The comparative efficacy of imputation methods for missing data in structural equation modeling

European Journal of Operational Research

Volumn 151, Issue 1, 2003, Pages 53-79

  Olinsky, Alan ^a   Chen, Shaw ^b   Harlow, Lisa ^c  

^a BRYANT UNIVERSITY   (United States)

^b UNIVERSITY OF RHODE ISLAND   (United States)

^c UNIVERSITY OF RHODE ISLAND   (United States)

Author keywords

Imputation; Missing data; Multivariate statistics; Regression; Structural equation modeling

Indexed keywords

DATA REDUCTION; ERROR ANALYSIS; MAXIMUM LIKELIHOOD ESTIMATION; PROBLEM SOLVING; REGRESSION ANALYSIS;

MULTIVARIATE STATISTICS;

OPERATIONS RESEARCH;

EID: 0041589601     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0377-2217(02)00578-7     Document Type: Article

References (63)

1. Acock, A., 1996. Working draft on working with missing data. Presented at the Theory Construction and Research Methodology Workshop, National Conference on Family Relations, Kansas City, MO.
2. Afifi A.A., Elashoff R.M. Missing observations in multivariate statistics. Part 1: Review of the literature. Journal of the American Statistical Association. 61:1966;595-604.
3. Allison P.D. Estimation of linear models with incomplete data. Clogg C.C. Sociological Methodology. 1987;71-103 Jossey-Bass, San Francisco.
4. Anderson T.W. An Introduction to Multivariate Analysis. 1958;Wiley, New York.
5. Arbuckle, J., 1996a. Amos users' guide: Version 3.6. SmallWaters Corp., Chicago.
6. Arbuckle J. Full information estimation in the presence of incomplete data. Marcoulides G.A., Schumacker R.E. Advanced Structural Equation Modeling: Issues and Techniques. 1996;LEA, Mahwah NJ.
7. Arbuckle, J.L., 1995. Amos for Windows: Analysis of Moment Structures, Version 3.5 [computer program]. SmallWaters Corp., Chicago.
8. Azen S.P., Van Guilder M., Hill M.A. Estimation of parameters and missing values under a regression model with non-normally distributed and non-randomly incomplete data. Statistics in Medicine. 8:1989;217-228.
9. Baker K., Harris P., O'Brien J. Data fusion: An appraisal and experimental evaluation. Journal of Market Research Society. 31:1989;153-212.
10. Bentler P. Fit indexes, Lagrange multipliers, constraint changes, and incomplete data in structural equation models. Multivariate Behavioral Research. 25:1990;163-172.
11. Bernaards C.A., Sijtsma K. Factor analysis of multimensional polytomous item response data suffering from ignorable item nonresponse. Multivariate Behavioral Research. 34:1999;275-313.
12. Board of Governors of the Federal Reserve System, 1996. Codebook for 1992 SCF, Washington, DC.
13. Bollen K.A. Structural Equations with Latent Variables. 1989;Wiley, New York.
14. Brown R.L. Efficacy of the indirect approach for estimating structural equation models with missing data: A comparison of five methods. Structural Equation Modeling. 1:1994;287-316.
15. Browne M.W. Generalized least squares estimators in the analysis of covariance structures. South African Statistical Journal. 8:1974;1-24.
16. Browne M.W. Covariance Structures. 1982;Cambridge University Press, Cambridge, MA.
17. Browne C.H. Asymptotic comparison of missing data procedures for estimating factor loadings. Psychometrika. 48:1983;269-291.
18. Chambers, R., 2000. Evaluation criteria for statistical editing and imputation. Report #28, National Statistics Methodology Series. Presented at UN/ECE meeting in Cardiff, UK, October 18-20, 2000.
19. Compustat, 1997. CD-ROM and mainframe database which offers extensive financial information on companies. Standard and Poors, New York, NY.
20. Dempster A., Laird N., Rubin D. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society B: Methodological. 39:1977;1-38.
21. DeSarbo W.S., Green P.E., Carroll J.D. Missing data in product-concept testing. Decision Sciences. 17:1986;163-185.
22. Donner A. The relative effectiveness of procedures commonly used in multiple regression analysis for dealing with missing values. The American Statistician. 36:1982;378-381.
23. Duncan, T., 1996. An introduction to the implementation of missing data models via structural equation methodology. Oregon Research Institute, 1899 Willamette St., Eugene OR 97401 (terryd@aragorn.ori.org).
24. Duncan T.E., Oman R., Ducan S.C. Modeling incomplete data in exercise behavior research using structural equation methodology. Journal of Sport and Exercise Psychology. 16:1994;187-205.
25. Ford B.L. An overview of hot-deck procedures. Madow W.G., OIkin I., Rubin D.B. Theory and Bibliographies. Incomplete Data in Sample Surveys. vol. II:1983;85-207 New York, Academic Press.
26. Graham J.W., Donaldson S.I. Evaluating interventions with differential attrition: The importance of nonresponse mechanisms and use of follow-up data. Journal of Applied Psychology. 78:1993;119-128.
27. Hair J., Anderson R., Tatham R., Black W. Multivariate Data Analysis: With Readings. fourth ed. 1995;Prentice Hall, New Jersey.
28. Haitovsky V. Missing data in regression analysis. Journal of the Royal Statistical Society B. 30:1968;67-82.
