Coverart for item
The Resource Missing data, Paul D. Allison

Missing data, Paul D. Allison

Label
Missing data
Title
Missing data
Statement of responsibility
Paul D. Allison
Creator
Subject
Language
eng
Summary
"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."--Pub. desc
Member of
Cataloging source
DLC
http://library.link/vocab/creatorName
Allison, Paul David
Dewey number
001.4/22
Illustrations
illustrations
Index
index present
LC call number
QA276
LC item number
.A55 2002
Literary form
non fiction
Nature of contents
bibliography
Series statement
Sage university papers. Quantitative applications in the social sciences
Series volume
no. 07-136
http://library.link/vocab/subjectName
  • Mathematical statistics
  • Missing observations (Statistics)
  • Statistique mathématique
  • Observations manquantes (Statistique)
  • Estadística matemática
  • Ontbrekende gegevens
  • Statistiek
  • Fehlende Daten
  • Statistik
Label
Missing data, Paul D. Allison
Instantiates
Publication
Note
"A SAGE university paper"--Cover
Bibliography note
Includes bibliographical references (pages 89-91) and index
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
1. Introduction -- 2. Assumptions ; Missing Completely at Random ; Missing at Random ; Ignorable ; Nonignorable -- 3. Conventional Methods ; Listwise ; Deletion; Pairwise Deletion ; Dummy Variable Adjustment ; Imputation -- 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 -- 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 -- 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 -- 7. Nonignorable Missing Data ; Two Classes of Models ; Heckman's Model for Sample Selection Bias ; ML Estimation With Pattern-Mixture Models ; Multiple Imputation With Pattern-Mixture Models
Control code
46364640
Dimensions
22 cm
Extent
vi, 93 pages
Isbn
9780761916727
Isbn Type
(pbk. : acid-free paper)
Lccn
2001001295
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
illustrations
System control number
(OCoLC)46364640
Label
Missing data, Paul D. Allison
Publication
Note
"A SAGE university paper"--Cover
Bibliography note
Includes bibliographical references (pages 89-91) and index
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
1. Introduction -- 2. Assumptions ; Missing Completely at Random ; Missing at Random ; Ignorable ; Nonignorable -- 3. Conventional Methods ; Listwise ; Deletion; Pairwise Deletion ; Dummy Variable Adjustment ; Imputation -- 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 -- 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 -- 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 -- 7. Nonignorable Missing Data ; Two Classes of Models ; Heckman's Model for Sample Selection Bias ; ML Estimation With Pattern-Mixture Models ; Multiple Imputation With Pattern-Mixture Models
Control code
46364640
Dimensions
22 cm
Extent
vi, 93 pages
Isbn
9780761916727
Isbn Type
(pbk. : acid-free paper)
Lccn
2001001295
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
illustrations
System control number
(OCoLC)46364640

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