In obtrusive data collection, the subjects are aware of the fact that they are being studied, which can influence their response or behaviour. Examples of obtrusive data collection methods are questionnaires or interviews.
Questionnaires are survey instruments that are completed by the subjects. Questionnaires, like interviews, can contain short closed-ended questions (multiple choice) or broad open-ended questions. Questionnaires are used to collect data from a large group of subjects on a specific topic. Currently, many questionnaires are developed and administered online.
BMS Survey software
BMS LAB offers two survey software tools which students and researchers of the University of Twente can use to develop their own online survey and collect research data. For more information, click here or navigate the menu on the left.
Interviews are used to collect data from a small group of subjects on a broad range of topics. You can use structured or unstructured interviews. Structured interviews are comparable to a questionnaire, with the same questions in the same order for each subject and with multiple choice answers. For unstructured interviews questions can differ per subject and can depend on answers given on previous questions, there is no fixed set of possible answers.
In unobtrusive data collection, subjects are not aware of the fact that they are being studied and therefore your research does not affect their response or behaviour. The three main types of unobtrusive data are indirect measures, content analysis and secondary analysis of data.
Indirect measures are unobtrusive data collected in an indirect way. These measures are often drawn from information recorded for other purposes than scientific research. Examples of indirect measures are car accidents, house prices, employment rates, social media posts or even garbage.
Content analysis is used to collect data from documentary sources, for example by extracting major themes, key words or features from (textual) documents. Content analysis is often used to convert textual sources into quantitative information.
Secondary analysis of data
Secondary analysis of data focuses on the re-use of quantitative data instead of textual data. For secondary analysis, information from electronic databases or open access research data depository can be used, like standardized testing data, economic data or consumer data. It is also possible to combine datasets from multiple sources. Find out more about using existing datasets or sharing your own research data.