29. Hartley H.O., Hocking R.R. The analysis of incomplete data. Biometrics. 27:1971;783-823.
30. Hawkins M.R., Merriam V.H. An overmodeled world. Direct Marketing. (February):1991;21-24.
31. Heitjan D.F. Annotation: What can be done about missing data? Approaches to imputation. American Journal of Public Health. 87(4):1997;548-550.
32. Jöreskog K.G., Sörbom D. Lisrel 8 User's Reference Guide. 1993;Scientific Software International, Chicago.
33. Jöreskog K.G., Sörbom D. Prelis 2 User's Reference Guide. 1993;Scientific Software International, Chicago.
34. Kennickell, A.B., 1991. Imputation of the 1989 Survey of Consumer Finances: Stochastic relaxation and multiple imputation. Proceedings of the Section on Survey Research Methods, American Statistical Association, Atlanta, Georgia.
35. Kim J.O., Curry J. The treatment of missing data in multivariate analysis. Sociological Methods and Research. 6:1977;215-241.
36. Larsen, B.S., Madesn, B., 1999. Error identification and imputations with neural networks. Contributed working paper No. 26, UN/ECE Work Session on Statistical Data Editing, June 2-4, 1999, Rome, Italy.
37. Lepkowski J.M., Landis J.R., Stehouwer S.A. Strategies for the analysis of imputed data from a sample survey: The national medical care utilization and expenditure survey. Medical Care. 25:1987;705-716.
38. Little R.J.A. Missing data adjustments in large surveys. Journal of Business and Economic Statistics. 6:1988;296-297.
39. Little R., Rubin D. Statistical Analysis with Missing Data. 1987;Wiley, New York.
40. Little R., Rubin D. The analysis of social science data with missing values. Sociological Methods and Research. 18:1989;292-326.
41. Little R., Schenker N. Missing data. Arminger G., Clogg C.C., Sobel M.E. Handbook of Statistical Modeling for the Social and Behavioral Sciences. 1995;Plenum Press, New York.
42. Malhotra N.K. Analyzing marketing research data with incomplete information on the dependent variable. Journal of Marketing Research. 24:1987;74-84.
43. MathSoft, Inc., 1997. S-Plus 4.5 Professional [computer program], Cambridge, MA.
44. Montalto C.P., Sung J. Multiple imputation in the 1992 survey of consumer finances. Financial Counseling and Planning. 7:1996;133-141.
45. Muthen B., Kaplan D., Hollis M. On structural equation modeling with data that are not missing completely at random. Psychometrica. 52:1987;431-462.
46. Navidi W. A graphical illustration of the EM algorithm. The American Statistician. 51(1):1997;29-31.
47. Neale, M.C., 1994. Mx: Statistical modeling, second ed. Department of Psychiatry, Box 710 MCV, Richmond, VA 23298.
48. Nordbotten, S., 1998. New methods of editing and imputation. Agriculture Statistics 2000. An International Conference on Agriculture Statistics, Washington, DC, March 18-20, 1998.
49. Orchard, T., Woodbury, M.A., 1972. A missing information principle: Theory and applications. Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Applied Probability, vol. 1. University of California Press, Berkeley, CA.
50. Prosser, R., 1991. Multilevel analysis with incomplete data: A Monte Carlo comparison of four treatments. Doctoral dissertation, University of Toronto, unpublished.
51. Rizvi M.H. Hot-deck procedures: Introduction. Volume III. Madow W.G., Olkin I. Incomplete data in sample surveys. Proceedings of the Symposium. vol. II:1983;351-352 Academic Press, New York.
52. Roth P.L. Missing data: A conceptual review for applied psychologists. Personnel Psychology. 47:1994;537-560.
53. Rubin D.B. Inference and missing data. Biometrika. 61:1976;581-592.
54. Rubin D. Multiple Imputations for Nonresponse in Surveys. 1987;John Wiley and Sons, New York.
55. Santos, R., 1981. Multiple imputation for nonresponse in surveys (Survey Research Center Report). University of Michigan.
56. Schafer, J.L., 1997. NORM. A program for multiple imputation [computer program]. Pennsylvania State University.
57. SPSS, Inc., 1997. Statistical Package for the Social Sciences [computer program], Chicago.
58. Statistical Solutions, Inc., 1998. SOLAS for missing data analysis [computer program]. Cork, Ireland.
59. Timm N.H. The estimation of variance-covariance and correlation matrices from incomplete data. Psychometrika. 35:1970;417-437.
60. Verleye G., Pepermans R., Despontin M. Missing at random data problems and maximum likelihood structural equation modeling. Kwantitatieve Methoden. 62:1999;95-110.
61. Walker A., Acock A., Bowman S., Li F. Amount of care given and caregiving satisfaction: A latent growth curve analysis. Journal of Gerontology: Psychological Sciences. 518:1996;130-142.
62. Wilks S.S. Moments and distributions of estimates of population parameters from fragmentary samples. Annals of Mathematical Statistics. 3:1932;163-195.
63. Wilmot, C.G., Shivananjappa, S., 2001. Comparison of hot-deck and neural-network imputation. Invited paper, International Conference on Transport Survey Quality and Innovation, August 2001, Kruger National Park, South Africa.