@inbook{f9ed30b45e19491baa613d05445ca503,
title = "A Discussion of the Validity of Data Analytics",
abstract = "Validity is often understood as the “correctness” of a method or instrument. However, many data collection issues can degrade the validity of findings about people. Data validity is also a crucial issue for web and social media analytics. Data validity is data accuracy, where accuracy is the degree to which the data conforms to actual values. If the data has high validity, it means that the values in the dataset correspond to true properties in the physical (or virtual) world. If the numbers do not correspond to the actual values, there is a data validity issue; in other words, the data validity is low. In this chapter, you will gain a better understanding of data validity, the major causes of data validity, and how to account for and correct data validity.",
author = "Jansen, \{Bernard J.\} and Aldous, \{Kholoud K.\} and Joni Salminen and Hind Almerekhi and Jung, \{Soon gyo\}",
note = "Publisher Copyright: {\textcopyright} 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.",
year = "2024",
month = sep,
day = "6",
doi = "10.1007/978-3-031-41933-1\_12",
language = "English",
series = "Synthesis Lectures on Information Concepts, Retrieval, and Services",
publisher = "Springer Nature",
pages = "139--145",
booktitle = "Synthesis Lectures on Information Concepts, Retrieval, and Services",
}