The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Missing Data Techniques for Structural Equation Modeling.
Abstract
As with other statistical methods, missing data often create major problems for the estimation of structural equation models (SEMs). Conventional methods such as listwise or pairwise deletion generally do a poor job of using all the available information. However, structural equation modelers are fortunate that many programs for estimating SEMs now have maximum likelihood methods for handling missing data in an optimal fashion. In addition to maximum likelihood, this article also discusses multiple imputation. This method has statistical properties that are almost as good as those for maximum likelihood and can be applied to a much wider array of models and estimation methods.
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Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701
Missing data: our view of the state of the art
Schafer, J. L., & Graham, J. W. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.2.147 · Cited by 11,638
RUBIN, D. B. (1976) · Biometrika · doi:10.1093/biomet/63.3.581 · Cited by 9,925
Analysis of Incomplete Multivariate Data
Schafer, J. L. (1997) · doi:10.1201/9781439821862 · Cited by 6,012
A comparison of inclusive and restrictive strategies in modern missing data procedures.
Collins, L. M., Schafer, J. L., & Kam, C.-M. (2001) · Psychological Methods · doi:10.1037/1082-989x.6.4.330 · Cited by 2,389
On structural equation modeling with data that are not missing completely at random
Muthén, B., Kaplan, D., & Hollis, M. (1987) · Psychometrika · doi:10.1007/bf02294365 · Cited by 883 · cited 1×
“For example, if you do not have access to a program that does direct ML, you can still use MI to handle the missing data, or if you want to estimate your SEM using some method other than ML, such as two-stage least squares (Bollen, 1995), then MI would be a good choice.”
Method
Research works printed but not linked: 28
- 10.2307/2282773 · DOI
- Allison, 1987, Estimation of linear models with incomplete data., 71
- Allison. 2000 · doi:10.1093/acprof:oso/9780198298656.003.0001
- Allison, 2001, Missing data.
- Arbuckle, 1996, Full information estimation in the presence of incomplete data., 243
- Bollen. 1995 · DOI
- Cohen, 1983, Applied multiple regression and correlation analysis for the behavioral sciences
- Curran. 1997 · doi:10.1177/0266242697152007
- Duncan. A comparison of model- and multiple imputation-based approaches to longitudinal analysis with partial missingness. 5 1 1998 · DOI
- Gilks, 1996, Markov chain Monte Carlo in practice.
- 10.2307/2283101 · doi:10.2307/2283101
- Haitovsky, 1968, Missing data in regression analysis., Journal of the Royal Statistical Society: Series B, 30, 67, 10.1111/j.2517-6161.1968.tb01507.x · DOI · Paper page
- 10.2307/2291399 · DOI
- Kim, 1977, The treatment of missing data in multivariate analysis., Sociological Methods & Research, 6, 215, 10.1177/004912417700600206 · DOI · Paper page
- Li, 1991, Significance levels from repeated p-values and multiply imputed data., Statistica Sinica, 1, 65
- 10.2307/1391878 · DOI
- 10.2307/2290664 · doi:10.2307/2290664 · Paper page
- Little, 1987, Statistical analysis with missing data.
- Marini, 1979, Maximum likelihood estimation in panel studies with missing data., 314
- McLachlan, 1997, The EM algorithm and extensions.
- Raghunathan, 2001, A multivariate technique for multiply imputing missing values using a sequence of regression models., Survey Methodology, 27, 85
- 10.2307/2335739 · doi:10.2307/2335739 · Paper page
- Rubin. 1978 · doi:10.1111/j.1746-1561.1978.tb08084.x
- 10.1207/s15327906mbr3304_5 · DOI · Paper page
- Sinharay, 2001, The use of multiple imputation for the analysis of missing data., Psychological Methods, 6, 317, 10.1037/1082-989X.6.4.317 · DOI · Paper page
- 10.2307/2289457 · DOI
- Wothke, 2000, Longitudinal and multi-group modeling with missing data., 1
- Yuan, 2000, Three likelihood-based methods for mean and covariance structure analysis with nonnormal missing data., 165
Cited by
Baer, R. A., Smith, G. T., Lykins, E., Button, D., Krietemeyer, J., Sauer, S., Walsh, E., Duggan, D., & Williams, J. M. G. (2008) · Assessment · doi:10.1177/1073191107313003 · Cited by 2,930
Multiple imputation of discrete and continuous data by fully conditional specification
van Buuren, S. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280206074463 · Cited by 2,827
A Brief Guide to Structural Equation Modeling
Weston, R., & Gore, P. A. (2006) · The Counseling Psychologist · doi:10.1177/0011000006286345 · Cited by 2,718
Principled missing data methods for researchers
Dong, Y., & Peng, C.-Y. J. (2013) · SpringerPlus · doi:10.1186/2193-1801-2-222 · Cited by 2,208
Life-span development of self-esteem and its effects on important life outcomes.
Orth, U., Robins, R. W., & Widaman, K. F. (2012) · Journal of Personality and Social Psychology · doi:10.1037/a0025558 · Cited by 860
Metadata
- Authors
- Allison, P. D.
- Venue
- Journal of Abnormal Psychology
- Published
- 2003-11
- Volume
- 112
- Issue
- 4
- Pages
- 545-557
- ISSN
- 0021-843X (ISSN-L)
- 1939-1846
- OpenAlex
- W1989984421
- Cited by
- 1,333
- References
- 7
- FWCI
- 6.85
- Type
- journal article
- Language
- en
- Open access
- closed
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