Applied Missing Data Analysis (Methodology in the Social Sciences)
Product Description
Condition - Very Good
The item shows wear from consistent use but remains in good condition. It may arrive with damaged packaging or be repackaged.
Applied Missing Data Analysis (Methodology in the Social Sciences)
Walking readers step by step through complex concepts, this book translates missing data techniques into something that applied researchers and graduate students can understand and utilize in their own research. Enders explains the rationale and procedural details for maximum likelihood estimation, Bayesian estimation, multiple imputation, and models for handling missing not at random (MNAR) data. Easy-to-follow examples and small simulated data sets illustrate the techniques and clarify the underlying principles. The companion website (www.appliedmissingdata.com) includes data files and syntax for the examples in the book as well as up-to-date information on software. The book is accessible to substantive researchers while providing a level of detail that will satisfy quantitative specialists.
Technical Specifications
You might also like
Principles and Practice of Structural Equation Modeling, Fourth Edition (Methodology in the Social Sciences)
Missing Data (Quantitative Applications in the Social Sciences)
Longitudinal Structural Equation Modeling (Methodology in the Social Sciences)
Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (Methodology in the Social Sciences)
Confirmatory Factor Analysis for Applied Research, Second Edition (Methodology in the Social Sciences)
Flexible Imputation of Missing Data (Chapman & Hall/CRC Interdisciplinary Statistics)
The Reviewer’s Guide to Quantitative Methods in the Social Sciences
Statistical Analysis with Missing Data
Growth Modeling: Structural Equation and Multilevel Modeling Approaches (Methodology in the Social Sciences)
Advanced R (Chapman & Hall/CRC The R Series)