Analyzing Rating-Scale Data

When people are asked to give feedback using rating scales, such as a survey that asks you to rate something from “Strongly Disagree” to “Strongly Agree,” researchers need a way to turn those opinions into something measurable. The most common approach is to assign numbers to each option and calculate the average.
 

For example, imagine you are using a five point scale where:
 

1 = Strongly Disagree
2 = Disagree
3 = Neutral
4 = Agree
5 = Strongly Agree
 

Once every participant has responded, you add up all the numbers and divide by the number of people to get an average. This makes it easier to compare opinions between different groups or across multiple studies.

That said, it is important to understand that rating scale data is not perfectly numerical. Unlike measurements such as height or temperature, where the difference between each value is consistent, the difference between choosing a 2 versus a 3 may not be the same as choosing a 4 versus a 5. Even so, many researchers treat these scales as evenly spaced because it simplifies analysis and works well enough for most practical use cases.

• Averages
  Shows overall player sentiment by summarizing responses into a single value.
   

• Distribution
  Reveals how responses are spread out, highlighting polarization or clusters.
   

• Segments
  Compares how different player groups respond, such as new vs. experienced players.
   

• Top-box
  Focuses on the strongest positive responses to show who truly loved something.
   

• Trends
  Tracks how responses change over time, such as improvement across builds or updates.
   

• Correlations
  Explores relationships between two variables

• Calculate mean ratings to get a general sense of how players felt.

• Compare averages across levels, game builds, or player segments.
   

How to calculate an average rating:
 

To calculate the average (mean) of a rating scale, assign a number to each response option, then add up all the responses and divide by the total number of players.
 

For example, if you use a 1 to 5 scale and receive these ratings:
5, 4, 4, 3, 4
 

You would add them together:
5 + 4 + 4 + 3 + 4 = 20
 

Then divide by the number of responses:
20 ÷ 5 = 4.0
 

That final number represents the average player rating.
 

🔎 Example:

• Level 1 Fun = 4.6

• Level 2 Fun = 2.8 → Might need work
   

⚠️ Caveat: Averages can hide strong differences in opinion.
 

A big trap is focusing only on the average score. Doing so can obscure important patterns in the data.
 

For example, imagine you asked 20 players whether they enjoyed a game level using a 1 to 7 scale. If the average response is a 4, you might assume players felt neutral. But if you look closer and see that 10 players rated the level a 1 and 10 rated it a 7, you would discover that no one felt neutral at all. The response was sharply divided between love and hate.

That insight is completely lost when you look only at the average.

This is why researchers also examine the distribution of responses, meaning how many players selected each number on the scale. Looking at the spread of responses provides a much more complete picture of player sentiment.