calculate the normalized value Normalized value = (Input Value - Min value defined in metric) / (Max value defined in metric - Min value defined in metric) * current metric weight / (sum of valid metric weight) * scale_factor Note: The normalized values are 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. Example Calculate the normalized value for the Please rate the competency of the technician metric. The metric has the following values: Table 1. Values of the metric Input value 3 Minimum value 1 Maximum value 6 Current metric weight 10 Number of responses 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 Several data types are ignored because the values cannot be calculated. These invalid data types include string, date, and datetime. For reporting purposes, use the Metric Result [asmt_metric_result] table. On this page Send Feedback Previous Topic Next Topic
Example: calculate the normalized value for a survey metric The normalized value is calculated based on a linear equation and the scale definition of the metric. Equation used to calculate the normalized value Normalized value = (Input Value - Min value defined in metric) / (Max value defined in metric - Min value defined in metric) * current metric weight / (sum of valid metric weight) * scale_factor Note: The normalized values are 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. Example Calculate the normalized value for the Please rate the competency of the technician metric. The metric has the following values: Table 1. Values of the metric Input value 3 Minimum value 1 Maximum value 6 Current metric weight 10 Number of responses 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 Several data types are ignored because the values cannot be calculated. These invalid data types include string, date, and datetime. For reporting purposes, use the Metric Result [asmt_metric_result] table.
Example: calculate the normalized value for a survey metric The normalized value is calculated based on a linear equation and the scale definition of the metric. Equation used to calculate the normalized value Normalized value = (Input Value - Min value defined in metric) / (Max value defined in metric - Min value defined in metric) * current metric weight / (sum of valid metric weight) * scale_factor Note: The normalized values are 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. Example Calculate the normalized value for the Please rate the competency of the technician metric. The metric has the following values: Table 1. Values of the metric Input value 3 Minimum value 1 Maximum value 6 Current metric weight 10 Number of responses 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 Several data types are ignored because the values cannot be calculated. These invalid data types include string, date, and datetime. For reporting purposes, use the Metric Result [asmt_metric_result] table.
