Georeferenced data are often anonymized for data protection reasons. This is done either by aggregating the data into larger spatial units (e.g., higher-level administrative units or grids with larger cell sizes) or by using stochastic methods to deliberately overlay the original coordinates. These methods significantly distort the data and associated variables, making further modeling steps...
Comparisons of educational achievement across cohorts are frequently used to examine changes in educational performance and to evaluate the impact of societal or educational events, such as the COVID-19 pandemic. However, drawing causal inferences from observational data without strong experimental control remains challenging, as often no single clear methodological approach is universally...
Unions decide how to allocate their resources to organise new workers and to benefit existing members. While there is a growing literature studying the aggregate effects of shocks to union power, little is known about how individual unions change their behaviour in response to these shocks. We illustrate the importance of accounting for union equilibrium responses when studying the impact of...
A central assumption for identifying structural shocks in vector autoregressive (VAR) models via heteroskedasticity is the time-invariance of the impact effects of the shocks. It is shown how that assumption can be tested when long-run restrictions based on the cointegration structure of the variables are available for identifying structural shocks. The importance of performing such tests is illustrated ...
Multiple imputation of missing values in survey data analysis is a state-of-the-art technique. Typically, methods like multivariate imputation by chained equations (mice, van Buuren 2018) are employed, replacing missing values on a variable-by-variable basis. Generally, the information used for imputation comes from the survey dataset being analysed. Valid analysis results are achieved when the...
FAIRness of research data, meaning that data are managed according to the principles of being Findable, Accessible, Interoperable, and Reusable, has become a ubiquitous requirement in research data policies as well as in general guidelines for research data management. Meeting this requirement largely depends on the availability of rich and standardized DDI metadata—based on the Data Documentation ...
