The aim of this course is to train students in scientifically preparing a valid and reliable questionnaire. Students will first learn about the possibilities and constraints of the questionnaire as a scientific method of data collection. Students will learn to critically assess various types of modes (F2F, telephone, mail, Internet), question formats, response formats, and question order. Question format effects, as well as order, mode, context, and informant effects, are an integral part of the theory. Students will be taught how to move from conceptual research questions to recordable questionnaire data through the process of operationalisation. They will apply the theory on their own questionnaire and learn how to improve its quality by means of hands-on practical exercises, paying attention to the ethical and practical issues involved in using questionnaires. Attention will also be paid to issues involved with using or adapting an existing instrument. Finally, they will be taught how to prepare questionnaire data for analysis, how to analyze questionnaire data, how to integrate data of multiple informants, and how to report the results.
Specific goals and objective for the student
After successful completion of this course students are expected to be able to:
- Reproduce the main characteristics of the questionnaire as a scientific means of data collection and compare it with other methods of data collection (observation, open interview, etc.)
- Identify the main factors that are decisive for the validity and reliability of a questionnaire
- Design a questionnaire by operationalizing research questions into topics, indicators, and items
- Construct a simple questionnaire or adapt an existing questionnaire
- Prepare questionnaire data for analysis, integrate information of multiple informants and conduct simple analyses
- Reflect on their own performance in constructing a questionnaire and critically judge the quality of a questionnaire conducted by somebody else
This course consists of six sessions (morning and afternoon) done in 3 days. At each meeting the instructor will lecture on some theoretical aspects of the construction, conducting or analysis of questionnaires, introduce insightful exercises, and provide professional feedback to the work of students. Students will work in pairs on the exercises. This enables them to critically reflect on each other's performance.
Day 1: Developing a questionnaire
- Introduction: the questionnaire as a scientific method for data collection
- Formulating research questions and operationalisation
- Questionnaire mode (and its effects on validity)
- Question format, answering format and question order (and its effects on validity)
- Sampling design
Day 2: Collecting questionnaire data
- Formatting a questionnaire
- Guidelines for administration, including ethical issues
- Pilot testing your questionnaire and further refinement
- Using or adapting an existing questionnaire
- Multiple informant approach and informant effects
As a pair format your can format your own questionnaire (or one provided by the teacher) and pilot your questionnaire with three different persons in English. Based on your own experiences and peers’ comments improve you questionnaire.
Day 3: Preparing and analyzing questionnaire data
- Preparing questionnaire data for analysis
- Handling missing data
- Scale construction by practically applying factor analysis to questionnaire data. Factor analysis will be explained (without focus on mathematics and formulae). The difference between exploratory and confirmatory techniques will also be covered.
- Handling and evaluating multiple informant data
- Finally, we will practice the use of factor analysis on sample data in SPSS in a hands-on lab session in the afternoon.
This course is useful for PhD students and researchers of all scientific disciplines in need of knowledge of the questionnaire as a scientific method of data-collection. The course is especially useful for students who intend to use questionnaires for their own research.
A good understanding of basic statistics is required at least at a theoretical level: students are expected to master descriptive statistics (frequency distributions, cross tables, mean, mode, median, pie charts, etc.), and measures of dispersion (variance, sd), confidence intervals and the classical z-tests and t-tests, correlation, and linear regression techniques and preferably factor analysis.
The FLAMES prices for interuniversitary courses are used:
- PhD students and postdoctoral researchers of a Flemish university: free of charge
- Other academics: € 180
- Non-profit or social sector: € 300
- Private sector: € 600
Hasselt University - campus Diepenbeek Building D - (exact rooms to be announced)