Statistical Power Analysis with Missing Data
A Structural Equation Modeling Approach
Routledge – 2010 – 384 pages
Statistical power analysis has revolutionized the ways in which we conduct and evaluate research. 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. It answers many practical questions such as:
Points of Reflection encourage readers to stop and test their understanding of the material. Try Me sections test one’s ability to apply the material. Troubleshooting Tips help to prevent commonly encountered problems. Exercises reinforce content and Additional Readings provide sources for delving more deeply into selected topics. Numerous examples demonstrate the book’s application to a variety of disciplines. Each issue is accompanied by its potential strengths and shortcomings and examples using a variety of software packages (SAS, SPSS, Stata, LISREL, AMOS, and MPlus). Syntax is provided using a single software program to promote continuity but in each case, parallel syntax using the other packages is presented in appendixes. Routines, data sets, syntax files, and links to student versions of software packages are found at www.psypress.com/davey. The worked examples in Part 2 also provide results from a wider set of estimated models. These tables, and accompanying syntax, can be used to estimate statistical power or required sample size for similar problems under a wide range of conditions.
Class-tested at Temple, Virginia Tech, and Miami University of Ohio, this brief text is an ideal supplement for graduate courses in applied statistics, statistics II, intermediate or advanced statistics, experimental design, structural equation modeling, power analysis, and research methods taught in departments of psychology, human development, education, sociology, nursing, social work, gerontology and other social and health sciences. The book’s applied approach will also appeal to researchers in these areas. Sections covering Fundamentals, Applications, and Extensions are designed to take readers from first steps to mastery.
"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.