Methods for the analysis of explanatory linear regression models with missing data not at random

Quality and Quantity

Volumn 37, Issue 4, 2003, Pages 363-376

  Navarro Pastor, José Blas ^a  

^a UNIVERSITAT AUTÒNOMA DE BARCELONA   (Spain)

Author keywords

Incomplete maximum likelihood estimation; Monte Carlo simulation; Multiple imputation; Nonrandom missing data; Regression analysis

Indexed keywords

EID: 3543050652     PISSN: 00335177     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1027323122628     Document Type: Article

References (37)

1. Arbuckle, J. L. (1997). Amos 3.6 for Windows. [computer program]. Chicago: SmallWaters Corporation.
2. Baker, S. G. & Laird, N. M. (1988). Regression analysis for categorical variables with outcome subject to nonignorable nonresponse. Journal of the American Statistical Association 83: 62-70.
3. Basilevsky, A., Sabourin, D., Hum D. & Anderson, A. (1985). Missing data estimators in the general linear model: an evaluation of simulated data as an experimental design. Communications in Statistics - Simulation and Computation 14: 371-394.
4. Bernaards, C. A. & Sijtsma, K. (2000). Influence of imputation and EM methods on factor analysis when item nonresponse in questionnaire data is nonignorable. Multivariate Behavioral Research 35: 321-364.
5. Delucchi, K. L. (1994). Methods for the analysis of binary outcome results in the presence of missing data. Journal of Consulting and Clinical Psychology 62: 569-575.
6. Ezpeleta, L., de la Osa, N., Doménech, J. M., Navarro, J. B., Losilla, J. M. & Judez, J. (1997a). Diagnostic agreement between clinician and the structured Diagnostic Interview for Children and Adolescents - -DICA-R - in an Outpatient Sample. Journal of Child Psychology and Psychiatry 38: 431-440.
7. Ezpeleta, L., de la Osa, N., Doménech, J. M., Navarro, J. B. & Losilla, J. M. (1997b). Test-retest reliability of the Spanish adaptation of the Diagnostic Interview of Children and Adolescents. Psicothema 9: 529-539.
8. Gold, M. S. & Bentler, P. M. (2000). Treatment of missing data: A Monte Carlo comparison of RBHDI, iterative stochastic regression imputation, and expectation-maximization. Structural Equation Modelling 7, 319-355.
9. Graham, J. W. & Donaldson, S. I. (1993). Evaluating interventions with differential attrition: The importance of nonresponse mechanism and use of follow-up data. Journal of Applied Psychology 78: 119-128.
10. Graham, J. W., Hofer, S. M., Donaldson, S. I., MacKinnon, D. P. & Schafer, J. L. (1997), Analysis with missing data in prevention research. In K. Bryant, M. Windle & S. West (eds). The Science of Prevention: Methodological Advances from Alcohol and Substance Abuse Research. Washington, DC: American Psychological Association.
11. Graham, J. W., Hofer, S. M. & Piccinin, A. M. (1994). Analysis with missing data in drug prevention research. In L. M. Collins & L. A. Seitz (eds). Advances in Data Analysis for Prevention Intervention Research. NIDA Research Monograph. Series (#142), Washington, DC: National Institute on Drug Abuse.
12. Graham, J. W., Hofer, S. M. & Mackinnon, D. P. (1996). Maximizing the usefulness of data obtained with planned missing value patterns: an application of maximum likelihood procedures. Multivariate Behavioral Research 31: 197-218.
13. Greenlees, J. S., Reece, W. S. & Zieschang, K. D. (1982). Imputation of missing values when the probability of response depends on the variable being imputed. Journal of the American Statistical Association 77: 251-261.
14. Huisman, M., Krol, B. & Van Sonderen, E. (1998). Handling missing data by re-approaching non-respondents. Quality & Quantity 32: 77-91.
15. Huisman, M. (2000). Imputation of missing item responses: some simple techniques. Quality & Quantity 34: 331-351.
16. King, G., Honaker, J., Joseph, A. & Scheve, K. (2001). Analyzing incomplete political science data: An alternative algorithm for multiple imputation. American Political Science Review 95: 49-69.
17. Kromrey, J. D. & Hines, C. V. (1994). Nonrandomly missing data in multiple regression: an empirical comparison of common missing-data treatments. Educational and Psychological Measurement 54: 573-593.
18. Landerman, L. R., Land, K. C. & Pieper, C. F. (1997). An empirical evaluation of the predictive mean matching method for imputing missing values. Sociological Methods and Research 26: 3-33.
19. Little, R. J. A. & Rubin, D. B. (1987). Statistical Analysis with Missing Data. New York: Wiley.
20. Little, R. J. A. & Rubin, D. B. (1989). The analysis of social science data with missing values. Sociological Methods and Research 18: 292-326.
21. Little, R. J. A. (1992). Regression with missing X's: A review. Journal of the American Statistical Association 87: 1227-1237.
22. Moinpour, C. M., Triplett, J. S., McKnight, B., Lovato, L.C., Upchurch, C., Leichman, C. G., Muggia, F. M., Tanaka, L., James, W. A., Lennard, M. & Meyskens, F. L. (2000). Challenges posed by non-random missing quality of life data in an advanced-stage colorectal cancer clinical trial. Psycho-Oncology 9: 340-354.
23. Navarro, J. B. & Losilla, J. M. (2000). Analysis of incomplete data with artificial neural networks: a simulation study. Psicothema 12: 503-510.
24. Norris, C. M., Ghali, W. A., Knudtson, M. L., Naylor, C. D. & Saunders, L. D. (2000). Dealing with missing data in observational health care outcome analyses. Journal of Clinical Epidemiology 53: 377-383.
25. de la Osa, N., Ezpeleta, L., Doménech, J. M., Navarro, J. B. & Losilla, J. M. (1997). Convergent and discriminant validity of the Structured Diagnostic Interview for Children and Adolescents (DICA-R). Psychology in Spain 1: 37-44.
26. Othuon, L. O. (1999). The accuracy of parameter estimates and coverage probability of population values in regression models upon different treatments of systematically missing data. Dissertation Abstracts International Section A 59: 4359.
27. Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. New York: Wiley.
28. Schafer, J. L. (1997). Analysis of Incomplete Multivariate Data. London: Chapman and Hall.
29. 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.
30. Simonoff, J. S. (1988). Regression diagnostics to detect non-random missingness in linear regression. Technometrics 30: 205-214.
31. SPSS Inc. (1999). Statistical Package for Social Sciences 10.0. [computer program]. Chicago: SPSS Inc.
32. Statistical Solutions Ltd. (1999). Solas 2.1 for Windows [computer program]. Cork, Ireland: Statistical Solutions Lts.
33. Taylor, M. A. & Amir, N. (1994). The problem of missing clinical data for research in psychopathology. Some solution guidelines. The Journal of Nervous and Mental Disease 182: 222-229.
34. The MathWorks Inc. (1998). Matlab 5.2 [computer program]. Natick, MA: The MathWorks Inc.
35. Von Eye, A. (1990). Statistical Methods in Longitudinal Research. San Diego: Academic Press.
36. Wilson WindowWare Inc. (1996). WinBatch 2.0 [computer program]. Seattle, WA: Wilson WindowWare Inc.
37. Wothke, W. (1998). Longitudinal and multi-group modelling with missing data. In T. D. Little, K. U. Schnabel & J. Baumert (eds). Modeling Longitudinal and Multiple Group Data: Practical Issues, Applied Approaches and Specific Examples. Mahwah, NJ: Lawrence Erlbaum Associates.