{"id":640,"date":"2023-03-29T22:05:03","date_gmt":"2023-03-29T19:05:03","guid":{"rendered":"https:\/\/acua.qcri.org\/blog\/?p=640"},"modified":"2023-03-29T22:05:03","modified_gmt":"2023-03-29T19:05:03","slug":"a-discussion-of-the-validity-of-data-analytics","status":"publish","type":"post","link":"https:\/\/acua.qcri.org\/blog\/a-discussion-of-the-validity-of-data-analytics\/","title":{"rendered":"A Discussion of the Validity of Data Analytics"},"content":{"rendered":"
\"Understanding
Understanding Audiences, Customers, and Users via Analytics \u2013 An Introduction to the Employment of Web, Social, and Other Types of Digital People Data<\/figcaption><\/figure>\n

A Discussion of the Validity of Data Analytics chapter from our forthcoming book, Understanding Audiences, Customers, and Users via Analytics, covers the following.<\/p>\n

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.<\/p>\n

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.<\/p>\n

In this chapter, you will better understand data validity, the major causes of data validity, and how to account for and correct data validity.<\/p>\n

Jansen, B. J., Aldous, K, Salminen, J., Almerekhi, H. and Jung, S.G. (2023). Understanding Audiences, Customers, and Users via Analytics \u2013 An Introduction to the Employment of Web, Social, and Other Types of Digital People Data<\/span>. Springer Nature.<\/p>\n","protected":false},"excerpt":{"rendered":"

A Discussion of the Validity of Data Analytics chapter from our forthcoming book, Understanding Audiences, Customers, and Users via Analytics, covers the following. 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. 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