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:
- How missing data affects the statistical power in a study
- How much power is likely with different amounts and types of
missing data
- How to increase the power of a design in the presence of
missing data, and
- How to identify the most powerful design in the presence of
missing data.
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.
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