Missing Data by Paul D Allison
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.-
Survey Questions
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Q Methodology
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Logistic Regression
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Reliability and Validity Assessment
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Latent Class Analysis
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ANOVA
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Nonparametric Statistics
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Factor Analysis
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Exploratory Data Analysis
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Introduction to Factor Analysis
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Interpreting and Using Regression
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Understanding Regression Analysis
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Bayesian Statistical Inference
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Applied Logistic Regression Analysis
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Multiple Regression in Practice
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Discriminant Analysis
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Confirmatory Factor Analysis
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Applied Regression
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Introduction to the Laws of Statistical Sampling
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Multiple Time Series Models
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Random Factors in ANOVA
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Logit Modeling
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Loglinear Models with Latent Variables
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Contextual Analysis
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Neural Networks
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Ordinal Log-Linear Models
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Social Choice
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Stochastic Parameter Regression Models
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Analytic Mapping and Geographic Databases
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Probability Theory
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Univariate Tests for Time Series Models
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Introduction to Applied Demography
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Data Theory and Dimensional Analysis
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Nonrecursive Causal Models
"…an excellent resource for researchers who are conducting multivariate statistical studies"
-- Richard A. Chechile
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.”
| SKU | Unavailable |
| ISBN 13 | 9780761916727 |
| ISBN 10 | 0761916725 |
| Title | Missing Data |
| Author | Paul D Allison |
| Series | Quantitative Applications In The Social Sciences |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | SAGE Publications Inc |
| Year published | 2001-10-03 |
| Number of pages | 104 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |