A Journey from Wild to Textbook Data to Reproducibly Refresh the Wages Data from the National Longitudinal Survey of Youth Database. Issue 3 (18th November 2022)

• Record Type: Journal Article
• Authors: Amaliah, Dewi; Cook, Dianne; Tanaka, Emi; Hyde, Kate; Tierney, Nicholas
• Is Part Of: Journal of Statistics and Data Science Education. Volume 30:Issue 3(2022)
• Page Start: 289
• Page End: 303
• Publication Date: 2022-11-18
• Subjects: Data cleaning -- Data tidying -- Initial data analysis -- Longitudinal data -- NLSY79 -- Reproducible workflow
• DOI: 10.1080/26939169.2022.2094300
• Languages: English
• ISSNs: 2693-9169

Abstract

Abstract: Textbook data is essential for teaching statistics and data science methods because it is clean, allowing the instructor to focus on methodology. Ideally textbook datasets are refreshed regularly, especially when they are subsets taken from an ongoing data collection. It is also important to use contemporary data for teaching, to imbue the sense that the methodology is relevant today. This article describes the trials and tribulations of refreshing a textbook dataset on wages, extracted from the National Longitudinal Survey of Youth (NLSY79) in the early 1990s. The data is useful for teaching modeling and exploratory analysis of longitudinal data. Subsets of NLSY79, including the wages data, can be found in supplementary materials from numerous textbooks and research articles. The NLSY79 database has been continually updated through to 2018, so new records are available. Here we describe our journey to refresh the wages data, and document the process so that the data can be regularly updated into the future. Our journey was difficult because the steps and decisions taken to get from the raw data to the wages textbook subset have not been clearly articulated. We have been diligent to provide a reproducible workflow for others to follow, which also hopefully inspires more attempts at refreshing data for teaching. Three new datasets and the code to produce them are provided in the open source R package called yowie . Supplementary materials for this article are available online.