Handling of missing data in psychological research: Problems and solutions;Umgang mit Fehlenden Werten in der Psychologischen Forschung Probleme und Lösungen

Psychologische Rundschau

Volumn 58, Issue 2, 2007, Pages 103-117

  Lüdtke, Oliver ^a   Robitzsch, Alexander ^b   Trautwein, Ulrich ^b   Köller, Olaf ^b  

^a MAX PLANCK INSTITUTE FOR HUMAN DEVELOPMENT   (Germany)

^b NONE

Author keywords

Missing data; Multiple imputation; Structurai equation modeling

Indexed keywords

EID: 34250020820     PISSN: 00333042     EISSN: None     Source Type: Journal    
DOI: 10.1026/0033-3042.58.2.103     Document Type: Article

References (59)

1. Allison, P. D. (2001). Missing data. Thousands Oaks, CA: Sage.
2. Arbuckle, J. L. (1996). Full information estimation in the presence of incomplete data. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling (pp. 243-277). Mahwah, NJ: Lawrence Erlbaum Publishers.
3. Baumert, J., Bos, W. & Lehmann, R. (Hrsg.). (2000). TIMSS/III. Dritte Internationale Mathematik- und Naturwissenschaftsstudie - Mathematische und naturwissenschaftliche Bildung am Ende der Schullaufbahn: Vol. 1. Mathematische und naturwissenschaftliche Grundbildung am Ende der Pflichtschulzeit. Opladen: Leske + Budrich.
4. Baumert, J., Klieme, E., Neubrand, J., Prenzel, M., Schiefele, U., Schneider, W., Stanat, P., Tillmann, K.-J. & Weiß, M. (Hrsg.). (2001). PISA 2000. Basiskompetenzen von Schülerinnen und Schülern im internationalen Vergleich. Opladen: Leske + Budrich.
5. Bück, S. F. (1960). A method of estimation of missing values in multivariate data suitable for use with an electronic Computer. Journal of the Royal Statistical Society, 22, 302-306.
6. Chen, J. & Shao, J. (2000). Nearest-neighbor imputation for survey date. Journal of Official Statistics, 16, 583-599.
7. Chen, J. & Shao, J. (2001). Jackknife variance estimation for nearest-neighbor imputation. Journal of the American Statistical Association, 95, 260-269.
8. Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale: Erlbaum.
9. Collins, L. M., Schafer, J. L. & Kam, C.-M. (2001). A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychological Methods, 6, 330-351.
10. De Leeuw, E. D. (1999). Prevention is the better cure: How to reduce missing data. Kwantitative Methoden, 62,39-55.
11. Demirtas, H. (2004). Modeling incomplete longitudinal data. Journal of Modern Applied Statistical Methods, 3, 305-321.
12. Dempster, A. P., Laird, N. M. & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 39,1-38.
13. Dietz, F., Schmid, S. & Fries, S. (2005). Lernen oder Freunde treffen? Lernmotivation unter den Bedingungen multipler Handlungsoptionen. Zeitschrift für Pädagogische Psychologie, 19,173-189.
14. Durrant, G. B. (2005). Imputation methods for handling itemnonresponse in the social sciences: A methodological review. National Centre for Research Methods Working Paper Series.
15. Enders, C. K. (2001a). A primer on maximum likelihood algorithms available for use with missing data. Structural Equation Modeling, 8, 128-141.
16. Enders, C. K. (2001b). The impact of nonnormality on full information maximum-likelihood estimation for structural equation models with missing data. Psychological Methods, 6, 352-370.
17. Fay, R. E. (1999). Theory and application of nearest neighbor imputation in Census 2000. Proceedings of the Section on Survey Research Methods, American Statistical Association, 112-121.
18. Finkbeiner, C. (1979). Estimation for the multiple factor model when data are missing. Psychometrika, 44, 409-420.
19. Gelman, A. & Carlin, J. B. (2002). Poststratification and weighting adjustments. In R. M. Groves, D. A. Dillman, J. L. Eltinge & R. J. A. Little (Eds.), Survey nonresponse (pp. 289-314). New York: Wiley.
20. Gelman, A., Carlin, J., Stern, H. & Rubin, D. B. (2003). Bayesian data analysis. London: Chapman & Hall.
21. Graham, J. W. (2003). Adding missing-data-relevant variables to FIML-based structural equation models. Structural Equation Modeling, 10, 80-100.
22. Graham, J. W., Cumsille, P. E. & Elek-Fisk, E. (2003). Methods for handling missing data. In J. A. Schinka & W. F. Velicer (Eds.), Handbook of psychology: Research methods in psychology (Vol. 2, pp. 87-114). New York: John Wiley & Sons.
23. Graham, J. W. & Schafer, J. L. (1999). On the performance of multiple imputation for multivariate data with small sample size. In R. Hoyle (Ed.), Statistical strategies for small sample research (pp. 1-29). Thousand Oaks, CA: Sage.
24. Graham, J. W., Taylor, B. J. & Cumsille, P. E. (2001). Planned missing data designs in the analysis of change. In L. M. Collins & A. G. Sayer (Eds.), New methods for the analysis of change (pp. 335-353). Washington, DC: American Psychological Association.
25. Horton, N. J. & Lipsitz, S. R. (2001). Multiple imputation in practice: Comparison of software packages for regression models with missing variables. The American Statistican, 55, 244-254.
26. Hox, J. J. (1999). A review of current software for handling missing data. Kwantitative Methoden, 62, 123-138.
