799
Views
29
CrossRef citations to date
0
Altmetric
Original Articles

Missing data techniques for multilevel data: implications of model misspecification

, &
Pages 1845-1865 | Received 05 Jan 2010, Accepted 30 Sep 2010, Published online: 30 Nov 2010

References

  • Allison , P. D. 2003 . Missing data techniques for structural equation modeling . J. Abnorm. Psychol. , 11 : 545 – 557 .
  • Allison , P. D. Multiple Imputation of Categorical Variables under the Multivariate Normal Model . Meeting of the American Sociological Association . Montreal, QC. available at http://www.allacademic.com/meta/p102543_index.html
  • Collins , L. M. , Schafer , J. L. and Kam , C. 2001 . A comparison of inclusive and restrictive strategies in modern missing data procedures . Psychol. Methods , 6 : 330 – 351 .
  • Dempster , A. P. , Laird , N. M. and Rubin , D. B. 1977 . Maximum likelihood from incomplete data via the EM algorithm . J. R. Statist. Soc. Ser. B Statist. Methodol. , 39 : 1 – 38 .
  • Enders , C. K. 2001 . A primer on maximum likelihood algorithms available for use with missing data . Struct. Equ. Model. , 8 : 128 – 141 .
  • Enders , C. K. 2006 . “ Analyzing structural equation models with missing data ” . In Structural Equation Modeling: A Second Course , Edited by: Hancock , G. R. and Mueller , R. O. 313 – 342 . Greenwich, CT : Information Age Publishing .
  • Goldstein , H. 2003 . Multilevel Statistical Models , 3 , London : Hodder Arnold .
  • Graham , J. W. 2003 . Adding missing-data-relevant variables to FIML-based structural equation models . Struct. Equ. Model , 10 : 80 – 100 .
  • Graham , J. W. , Olchowski , A. E. and Gilreath , T. D. 2007 . How many imputations are really needed? Some practical clarifications of multiple imputation theory . Prev. Sci , 8 : 206 – 213 .
  • Harel , O. 2007 . Inferences on missing information under multiple imputation and two-stage multiple imputation . Statist. Methodol. , 4 : 75 – 89 .
  • Harel , O. and Zhou , X. 2007 . Multiple imputation – review of theory, implementation and software . Stat. Med. , 26 : 3057 – 3077 .
  • Hershberger , S. L. and Fisher , D. G. 2003 . A note on determining the number of imputations for missing data . Struct. Equ. Model. , 10 : 648 – 650 .
  • Hox , J. 2002 . Multilevel Analysis: Techniques and Applications , Mahwah, NJ : Lawrence Erlbaum Associates .
  • Jacobusse , G. W. winMICE , software available at http://www.multiple-imputation.com
  • Little , R. J.A. and Rubin , D. B. 1989 . The analysis of social science data with missing values . Sociol. Methods Res. , 18 : 292 – 326 .
  • Little , R. J.A. and Rubin , D. B. 2002 . Statistical Analysis with Missing Data , 2 , Hoboken, NJ : John Wiley and Sons, Inc .
  • Muthén , L. K. and Muthén , B. O. Mplus User's Guide , 5 , 1998 – 2007 . Los Angeles, CA : Muthén & Muthén .
  • Peugh , J. L. and Enders , C. K. 2004 . Missing data in educational research: A review of reporting practices and suggestions for improvement . Rev. Educ. Res. , 74 : 525 – 556 .
  • R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2006; software available at http://www.R-project.org
  • Rasbash , J. , Charlton , C. , Browne , W. J. , Healy , M. and Cameron , B. MLwiN , software available at http://www.cmm.bristol.ac.uk/index.shtml
  • Raudenbush , S. W. and Bryk , A. S. 2002 . Hierarchical Linear Models: Applications and Data Analysis Methods , Thousand Oaks, CA : Sage Publications, Inc .
  • Roberts , J. K. and McLeod , P. 2008 . “ Software options for multilevel models ” . In Multilevel Modeling of Educational Data , Edited by: O'Connell , A. A. and Betsy McCoach , D. 427 – 467 . Charlotte, NC : Information Age Publishing, Inc .
  • Rubin , D. B. 1976 . Inference and missing data . Biometrika , 63 : 581 – 592 .
  • Rubin , D. B. 1987 . Multiple Imputation for Nonresponse in Surveys , New York : J. Wiley and Sons .
  • Rubin , D. B. 1996 . Multiple imputation after 18+ years . J. Am. Statist. Assoc , 91 : 473 – 489 .
  • Schafer , J. L. 1997 . Analysis of Incomplete Multivariate Data , New York : Chapman and Hall .
  • Schafer , J. L. 1997 . “ Imputation of missing covariates under a multivariate linear mixed model ” . University Park, PA : Department of Statistics, Pennsylvania State University . Tech. Rep. 97-04
  • Schafer , J. L. 2001 . “ Multiple imputation with PAN ” . In New Methods for the Analysis of Change , Edited by: Collins , L. M. and Sayer , A. G. 357 – 377 . Washington, DC : American Psychological Association .
  • Schafer , J. L. 2003 . Multiple imputation in multivariate problems when the imputation and analysis models differ . Stat. Neerl , 57 : 19 – 35 .
  • Schafer , J. L. “ NORM (Multiple imputation of incomplete multivariate data under a normal model, version 2) ” . software available at http://www.stat.psu.edu/~jls/misoftwa.html
  • Schafer , J. L. and Graham , J. W. 2002 . Missing data: Our view of the state of the art . Psychol. Methods , 7 : 147 – 177 .
  • Schafer , J. L. and Olsen , M. K. 1998 . Multiple imputation for multivariate missing-data problems: A data analyst's perspective . Multivariate Behav. Res. , 33 : 545 – 571 .
  • Schafer , J. L. and Yucel , R. M. 2002 . Computational strategies for multivariate linear mixed-effects models with missing values . J. Comput. Graph. Stat. , 11 : 437 – 457 .
  • Sinharay , S. , Stern , H. S. and Russell , D. 2001 . The use of multiple imputation for the analysis of missing data . Psychol. Methods , 6 : 317 – 329 .
  • Snijders , T. A.B. and Bosker , R. J. 1999 . Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling , London : SAGE Publications .
  • S-PLUS Version 8, Tibco Software, Inc., Palo Alto, CA, 2007; software available at http://www.tibco.com
  • Swaminathan , H. and Rogers , H. J. 2008 . “ Estimation procedures for hierarchical linear models ” . In Multilevel Modeling of Educational Data , Edited by: O'Connell , A. A. and Betsy McCoach , D. 469 – 519 . Charlotte, NC : Information Age Publishing, Inc .
  • Tanner , M. A. and Wong , W. H. 1987 . The calculation of posterior distributions by data augmentation . J. Am. Statist. Assoc. , 82 : 528 – 540 .
  • Tourangeau , K. , Nord , C. , Lê , T. , Pollack , J. M. and Atkins-Burnett , S. 2006 . Early Childhood Longitudinal Study, Kindergarten Class of 1998–99 (ECLS-K), Combined User's Manual for the ECLS-K Fifth-Grade Data Files and Electronic Codebooks , Washington, DC : National Center for Education Statistics . (NCES 2006–032)
  • Yucel , R. M. and Demirtas , H. 2010 . Impact of non-normal random effects on inference by multiple imputation. A simulation assessment . Comput. Stat. Data Anal. , 54 : 790 – 801 .

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.