Temple University, Philadelphia, Pennsylvania, USA Virginia Technical Institute, Blacksburg, USA

"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…The writing style is …easy to read and engaging…This book will … be used as a supplement in power analysis and SEM classes…and by … individuals who are currently calculating power for research studies…this book fills an important gap in the published literature." - Jay Maddock, University of Hawaii at Manoa, USA"This text fills an enormous hole in the literature, and is sorely needed…the clear writing, examples, and syntax for a variety of programs are major strengths…It will make a major and lasting contribution to the field…everything that I would want in a text for doctoral students is here." - Jim Deal, North Dakota State University, USA"… a valuable contribution to researchers conducting structural equation modeling research as well as to researchers in general in helping to inform on basic issues of missing data… reader friendly and accessible for all… The quality of scholarship is high. It is evident the authors understand the material." - Debbie Hahs-Vaughn, University of Central Florida, USA"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

• 1. Introduction. Part 1. Fundamentals. 2. The LISREL Model. 3. Missing Data: An Overview. 4. Estimating Statistical Power with Complete Data. Part 2. Applications. 5. Effects of Selection on Means, Variances, and Covariances. 6. Testing Covariances and Mean Differences with Missing Data .7. Testing Group Differences in Longitudinal Change. 8. Application to Manage Missingness Designs. 9. Using Montel Carlo Simulation Approaches to Study Power with Missing Data. Part 3. Extensions. 10. Additional Issues with Missing Data in Structural Equation Models. 11. Summary and Conclusions.

Temple University, Philadelphia, Pennsylvania, USA Virginia Technical Institute, Blacksburg, USA

"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…The writing style is …easy to read and engaging…This book will … be used as a supplement in power analysis and SEM classes…and by … individuals who are currently calculating power for research studies…this book fills an important gap in the published literature." - Jay Maddock, University of Hawaii at Manoa, USA"This text fills an enormous hole in the literature, and is sorely needed…the clear writing, examples, and syntax for a variety of programs are major strengths…It will make a major and lasting contribution to the field…everything that I would want in a text for doctoral students is here." - Jim Deal, North Dakota State University, USA"… a valuable contribution to researchers conducting structural equation modeling research as well as to researchers in general in helping to inform on basic issues of missing data… reader friendly and accessible for all… The quality of scholarship is high. It is evident the authors understand the material." - Debbie Hahs-Vaughn, University of Central Florida, USA"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

• 1. Introduction. Part 1. Fundamentals. 2. The LISREL Model. 3. Missing Data: An Overview. 4. Estimating Statistical Power with Complete Data. Part 2. Applications. 5. Effects of Selection on Means, Variances, and Covariances. 6. Testing Covariances and Mean Differences with Missing Data .7. Testing Group Differences in Longitudinal Change. 8. Application to Manage Missingness Designs. 9. Using Montel Carlo Simulation Approaches to Study Power with Missing Data. Part 3. Extensions. 10. Additional Issues with Missing Data in Structural Equation Models. 11. Summary and Conclusions.

Temple University, Philadelphia, Pennsylvania, USA Virginia Technical Institute, Blacksburg, USA

"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…The writing style is …easy to read and engaging…This book will … be used as a supplement in power analysis and SEM classes…and by … individuals who are currently calculating power for research studies…this book fills an important gap in the published literature." - Jay Maddock, University of Hawaii at Manoa, USA"This text fills an enormous hole in the literature, and is sorely needed…the clear writing, examples, and syntax for a variety of programs are major strengths…It will make a major and lasting contribution to the field…everything that I would want in a text for doctoral students is here." - Jim Deal, North Dakota State University, USA"… a valuable contribution to researchers conducting structural equation modeling research as well as to researchers in general in helping to inform on basic issues of missing data… reader friendly and accessible for all… The quality of scholarship is high. It is evident the authors understand the material." - Debbie Hahs-Vaughn, University of Central Florida, USA"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

• 1. Introduction. Part 1. Fundamentals. 2. The LISREL Model. 3. Missing Data: An Overview. 4. Estimating Statistical Power with Complete Data. Part 2. Applications. 5. Effects of Selection on Means, Variances, and Covariances. 6. Testing Covariances and Mean Differences with Missing Data .7. Testing Group Differences in Longitudinal Change. 8. Application to Manage Missingness Designs. 9. Using Montel Carlo Simulation Approaches to Study Power with Missing Data. Part 3. Extensions. 10. Additional Issues with Missing Data in Structural Equation Models. 11. Summary and Conclusions.