If you know a thing only qualitatively, you know it no more than vaguely.
Deep understanding, not broad coverage, is the strength of qualitative research.Quantitative (量化) vs Qualitative (質化)
- The way to distinguish between quantitative and qualitative data is to focus on the status of a single observation.
- Sample size does not determine whether data are quantitative or qualitative.
- Quantitative and qualitative data can be, and often are, collected in the same study.
- Rating scales are not designed to capture opinions, per se, but rather are designed to capture estimations of magnitude. Data from Likert scales and continuous (e.g. 1-10) rating scales are quantitative.
- 可以比較大小
- Qualitative data can be analyzed statistically, but cannot be compared in terms of magnitude.
- 無法比較大小
| Quantitative | Qualitative |
|---|---|
| any single observation is a number that represents an amount or a count | any single observation is a word, or a sentence, or a description that represents a category then the data |
| requires some precise measuring instrument | itself is the measuring instrument |
| usually during summative testing when measuring the usability of a system | usually during formative testing when identifying usability problems, such as cognitive walkthrough |
| attempting to prove something | attempting to understand something |
Subjective (主觀) vs Objective (客觀)
- Subjective data result from an individual's personal opinion or judgement and not from some external measure. Objective data are 'external to the mind' and concern facts that actually exist.
- Both quantitative and qualitative data can be objective or subjective.
| Quantitative | Qualitative | |
|---|---|---|
| Objective | "The chip speed of my computer is 2 GHz" | "Yes, I own a computer" |
| Subjective | "On a scale of 1-10, my computer scores 7 in terms of its ease of use" | "I think computers are too expensive" |
The discipline of usability is concerned with prediction, not opinions. Misunderstanding and misusing the above terms can reduce the value of a usability study, leading to wrong decisions. The consequences are a waste of company money and reduce people's confidence in what usability studies can deliver.
(Source: Usability Test Data by Philip Hodgson)
