Quantitative research in vocational education and training (VET) is routinely affected by missing or incomplete information. However, the handling of missing data in published VET research is often sub-optimal, leading to a real risk of generating results that can range from being slightly biased to being plain wrong. Given that the growing availability of data from large-scale surveys and administrative collections offers exciting new opportunities for quantitative VET research, it is important that researchers follow best-practice approaches when using such data in their own work. Against this backdrop, we: (1) provide a primer on the use of appropriate missing data methods for quantitative VET research; and (2) illustrate the detrimental effects of inefficient methods on research results via a simulation study using real-world education and training data from the Longitudinal Surveys of Australian Youth (LSAY).
- Large-scale datasets
- Missing data
- Missing data bias
- Multiple imputation
- Vocational education and training