Quantitative research
Quantitative research collects and analyzes numerical data to measure, compare and test: how many customers prefer an option, how satisfaction changed, whether a difference between groups is statistically real. It favors large samples and standard measures.
How it works
Common methods include surveys with closed questions, experiments such as A/B tests, analytics, and quantitative usability studies that measure task success and time. The design fixes the questions and measures in advance so every respondent is measured the same way, and the sample is large enough, and representative enough, to generalize from.
Analysis uses descriptive statistics (means, percentages) and inferential statistics (confidence intervals, significance tests) to judge whether results reflect the population or chance. Quantitative research is strong on scale and precision, provided the sample represents the population and the questions measure what they are meant to. Sample sizes are set in advance from the precision the decision needs.
A worked example
Asked to choose between two plan designs, 58 percent of 1,200 streaming subscribers pick design B, with a margin of error of about 2.8 points at 95 percent confidence, so the preference holds above 50 percent. Interviews with 20 of the respondents trace the preference to one feature of B, which shows the monthly total up front.
Related
Automate this in Pulse
A quantitative study's closed answer options should cover what people would say unprompted, and the themes from a round of Pulse interviews show which options a draft questionnaire is missing.