Applied longitudinal data analysis : modeling change and event occurrence /

Bibliographic Details
Main Authors: Singer, Judith D. (Author), Willett, John B. (Author)
Corporate Author: Oxford Academic
Format: eBook
Language:English
Published: Oxford ; New York : Oxford University Press, [2003]
Subjects:
Online Access:Connect to the full text of this electronic book

MARC

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245 1 0 |a Applied longitudinal data analysis :  |b modeling change and event occurrence /  |c by Judith B. Singer and John B. Willett. 
264 1 |a Oxford ;  |a New York :  |b Oxford University Press,  |c [2003] 
300 |a 1 online resource ( xx, 644 pages) :  |b illustrations. 
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504 |a Includes bibliographical references and index. 
505 0 0 |t A Framework for Investigating Change over Time --  |t When Might You Study Change over Time? --  |t Distinguishing Between Two Types of Questions about Change --  |t Three Important Features of a Study of Change --  |t Exploring Longitudinal Data on Change --  |t Creating a Longitudinal Data Set --  |t Descriptive Analysis of Individual Change over Time --  |t Exploring Differences in Change across People --  |t Improving the Precision and Reliability of OLS-Estimated Rates of Change: Lessons for Research Design --  |t Introducing the Multilevel Model for Change --  |t What Is the Purpose of the Multilevel Model for Change? --  |t The Level-1 Submodel for Individual Change --  |t The Level-2 Submodel for Systematic Interindividual Differences in Change --  |t Fitting the Multilevel Model for Change to Data --  |t Examining Estimated Fixed Effects --  |t Examining Estimated Variance Components --  |t Doing Data Analysis with the Multilevel Model for Change --  |t Example: Changes in Adolescent Alcohol Use --  |t The Composite Specification of the Multilevel Model for Change --  |t Methods of Estimation, Revisited --  |t First Steps: Fitting Two Unconditional Multilevel Models for Change --  |t Practical Data Analytic Strategies for Model Building --  |t Comparing Models Using Deviance Statistics --  |t Using Wald Statistics to Test Composite Hypotheses About Fixed Effects --  |t Evaluating the Tenability of a Model's Assumptions --  |t Model-Based (Empirical Bayes) Estimates of the Individual Growth Parameters --  |t Treating TIME More Flexibly --  |t Variably Spaced Measurement Occasions --  |t Varying Numbers of Measurement Occasions. 
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