Incomplete data is part of life and almost all areas of scientific studies. Users tend to skip certain fields when they fill out online forms; participants choose to ignore sensitive questions on surveys; sensors fail, resulting in the loss of certain readings; publicly viewable…
Maximizing data quality may be especially difficult in trauma-related clinical research. Strategies are needed to improve data quality and assess the impact of data quality on clinical predictive models. This study had two objectives. The first was to compare missing data betwee…
This dissertation is composed of three papers which address the problem of variable selection for models with missing data. In the first paper, we consider variable selection for generalized linear models with missing data, including missing covariate and/or response data. The s…
This dissertation consists of three essays which explore scenarios where data quality issues interfere with the goals of empirical research. These situations motivate closer analysis of existing econometric methods and even the provision of new methods to account for the deficie…
