Estimating and correcting for multiple types of measurement errors in longitudinal studies

Survey data, in its myriad forms, is essential in modern societies for policy development, business and marketing, as well as academic research. Longitudinal datasets (where the same individuals are interviewed on repeated occasions over time), are particularly important because they enable the analysis of ‘within-individual’ change which is increasingly recognised as essential for developing causal explanations of the social world. Although the importance of longitudinal data is widely recognised, research on the quality and accuracy of longitudinal data is surprisingly sparse. Measurement error is particularly problematic in longitudinal data, because it can incorporate multiple types of errors that appear at different time points. The central aim of the proposed project was to develop a new framework for estimating measurement error that could be applied to longitudinal data structures. The model developed in this project makes it possible to both estimate and correct:
- Method effects (where the response scale used for the question biases answers);
- Acquiescence (where respondents tend to agree to questions regardless of their content);
- Social desirability (where respondents provide answers in ways that are considered socially desirable);
- Cross-cultural effects (where measurement errors vary cross-culturally).
In addition to controlling for these different types of measurement error, the methods developed enable the evaluation of how error changes over time.