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Missing data : analysis and design / John W. Graham.

By: Material type: TextTextSeries: Statistics for social and behavioral sciencesPublisher: New York, NY : Springer, [2012]Copyright date: ©2012Description: xxiii, 323 pages : illustrations (some colour) ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 1461440173
  • 9781461440178
Subject(s): DDC classification:
  • 519.5 23
LOC classification:
  • QA276 .G7124 2012
Contents:
Part 1. Missing Data Theory -- Missing Data Theory -- Analysis of Missing Data -- Part 2. Multiple Imputation and Basic Analysis -- Multiple Imputation with Norm 2.03 -- Analysis with SPSS (Versions Without MI Module) Following Multiple Imputation with Norm 2.03 -- Multiple Imputation and Analysis with SPSS 17-20 -- Multiple Imputation and Analysis with Multilevel (Cluster) Data -- Multiple Imputation and Analysis with SAS -- Part 3. Practical Issues in Missing Data Analysis -- Practical Issues Relating to Analysis with Missing Data: Avoiding and Troubleshooting Problems -- Dealing with the Problem of Having Too Many Variables in the Imputation Model -- Simulations with Missing Data -- Using Modern Missing Data Methods with Auxiliary Variables to Mitigate the Effects of Attrition on Statistical Power -- Part 4. Planned Missing Data Design -- Planned Missing Data Designs I: The 3-Form Design -- Planned Missing Data Design 2: Two-Method Measurement.
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Holdings
Item type Current library Call number Copy number Status Date due Barcode
Book City Campus City Campus Main Collection 519.5 GRA (Browse shelf(Opens below)) 1 Available A519363B

Includes bibliographical references.

Part 1. Missing Data Theory -- Missing Data Theory -- Analysis of Missing Data -- Part 2. Multiple Imputation and Basic Analysis -- Multiple Imputation with Norm 2.03 -- Analysis with SPSS (Versions Without MI Module) Following Multiple Imputation with Norm 2.03 -- Multiple Imputation and Analysis with SPSS 17-20 -- Multiple Imputation and Analysis with Multilevel (Cluster) Data -- Multiple Imputation and Analysis with SAS -- Part 3. Practical Issues in Missing Data Analysis -- Practical Issues Relating to Analysis with Missing Data: Avoiding and Troubleshooting Problems -- Dealing with the Problem of Having Too Many Variables in the Imputation Model -- Simulations with Missing Data -- Using Modern Missing Data Methods with Auxiliary Variables to Mitigate the Effects of Attrition on Statistical Power -- Part 4. Planned Missing Data Design -- Planned Missing Data Designs I: The 3-Form Design -- Planned Missing Data Design 2: Two-Method Measurement.

Machine converted from AACR2 source record.

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