Statistical power analysis with missing data
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Statistical power analysis with missing data a structural equation modeling approach by Adam Davey

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Published by Routledge in New York .
Written in English

Subjects:

  • Social sciences -- Statistics,
  • Social sciences -- Statistical methods,
  • Social sciences -- Mathematical models

Book details:

Edition Notes

Includes bibliographical references and index.

StatementAdam Davey, Jyoti Savla.
ContributionsSavla, Jyoti.
Classifications
LC ClassificationsHA29 .D277 2010
The Physical Object
Paginationp. cm.
ID Numbers
Open LibraryOL24061257M
ISBN 109780805863697, 9780805863703
LC Control Number2009026347

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  Similar developments in the statistical analysis of incomplete (missing) data are gaining more widespread applications. This volume brings statistical power and incomplete data together under a common framework, in a way that is readily accessible to those with only an introductory familiarity with structural equation by: Similar developments in the statistical analysis of incomplete (missing) data are gaining more widespread applications. This volume brings statistical power and incomplete data together under a common framework, in a way that is readily accessible to those with only an introductory familiarity with structural equation modeling. "There is very little in the field about the effect of missing data on statistical power. This is an important area that needs to be addressed'e The writing style is 'e easy to read and engaging'e This book will 'e be used as a supplement in power analysis and SEM classes'e and by 'e individuals who are currently calculating power for research studies'e this book fills an important gap in the. Statistical Power Analysis with Missing Data: A Structural Equation Modeling Approach View larger image. By: Adam Davey and Jyoti "Tina" Savla. Sign Up Now! Already a Member? Log In You must be logged into Bookshare to access this title. Learn about membership options, or .

The objective of Missing Data: Analysis and Design is to enable investigators who are non-statisticians to implement modern missing data procedures properly in their research, and reap the benefits in terms of improved accuracy and statistical power. Missing Data: Analysis and Design contains essential information for both beginners and Brand: Springer-Verlag New York. Statistical power is the probability that a statistical test will correctly reject the null hypothesis. This chapter presents the concept of statistical power and several closely related Author: Joseph Stephen Rossi. Time series analysis and temporal autoregression Moving averages Trend Analysis ARMA and ARIMA (Box-Jenkins) models Spectral analysis 18 Resources Distribution tables Bibliography Statistical Software Test Datasets and data archives Websites File Size: 1MB. "The book has the potential to add to the research literature in terms of how to do statistical power analysis with missing data I would definitely buy this book because of the programs and instructions for power calculations for covariance structure models." - David P. MacKinnon, Arizona State University, USA. Table of Contents. 1.

Statistical analysis of data sets with missing values is a pervasive problem for which standard methods are of limited value. The first edition of Statistical Analysis with Missing Data has been a standard reference on missing-data methods. Statistical Analysis with Missing Data book. Read 3 reviews from the world's largest community for readers. * Emphasizes the latest trends in the field. /5. Statistical Power Analysis with Missing Data: A Structural Equation Modeling Approach View larger image. By: Adam Davey and Jyoti "Tina" Savla. Sign Up Now! Already a Member? Log In You must be logged into UK education collection to access this title. Power analysis is normally conducted before the data collection. The main purpose underlying power analysis is to help the researcher to determine the smallest sample size that is suitable to detect the effect of a given test at the desired level of significance.