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Informationen zum Autor Paul D. Allison, Ph.D. , is Professor of Sociology at the University of Pennsylvania where he teaches graduate courses in methods and statistics. He is also the founder and president of Statistical Horizons LLC which offers short courses on a wide variety of statistical topics. After completing his doctorate in sociology at the University of Wisconsin, he did postdoctoral study in statistics at the University of Chicago and the University of Pennsylvania. He has published eight books and more than 60 articles on topics that include linear regression, log-linear analysis, logistic regression, structural equation models, inequality measures, missing data, and survival analysis. Much of his early research focused on career patterns of academic scientists. At present, his principal research is on methods for analyzing longitudinal data, especially those for determining the causes and consequences of events, and on methods for handling missing data. A former Guggenheim Fellow, Allison received the 2001 Lazarsfeld Award for distinguished contributions to sociological methodology. In 2010 he was named a Fellow of the American Statistical Association. He is also a two-time winner of the American Statistical Association’s award for “Excellence in Continuing Education.” Klappentext Sooner or later anyone who does statistical analysis runs into problems with missing data in which information for some variables is missing for some cases. Why is this a problem? Because most statistical methods presume that every case has information on all the variables to be included in the analysis. Using numerous examples and practical tips, this book offers a nontechnical explanation of the standard methods for missing data (such as listwise or casewise deletion) as well as two newer (and, better) methods, maximum likelihood and multiple imputation. Anyone who has been relying on ad-hoc methods that are statistically inefficient or biased will find this book a welcome and accessible solution to their problems with handling missing data. Inhaltsverzeichnis Series Editor¿s Introduction 1. Introduction 2. Assumptions Missing Completely at Random Missing at Random Ignorable Nonignorable 3. Conventional Methods Listwise Deletion Pairwise Deletion Dummy Variable Adjustment Imputation Summary 4. Maximum Likelihood Review of Maximum Likelihood ML With Missing Data Contingency Table Data Linear Models With Normally Distributed Data The EM Algorithm EM Example Direct ML Direct ML Example Conclusion 5. Multiple Imputation: Bascis Single Random Imputation Multiple Random Imputation Allowing for Random Variation in the Parameter Estimates Multiple Imputation Under the Multivariate Normal Model Data Augmentation for the Multivariate Normal Model Convergence in Data Augmentation Sequential Verses Parallel Chains of Data Augmentation Using the Normal Model for Nonnormal or Categorical Data Exploratory Analysis MI Example 1 6. Multiple Imputation: Complications Interactions and Nonlinearities in MI Compatibility of the Imputation Model and the Analysis Model Role of the Dependent Variable in Imputation Using Additional Variables in the Imputation Process Other Parametric Approaches to Multiple Imputation Nonparametric and Partially Parametric Methods Sequential Generalized Regression Models Linear Hypothesis Tests and Likelihood Ratio Tests MI Example 2 MI for Longitudinal and Other Clustered Data
