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Applied Missing Data Analysis
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Applied Missing Data Analysis (Methodology in the Social Sciences)

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by Craig K. Enders , Enders

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      New: BRAND NEW HARDBACK 9.685 by 6.85 inches. (382 pages) walking readers step by step through complex concepts, this book translates missing data techniques into something that applied researchers and graduate students can understand and utilize in their own research. enders explains the rationale and procedural details for maximum likelihood estimation, bayesian estimation, multiple imputation, and models for handling missing not at random (mnar) data. easy-to-follow examples and small simulated data sets illustrate the techniques and clarify the underlying principles. the companion web-sight, web. appliedmissingdata[full stop]com, includes data files and syntax for the examples in the book as well as up-to-date information on software. the book is accessible to substantive researchers while providing a level of detail that will satisfy quantitative specialists. this book will appeal to researchers and graduate students in psychology, education, management, family studies, public health, sociology, and political science. it will also serve as a supplemental text for doctoral-level courses or seminars in advanced quantitative methods, survey analysis, longitudinal data analysis, and multilevel modeling, and as a primary text for doctoral-level courses or seminars in missing data. part 1. an introduction to missing data. 1.1 introduction. 1.2 chapter overview. 1.3 missing data patterns. 1.4 a conceptual overview of missing data heory. 1.5 a more formal description of missing data theory. 1.6 why is the missing data mechanism important? 1.7 how plausible is the missing at random mechanism? 1.8 an inclusive analysis strategy. 1.9 testing the missing completely at random mechanism. 1.10 planned missing data designs. 1.11 the three-form design. 1.12 planned missing data for longitudinal designs. 1.13 conducting power analyses for planned missing data designs. 1.14 data analysis example. 1.15 summary. 1.16 recommended readings. part 2. traditional methods for dealing with missing data. 2.1 chapter overview. 2.2 an overview of deletion methods. 2.3 listwise deletion. 2.4 pairwise deletion. 2.5 an overview of single imputation techniques. 2.6 arithmetic mean imputation. 2.7 regression imputation. 2.8 stochastic regression imputation. 2.9 hot-deck imputation. 2.10 similar response pattern imputation. 2.11 averaging the available items. 2.12 last observation carried forward. 2.13 an illustrative simulation study. 2.14 summary. 2.15 recommended readings. part 3. an introduction to maximum likelihood estimation. 3.1 chapter overview. 3.2 the univariate normal distribution. 3.3 the sample likelihood. 3.4 the log-likelihood. 3.5 estimating unknown parameters. 3.6 the role of first derivatives. 3.7 estimating standard errors. 3.8 maximum likelihood estimation with multivariate normal data. 3.9 a bivariate analysis example. 3.10 iterative optimization algorithms. 3.11 significance testing using the wald statistic. 3.12 the likelihood ratio test statistic. 3.13 should i use the wald test or the likelihood ratio statistic? 3.14 data analysis example 1. 3.15 data analysis example 2. 3.16 summary. 3.17 recommended readings. part 4. maximum likelihood missing data handling. 4.1 chapter overview. 4.2 the missing data log-likelihood. 4.3 how do the incomplete data records improve estimation? 4.4 an illustrative computer simulation study. 4.5 estimating standard errors with missing data. 4.6 observed versus expected information. 4.7 a bivariate analysis example. 4.8 an illustrative computer simulation study. 4.9 an overview of the em algorithm. 4.10 a detailed description of the em algorithm. 4.11 a bivariate analysis example. 4.12 extending em to multivariate data. 4.13 maximum likelihood software options. 4.14 data analysis example 1. 4.15 data analysis example 2. 4.16 data analysis example 3. 4.17 data analysis example 4. 4.18 data analysis example 5. 4.19 summary. 4.20 recommended readings. part 5. improving the accuracy of maximum likelihood analyses. 5.1...
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      About the Book

      Walking readers step by step through complex concepts, this book translates missing data techniques into something that applied researchers and graduate students can understand and utilize in their own research. Enders explains the rationale and procedural details for maximum likelihood estimation, Bayesian estimation, multiple imputation, and models for handling missing not at random (MNAR) data. Easy-to-follow examples and small simulated data sets illustrate the techniques and clarify the underlying principles. The companion website (www.appliedmissingdata.com) includes data files and syntax for the examples in the book as well as up-to-date information on software. The book is accessible to substantive researchers while providing a level of detail that will satisfy quantitative specialists.

      Product Details

      • Media: Hardcover Book, 377 pages
      • Language: English
      • Publisher: Guilford Publications
      • Edition: Unknown
      • ISBN-10: 1606236393
      • Publication Date: May. 17th, 2010
      • ISBN-13: 9781606236390
      • Dimensions: 7.00 x 10.00 x 1.00 inches
      • Shipping Weight: 1.90 lbs
      • Categories: Psychology Statistics Education Statistics Nursing Research Theory

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