Understanding Survey Scoring

Jump to the following sections:

What is Survey Scoring?
Why is Scoring Important?
How Metolius Makes Scoring Easy
Scoring in Metolius - The Three Components
Metolius Scoring Logic

What is Survey Scoring?

Scoring is a systematic approach used to quantify the responses of survey participants, enabling analysis of the data gathered from their answers. The scoring method can differ based on the survey type and its objectives. Generally, scoring entails assigning numerical values to the participants' responses, which can then be examined to uncover trends and patterns within the data.

For instance, in a Likert scale survey, participants might be asked to express their level of agreement with various statements using a scale from 1 to 5, where 1 signifies "strongly disagree" and 5 indicates "strongly agree." These scores are then compiled and analyzed to assess the overall agreement or disagreement regarding the statements presented.

In a similar vein, a Net Promoter Score (NPS) survey asks participants to indicate how likely they are to recommend a product or service to others on a scale of 0 to 10, with 0 representing "not at all likely to recommend" and 10 representing "extremely likely to recommend". The resulting scores are utilized to compute an overall NPS score, which serves as an indicator of customer loyalty and satisfaction.

Why is Scoring Important?

Scoring a survey is important because it establishes a systematic and objective framework for gathering and analyzing responses, empowering organizations to make informed, data-driven decisions based on insights from their customers or employees. By assigning numerical values or ratings to each response, organizations can easily identify trends and patterns in the data, enabling them to take actionable steps based on the feedback received.

Scoring also allows organizations to compare responses across different questions and different groups of participants, providing insights into areas of strength and weakness. For example, if a company conducts a customer satisfaction survey and finds that customers consistently rate their customer service experience low, they can focus on improving their customer service training and processes to address the issue.

Moreover, scoring also helps in making a comparison with benchmarks set by other organizations, industry standards, or results from a survey done previously within an organization. This comparison can provide valuable insights into how an organization is performing relative to its competitors, the industry as a whole, or its past, allowing it to identify areas where it needs to improve.

Overall, scoring a survey is an important step in the survey process as it provides a standardized and objective way to collect and analyze data, allowing organizations to make data-driven decisions and take action based on the feedback they receive.

How Metolius Makes Scoring Easy

Metolius makes scoring easy by doing the math for you! Once a response score range has been selected and the question responses have been assigned numerical values, results are immediately available after a participant has submitted their responses.

See this article on how to set up a survey for scoring.

The Three Components of Scoring in Metolius

1) Response Score Range

The response score range is defined at the survey level. This setting is found in the General Settings section of the survey when creating or editing a survey (Figure 1). This setting determines the minimum and maximum values and therefore establish the scoring boundaries for the survey. These defined limits are then applied when assigning value to each response option for every survey question.

A scoring range can vary depending on the type of survey being used, the goals and objectives of the survey and should be carefully selected to ensure that the data collected is relevant, useful, and actionable. For examples, see the section above "What is Survey Scoring" for explanations of a 5-point Likert scale and Net Promoter Score (NPS).

The response score range used in an survey is important because it helps to provide a standardized and objective way to collect and analyze data. By defining a clear scoring range at the outset of the survey process, organizations can ensure that the data collected is consistent and comparable across different responses and participants.

Figure 1

2) Response Value

With data analytic questions, each response option will be assigned a different scoring value. Values are assigned based on the importance or relevance of each response to the overall goals of the survey. In the figure below, a value of 1-5 is assigned to each response option to align with the 5-point scale set as the Response Score Range. Whichever option the participant chooses, the associated value will be calculated into their score.

Understanding-Survey-Scoring_Response-Block_Value

Figure 2

IMPORTANT - Automatic Scoring

The scoring value must mathematically align to the Response Score Range (RSR) that is selected in the General Settings section of the survey setup.

For example, If the RSR has been set to 0-5 but some of your questions have less than or more than 5 response options Metolius will automatically adjust the values for each response option so the highest scoring value is equal to five. To ensure balanced scoring, at least one response option must be assigned the maximum value of the set RSR. See the Value column in the screenshot below for an example.

Figure 3

3) Response Category

The response category column next to the value column adjusts how the score is calculated within the Question Analytics module. See this article for a more detailed explanation on Response Categories.

Positive - Choosing a positive response value adds the assigned value to the total score; Effectively increasing the overall value.

Neutral - Choosing a neutral response value excludes the value from the score and the overall value is not affected.

Negative - Choosing a negative response value subtracts the assigned value from the total score; Effectively reducing the overall value. Note that this is an advanced feature and most surveys do not need a negative.

Figure 4