Measurement error in longitudinal data / edited by Alexandru Cernat, Joseph W. Sakshaug.
Τύπος υλικού: ΚείμενοΓλώσσα: Αγγλικά Λεπτομέρειες δημοσίευσης: [Oxford] : Oxford University Press, [2021].Έκδοση: 1st edΠεριγραφή: xii, 448 p. : ill. ; 24 cmISBN:- 9780198859987
- 23 519.53
Τύπος τεκμηρίου | Τρέχουσα βιβλιοθήκη | Συλλογή | Ταξιθετικός αριθμός | Κατάσταση | Ημερομηνία λήξης | Barcode | |
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Books | Βιβλιοθήκη Παιδαγωγικού Ινστιτούτου = Pedagogical Institute Library | Main | 519.53 MEA (Περιήγηση στο ράφι(Άνοιγμα παρακάτω)) | Διαθέσιμο | PEI00053538 |
Includes bibliographical references and index.
Longitudinal data is essential for understanding how the world around us changes. Most theories in the social sciences and elsewhere have a focus on change, be it of individuals, of countries, of organisations, or of systems, and this is reflected in the myriad of longitudinal data that are being collected using large panel surveys. This type of data collection has been made easier in the age of Big Data and with the rise of social media. Yet our measurements of the world are often imperfect, and longitudinal data is vulnerable to measurement errors which can lead to flawed and misleading conclusions. This book tackles the important issue of how to investigate change in the context of imperfect data.
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