000 04044nam a22004455i 4500
001 978-1-4614-4018-5
003 DE-He213
005 20140220083249.0
007 cr nn 008mamaa
008 120607s2012 xxu| s |||| 0|eng d
020 _a9781461440185
_9978-1-4614-4018-5
024 7 _a10.1007/978-1-4614-4018-5
_2doi
050 4 _aQA276-280
072 7 _aJHBC
_2bicssc
072 7 _aSOC027000
_2bisacsh
082 0 4 _a519.5
_223
100 1 _aGraham, John W.
_eauthor.
245 1 0 _aMissing Data
_h[electronic resource] :
_bAnalysis and Design /
_cby John W. Graham.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2012.
300 _aXIX, 323 p. 27 illus., 19 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStatistics for Social and Behavioral Sciences
505 0 _aMissing Data Theory -- Multiple Imputation and Basic Analysis -- Practical Issues in Missing Data Analysis -- Planned Missing Data Design.
520 _aMissing data have long plagued those conducting applied research in the social, behavioral, and health sciences.  Good missing data analysis solutions are available, but practical information about implementation of these solutions has been lacking.  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 advanced readers.  For researchers with limited missing data analysis experience, this book offers an easy-to-read introduction to the theoretical underpinnings of analysis of missing data; provides clear, step-by-step instructions for performing state-of-the-art multiple imputation analyses; and offers practical advice, based on over 20 years' experience, for avoiding and troubleshooting problems.  For more advanced readers, unique discussions of attrition, non-Monte-Carlo techniques for simulations involving missing data, evaluation of the benefits of auxiliary variables, and highly cost-effective planned missing data designs are provided. The author lays out missing data theory in a plain English style that is accessible and precise.  Most analyses described in the book are conducted using the well-known statistical software packages SAS and SPSS, supplemented by Norm 2.03 and associated Java-based automation utilities.  A related web site contains free downloads of the supplementary software, as well as sample empirical data sets and a variety of practical exercises described in the book to enhance and reinforce the reader’s learning experience.  Missing Data: Analysis and Design and its web site work together to enable beginners to gain confidence in their ability to conduct missing data analysis, and more advanced readers to expand their skill set.  JOHN W. GRAHAM, PhD, is Professor of Biobehavioral Health at The Pennsylvania State University.  His research and publishing focus on the evaluation of health promotion and disease prevention interventions.  He specializes in evaluation research methods, including missing data analysis and design, structural equation modeling, and measurement.
650 0 _aStatistics.
650 1 4 _aStatistics.
650 2 4 _aStatistics for Social Science, Behavorial Science, Education, Public Policy, and Law.
650 2 4 _aStatistics, general.
650 2 4 _aStatistics for Life Sciences, Medicine, Health Sciences.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781461440178
830 0 _aStatistics for Social and Behavioral Sciences
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4614-4018-5
912 _aZDB-2-SMA
999 _c101472
_d101472