Questionnaire design
Writing clear, unbiased questions and a survey people actually finish.
Free tool: Survey Length Estimator
Data collection methods: a plain-English guide
Data collection is where a study’s quality is won or lost — garbage in, garbage out. No analysis can rescue data gathered with a leading questionnaire, the wrong measurement scale, or an unreliable instrument. This guide covers how to choose a method and then build an instrument that produces data worth analysing — and how to plan for the data once you have it.
The core skills
Likert scales
What a Likert scale is, how many points to use, and how to analyse the responses.
Levels of measurement
Nominal, ordinal, interval, ratio — and why the level decides which analysis is legal.
Cronbach’s alpha
Internal-consistency reliability — does your scale’s items measure one thing?
Free tool: Cronbach’s Alpha Calculator
Data management plan
How you’ll store, organise, document, and share your data — written before collection.
How it fits together
Data collection runs from your design: choose a method (survey, interview, observation, secondary data) that fits the question and your sample → build the instrument — for a survey, a well-designed questionnaire using appropriate scales at the right level of measurement → check the instrument is reliable and valid → and have a data management plan in place before the first response lands. Each choice constrains what you can legitimately analyse later.
Use the tools as you work
- Survey Length Estimator — predict completion time and trim a survey before drop-off kills your response rate.
- Cronbach’s Alpha Calculator — compute the internal-consistency reliability of your scale.
- Validity vs reliability — measuring the right thing, and measuring it consistently.
Get the free Data Collection toolkit
A fill-in data-management-plan starter plus instrument and measurement templates for planning collection before you start — from Research Design Simplified. We’ll email you the download link.
Frequently asked questions
What are the main data collection methods?
Surveys and questionnaires, interviews, focus groups, observation, experiments/measurements, and using existing (secondary) data. The right one follows from your research question and methodology.
What is the difference between primary and secondary data?
Primary data you collect yourself for this study; secondary data already exists (datasets, records, prior studies). Secondary data is faster and cheaper but wasn’t collected for your exact question.
Why does data collection matter so much?
Because analysis can’t fix bad data. A biased instrument, the wrong measurement level, or an unreliable scale corrupts every result downstream — so quality has to be built in before collection starts.