Missing Data

Analysis and Design

Authors:

ISBN: 978-1-4614-4017-8 (Print) 978-1-4614-4018-5 (Online)

Table of contents (13 chapters)

  1. Front Matter

    Pages i-xxiii

  2. Missing Data Theory

    1. Front Matter

      Pages 1-1

    2. No Access

      Book Chapter

      Pages 3-46

      Missing Data Theory

    3. No Access

      Book Chapter

      Pages 47-69

      Analysis of Missing Data

  3. Multiple Imputation and Basic Analysis

    1. Front Matter

      Pages 71-71

    2. No Access

      Book Chapter

      Pages 73-94

      Multiple Imputation with Norm 2.03

    3. No Access

      Book Chapter

      Pages 95-109

      Analysis with SPSS (Versions Without MI Module) Following Multiple Imputation with Norm 2.03

    4. No Access

      Book Chapter

      Pages 111-131

      Multiple Imputation and Analysis with SPSS 17-20

    5. No Access

      Book Chapter

      Pages 133-150

      Multiple Imputation and Analysis with Multilevel (Cluster) Data

    6. No Access

      Book Chapter

      Pages 151-190

      Multiple Imputation and Analysis with SAS

  4. Practical Issues in Missing Data Analysis

    1. Front Matter

      Pages 191-191

    2. No Access

      Book Chapter

      Pages 193-212

      Practical Issues Relating to Analysis with Missing Data: Avoiding and Troubleshooting Problems

    3. No Access

      Book Chapter

      Pages 213-228

      Dealing with the Problem of Having Too Many Variables in the Imputation Model

    4. No Access

      Book Chapter

      Pages 229-251

      Simulations with Missing Data

    5. No Access

      Book Chapter

      Pages 253-275

      Using Modern Missing Data Methods with Auxiliary Variables to Mitigate the Effects of Attrition on Statistical Power

  5. Planned Missing Data Design

    1. Front Matter

      Pages 277-277

    2. No Access

      Book Chapter

      Pages 279-294

      Planned Missing Data Designs I: The 3-Form Design

    3. No Access

      Book Chapter

      Pages 295-323

      Planned Missing Data Design 2: Two-Method Measurement