6 Are you measuring what you think you’re measuring?
This chapter covers
- The theoretical underpinnings of effective measurement
- Identifying the strengths and limitations of a measurement
- Reliably measuring information about your concept or process
- Ensuring your measures are valid representations of your concept or process
Measurement is central to data analysis, but turning data into something you can quantify is rarely straightforward. The information you need to answer meaningful questions is often hidden in unstructured formats or may not be recorded at all. While some organizations have well-structured systems that make capturing insights relatively simple, most of us contend with messy data or vague, unexplored ideas. Transforming those ideas into actionable insights requires rigor, creativity, and a strong grasp of the methods researchers use to make sense of our complex world.
The process of quantifying information is both an art and a science. Every method has its strengths and limitations, and as an analyst, you’ll often have the freedom to decide how best to approach your task. Along the way, you’ll need to consider key questions: Are your metrics reliable and consistent? Do they accurately reflect the phenomenon you’re studying? Without thoughtful evaluation, even the most carefully collected data can lead to flawed or misleading conclusions.