What to do before pretesting data collection instruments?

The claim that pretesting “identify and fix problems or issues” is incomplete. Empirical pretesting of data collection instrument is most impactful when you – the researcher – have met 3 pre-requisites:

1. developed an idea of how the data collection instrument is supposed to function, e.g. Is it supposed to be self- or interviewer-administered?
2. consulted best practices in questionnaire design and product usability, e.g. should matrix questions be used to “save space” or would a mix of question types result in better data quality?
3. adopted a working knowledge of the intended users, e.g. who is this instrument for? Their education attainment, language skills, and “survey literacy” should be taken into account.

Here’s why: Pretesting is a method, not the researcher. It cannot “identify and fix problems or issues” until YOU are trained to identify and fix problems and issues.

SEE ALSO:

How to improve research questions for better data collection?

How to resolve common usability issues?

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