Measurement Error Longitudinal Data (19 results)

Hardcover. Condition: New. Dust Jacket Condition: New. First Edition. Publisher's hardback in new condition: firm and square with strong joints, no bumps, no rubs. Contents are crisp, tight and clean; no pen-marks. Thus a very nice copy that looks and feels unread, now offered for sale at a very reasonable price.…

Condition: New.

Condition: New. 1st edition NO-PA16APR2015-KAP.

Condition: New.

Condition: New.

Condition: New.

Condition: New.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: new.

Condition: As New. Unread book in perfect condition.

Hardcover. Condition: Brand New. 496 pages. 9.25x6.25x1.00 inches. In Stock.

Condition: As New. Unread book in perfect condition.

Hardcover. Condition: new. Hardcover. 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 theworld are often imperfect, and longitudinal data is vulnerable to measurement errors which can lead to flawed and misleading conclusions.Measurement Error in Longitudinal Datatackles 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 datacollection, 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 mostimportant 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. Measurement Error in Longitudinal Data tackles the important issue of how to understand and estimate change in the context of imperfect data. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Hardback. Condition: New. New copy - Usually dispatched within 4 working days.

Condition: New.

Condition: New.

Hardcover. Condition: Brand New. 496 pages. 9.25x6.25x1.00 inches. In Stock.

Buch. Condition: Neu. Neuware - 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.…