Applied Linear Regression for Longitudinal Data : With an Emphasis on Missing Observations

By:

Tan , Frans E.S [author.]

Contributor(s):

Jolani, Shahab [co-author.]

Material type:

TextLanguage: English Series: Chapman & Hall/CRC Texts in Statistical Science SeriesPublication details: Boca Raton : CRC Press/Taylor & Francis, 2023.Description: xxi, 226p. : ill. ; 24 cmISBN:

9780367634315

Subject(s):

Other classification:

B28 R3

Summary: This book introduces best practices in longitudinal data analysis at intermediate level, with a minimum number of formulas without sacrificing depths. It meets the need to understand statistical concepts of longitudinal data analysis by visualizing important techniques instead of using abstract mathematical formulas. Different solutions such as multiple imputation are explained conceptually and consequences of missing observations are clarified using visualization techniques. Provides datasets and examples onlineGives state-of-the-art methods of dealing with missing observations in a non-technical way with a special focus on sensitivity analysisConceptualises the analysis of comparative (experimental and observational) studiesIt is the ideal companion for researchers and students in epidemiological, health, and social and behavioral sciences working with longitudinal studies without a mathematical background.

Item type:

Textual

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Holdings
Cover image	Item type	Current library	Home library	Collection	Shelving location	Call number	Materials specified	Vol info	URL	Copy number	Status	Notes	Date due	Barcode	Item holds	Item hold queue priority	Course reserves
	Textual	Central Science Library	Central Science Library			B28 R3 (Browse shelf(Opens below))					Available			SL1656003

Includes references and index.

This book introduces best practices in longitudinal data analysis at intermediate level, with a minimum number of formulas without sacrificing depths. It meets the need to understand statistical concepts of longitudinal data analysis by visualizing important techniques instead of using abstract mathematical formulas. Different solutions such as multiple imputation are explained conceptually and consequences of missing observations are clarified using visualization techniques. Provides datasets and examples onlineGives state-of-the-art methods of dealing with missing observations in a non-technical way with a special focus on sensitivity analysisConceptualises the analysis of comparative (experimental and observational) studiesIt is the ideal companion for researchers and students in epidemiological, health, and social and behavioral sciences working with longitudinal studies without a mathematical background.

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