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Missing Data (Quantitative Applications in the Social Sciences)
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N.º de artículo de eBay:406394751322
Características del artículo
- Estado
- copyright
- © 2001
- is_adult_product
- false
- binding
- paperback
- edition
- 1
- series_number
- 136
- MPN
- 9780761916727
- manufacturer
- SAGE Publications, Inc
- series_title
- Quantitative Applications in the Social Sciences
- pages
- 104
- ships_globally
- false
- genre
- Research methods: general
- publication_date
- 2001-10-03T00:00:01Z
- unspsc_code
- 55101500
- subject_code
- JMB
- gpsr_safety_attestation
- true
- target_audience
- Tertiary education
- edition_number
- 1
- batteries_required
- false
- volume
- 136
- Brand
- SAGE Publications Ltd
- number_of_items
- 1
- part_number
- 9780761916727
- batteries_included
- false
- ISBN
- 9780761916727
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Product Identifiers
Publisher
SAGE Publications, Incorporated
ISBN-10
0761916725
ISBN-13
9780761916727
eBay Product ID (ePID)
1911719
Product Key Features
Number of Pages
104 Pages
Publication Name
Missing Data
Language
English
Subject
Probability & Statistics / General, Research
Publication Year
2001
Type
Textbook
Subject Area
Mathematics, Social Science
Series
Quantitative Applications in the Social Sciences Ser.
Format
Trade Paperback
Dimensions
Item Height
0.3 in
Item Weight
5 Oz
Item Length
8.5 in
Item Width
5.5 in
Additional Product Features
Intended Audience
College Audience
LCCN
2001-001295
Dewey Edition
21
Series Volume Number
136
Illustrated
Yes
Dewey Decimal
001.4/22
Table Of Content
Series Editors Introduction1. Introduction2. Assumptions Missing Completely at Random Missing at Random Ignorable Nonignorable3. Conventional Methods Listwise Deletion Pairwise Deletion Dummy Variable Adjustment Imputation Summary4. 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 Conclusion5. 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 16. 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 MI Example 37. Nonignorable Missing Data Two Classes of Models Heckmans Model for Sample Selection Bias ML Estimation With Pattern-Mixture Models Multiple Imputation With Pattern-Mixture Models8. Summary and ConclusionNotesReferencesAbout the Author
Synopsis
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 relied 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., 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.
LC Classification Number
QA276.A55 2002
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