A Total Error Framework for Digital Data
64th ISI World Statistics Congress - Ottawa, Canada
Format: CPS Abstract
Keywords: big data, quality frameworks;, quality-assessment
Session: CPS 50 - Statistical methodology V
Tuesday 18 July 4 p.m. - 5:25 p.m. (Canada/Eastern)
A changing survey landscape (Lyberg and Heeringa 2021) with increasing nonresponse rates and survey costs has caused organizations to explore new data sources for statistics production (Japec and Lyberg 2021). There is a great potential to use new types of data, hereafter called digital data, for statistics production especially when blending them with existing survey or administrative data (National Academies of Sciences, Engineering, and Medicine 2022, Japec et al 2015). Our quality framework build on existing frameworks for surveys (Groves and Lyberg 2010), administrative (Zhang 2012, Reid et al 2017), found (Biemer and Amaya 2021) and digital trace data (Sen et al 2021).
In our framework we describe steps taken when statistics are produced based on digital data and error sources associated with each step. Blending digital data with other data sources is a vital step in our quality framework. The framework offers standard terminology to describe and document errors in digital data. We connect terminology used in our framework to terminology used in TSE frameworks. We also provide indicators to be used to evaluate quality of statistics produced based on digital data. We will present examples from applying the framework on digital data at Statistics Sweden.
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