Name: Measurement Error in Longitudinal Data (en Inglés)
SKU: 54346262
Price: 209.75 USD
Availability: InStock
Author: Cernat, Alexandru ; Sakshaug, Joseph W.
ISBN: 9780198859987

portada Measurement Error in Longitudinal Data (en Inglés)

Measurement Error in Longitudinal Data (en Inglés)

Formato

Libro Físico

Ilustrado por

Alexandru Cernat

Joseph W. Sakshaug

Editorial

Oxford University Press

Autor

Cernat, Alexandru ; Sakshaug, Joseph W.

Idioma

Inglés

N° páginas

464

Encuadernación

Tapa Dura

Dimensiones

23.6x15.5x2.5 cm

Peso

0.88 kg.

ISBN13

9780198859987

N° edición

Categorías

Probabilidad y estadística
Matemáticas aplicadas
Ciencia: cuestiones generales

Measurement Error in Longitudinal Data (en Inglés)

Alexandru Cernat (Ilustrado por) · Joseph W. Sakshaug (Ilustrado por) · Oxford University Press · Tapa Dura

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Reseña del libro "Measurement Error in Longitudinal Data (en Inglés)"

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 organizations, 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. Measurement Error in Longitudinal Data tackles the important issue of how to investigate change in the context of imperfect data. It compiles the latest advances in estimating change in the presence of measurement error from several fields and covers the entire process, from the best ways of collecting longitudinal data, to statistical models to estimate change under uncertainty, to examples of researchers applying these methods in the real world. This book introduces the essential issues of longitudinal data collection, such as memory effects, panel conditioning (or mere measurement effects), the use of administrative data, and the collection of multi-mode longitudinal data. It also presents some of the most important models used in this area, including quasi-simplex models, latent growth models, latent Markov chains, and equivalence/DIF testing. Finally, the use of vignettes in the context of longitudinal data and estimation methods for multilevel models of change in the presence of measurement error are also discussed.