TY - JOUR
T1 - A journey from wild to textbook data to reproducibly refresh the wages data from the National Longitudinal Survey of Youth Database
AU - Amaliah, Dewi
AU - Cook, Dianne
AU - Tanaka, Emi
AU - Hyde, Kate
AU - Tierney, Nicholas
N1 - Publisher Copyright:
© 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Data cleaning
KW - Data tidying
KW - Initial data analysis
KW - Longitudinal data
KW - NLSY79
KW - Reproducible workflow
UR - https://www.scopus.com/pages/publications/85134843263
U2 - 10.1080/26939169.2022.2094300
DO - 10.1080/26939169.2022.2094300
M3 - Article
AN - SCOPUS:85134843263
SN - 2693-9169
VL - 30
SP - 289
EP - 303
JO - Journal of Statistics and Data Science Education
JF - Journal of Statistics and Data Science Education
IS - 3
ER -