liveBook · Manning

Measurement

As explained in Effective Data Analysis

Measurement is the systematic assignment of values or categories to attributes, phenomena, or processes so they can be represented and analyzed.

Measurement turns abstract phenomena, events, objects, or processes into observable values or categories that support analysis and decisions. Good measurement connects a clearly defined concept to data that represent it consistently and accurately. section 6.0 section 6.1

Designing a useful measure typically requires you to:

  • Define the phenomenon and enumerate its components before attempting to quantify it. section 6.1
  • Choose an appropriate measurement scale, use available data where suitable, and identify proxy measures when direct capture is impossible. section 6.1
  • Review and test whether the measure remains consistent, accurate, and stable over time. section 6.0 section 6.1
  • Treat every metric as a reasoned choice with strengths, limitations, and possible risks of misleading conclusions. section 6.0

Measuring software usability

A self-report measure can ask how often respondents use a new feature before asking whether it improved usability. Separating usage from evaluation and choosing question order carefully reduces the risk that one question primes the next response. section 6.2.2

Figure 6.7 How do you think the wording of these questions might affect the responses you receive?
figure

Use the figure to examine how question wording can affect the responses a measure receives. section 6.2.2

How Effective Data Analysis covers it

How deep it goes
Explains conceptualization, scales, proxies, and testing, while stopping short of detailed statistical procedures or full instrument construction.
Examples
Uses stress measurement, organizational data, self-report questionnaires, software usability questions, response scales, and question-order comparisons.
What it assumes
Assumes an analytics context but teaches from the difficulty of turning abstract or messy information into quantifiable concepts.
What you can do after
Design and evaluate measures that represent target phenomena consistently, accurately, and appropriately for analysis.
How it connects
Connects measurement with measurement theory, operationalization, measurement scales, proxy measures, self-report measures, and stakeholder communication.

Also explained in

Compare how 4 books explain measurement →

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.