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Measurement scale

As explained in Effective Data Analysis

A rating scale is an ordered set of response options for indicating the degree or level of a characteristic, attitude, or judgment.

A measurement scale is a set of response options used to express the degree of a characteristic or judgment. In a survey, the scale helps turn a subjective experience, such as stress or customer sentiment, into data an analyst can compare. It is only one part of measurement: first define what you want to know, then choose or develop a scale that fits it and test whether the resulting measure remains accurate and stable. section 6.1 section 6.2.2

When designing or reviewing a rating scale, check the question as well as the response options:

  • Use precise wording. An ambiguous question can leave respondents answering different interpretations. section 6.2.2
  • Avoid leading language that suggests a desired answer, and separate distinct ideas rather than asking about both at once. section 6.2.2
  • Test changes in wording, presentation, and response format. Small variations can substantially change self-reported answers. section 6.2.2
Figure 6.6 Varying methods of presenting the same response format
figure

Compare ways of presenting the same response format; presentation is a design choice worth testing, not a neutral wrapper. section 6.2.2

An 11-point customer rating

Net promoter score (NPS) uses a 0–10 rating of likelihood to recommend, anchored by “not at all likely” and “extremely likely.” Responses are grouped as promoters, passives, or detractors; the percentage of detractors is subtracted from the percentage of promoters. The standardized score is easy to benchmark, but one question cannot explain the reasons behind a rating or fully capture customer experience, satisfaction, and loyalty. section 6.1.2

Treat the chosen scale as a claim about what the answers represent, not proof that the underlying concept has been captured. Before using the results for a decision, review the scale’s limitations and whether respondents can answer the question accurately. section 6.1 section 6.2.2

How Effective Data Analysis covers it

How deep it goes
Explains design checks for wording, formats, and testing, while stopping short of a full scale-development procedure.
Examples
Uses survey-question wording contrasts and NPS’s 0–10 likelihood-to-recommend rating.
What you can do after
Evaluate/understand a rating scale by checking its question, response format, and limits before interpreting the results.
How it connects
Connects rating scales to measurement theory, self-report measures, response bias, and net promoter score.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Effective Data Analysis. The section links point to the parts of the book it draws on.