27. King, G., Honacker, J., Joseph, A. & Scheve, K. (2001 ). Analyzing incomplete political science data: An alternative algorithm for multiple imputation. American Political Science Review, 95, 49-69.
28. Köller, O., Watermann, R., Trautwein, U. & Lüdtke, O. (2004). Wege zur Hochschulreife in Baden-Württemberg. TOSCA -Eine Untersuchung an allgemein bildenden und beruflichen Gymnasien. Opladen: Leske + Budrich.
29. Little, R. J. A. (1988). A test of missing completely at random for multivariate data with missing values. Journal of the American Statistical Association, 83, 1198-1202.
30. Little., R. J. A. (1993). Pattern-mixture models for multivariate incomplete data. Journal of the American Statistical Association, 87, 1227-1237.
31. Little, R. J. A. & Rubin, D. B. (2002). Statistical analysis with missing data. New York: Wiley.
32. Lövdén, M., Ghisletta, P. & Lindenberger, U. (2005). Social participation attenuates decline in perceptual speed in old and very old age. Psychology and Aging, 20, 423-434.
33. McDonald, R. P. & Ho, M.-H. R. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7, 64-82.
34. Mislevy, R. J., Beaton, A. E., Kaplan, B. & Sheehan, K. M. (1992). Estimating population characteristics from sparse matrix samples of item responses. Journal of Educational Measurement, 29, 133-161.
35. Muthén, B., Kaplan, D. & Hollis, M. (1987). On structural equation modeling with data that are not missing completely at random. Psychometrika, 42, 431-462.
36. Münnich, R. & Rässler, S. (2005). PRIMA: A new multiple imputation procedure for binary variables. Journal of Official Statistics, 21, 325-341.
37. Newman, D. A. (2003). Longitudinal modeling with randomly and systematically missing data: A simulation of ad hoc, maximum likelihood, and multiple imputation techniques. Organizational Research Methods, 6, 328-362.
38. Peugh, J. L. & Enders, C. K. (2004). Missing data in educational research: A review of reporting practices and suggestions for improvement. Review of Educational Research, 74, 525-556.
39. Rost, D. H. (2005). Interpretation und Bewertung pädagogisch- psychologischer Studien. Weinheim: Beltz.
40. Rubin, D. B. (1976). Inference and missing data. Biometrika, 63, 581-592.
41. Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. New York: Wiley.
42. Rubin, D. B. ( 1996). Multiple imputation after 18+ years. Journal of the American Statistical Association, 91, 473-489.
43. Rubin, D. B. (2003). Nested multiple imputation of NMES via partially incompatible MCMC. Statistica Neerlandica, 57, 3-18.
44. Schafer, J. L. (1997). Analysis of incomplete multivariate data. London: Chapman & Hall.
45. Schafer, J. L. (1999). NORM: Multiple Imputation of Incomplete Data under a Normal Model [Computer Software]. Retrieved from: http://methodology.psu.edu [4.10.2004].
46. Schafer, J. L. & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147-177.
47. Schafer, J. L. & Olsen, M. K. (1998). Multiple imputation for multivariate missing-data problems: A data analyst's perspective. Multivariate Behavioral Research, 33, 545-571.
48. Schafer, J. L. & Yucel, R. M. (2002). Computational strategies for multivariate linear mixed-effects models with missing values. Journal of Computational and Graphical Statistics, 11, 437-457.
49. Sijtsma, K. & van der Ark, L. (2003). Investigation and treatment of missing item scores in test and questionnaire data. Multivariate Behavioral Research, 38, 505-528.
50. Sinharay, S., Stern, H. S. & Russell, D. (2001). The use of multiple imputation for the analysis of missing data. Psychological Methods, 6, 317-329.
51. Spieß, M. (2004). Analyse von Längsschnittdaten mit fehlenden Werten. Grundlagen, Verfahren und Anwendungen. Habilitationsschrift: Bremen. http://nbn-resolving.de/urn:nbn:de: gbv:46-20050620037
52. Tanner, M. A. & Wong, W. H. (1987). The calculation of posterior distributions by data augmentation. Journal of the American Statistical Association, 82, 528-540.
53. Toutenburg, H., Heumann, C. & Nittner, T. (2004). Statistische Methoden bei unvollständigen Daten. Discussion Paper 380. Ludwig-Maximilians-Universität, München.
54. Trautwein, U., Lüdtke, O., Köller, 0. & Baumert, J. (2006). Self-esteem, academic self-concept, and achievement: How the learning environment moderates the dynamics of self-concept. Journal of Personality and Social Psychology, 90, 334-349.
55. Van Buuren, S. & Oudshoom, K. (1999). Flexible imputation by MICE. Report TNO-PG 99.054, TNO Prevention and Health, Leiden. Retrieved from: http://www.multiple. imputation.com [20.04.2006].
56. Verbeke, G. & Molenberghs, G. (2000). Linear mixed models for longitudinal data. New York: Springer.
57. von Hippel, P. T. (2004). Biases in SPSS 12.0 missing value analysis. American Statistician, 58, 160-164.
58. West, S. G. (2001). New approaches to missing data in psychological research: Introduction to the special section. Psychological Methods, 6, 315-316.
59. Wilkinson, L. & Task Force on Statistical Inference (1999). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist, 54, 594-604.