Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 268

The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.

Principled Missing Data Treatments

Open access
Lang, Kyle M., Little, Todd D.·2018·Prevention Science
DOI 10.1007/s11121-016-0644-5
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/s11121-016-0644-5

Cites

17 of 17 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 17 references

Research works printed but not linked: 30

  1. Anderson, T. W. (1957). Maximum likelihood estimates for a multivariate normal distribution when some observations are missing. Journal of the American Statistical Association, 52, 200–203. doi: 10.1080/01621459.1957.10501379 . · DOI · Paper page
  2. Andridge, R. R., & Little, R. J. A. (2010). A review of hot deck imputation for survey non-response. International Statistical Review, 78, 40–64. doi: 10.1111/j.1751-5823.2010.00103.x . · DOI · Paper page
  3. 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 Associates, Inc.
  4. Bodner, T. E. (2006). Missing data: prevalence and reporting practices. Psychological Reports, 99, 675–680. doi: 10.2466/PR0.99.7.675-680 . · doi:10.2466/pr0.99.7.675-680
  5. Diggle, P., & Kenward, M. G. (1994). Informative dropout in longitudinal data analysis (with discussion). Applied Statistics, 43, 49–94. · DOI · Paper page
  6. Enders, C. K. (2001). The performance of the full information maximum likelihood estimator in multiple regression models with missing data. Educational and Psychological Measurement, 61, 713–740. doi: 10.1177/00131640121971482 . · DOI · Paper page
  7. Enders, C. K. (2010). Applied missing data analysis. New York: Guilford.
  8. Enders, C. K., & Bandalos, D. L. (2001). The relative performance of full information maximum likelihood estimation for missing data in structural equation models. Structural Equation Modeling, 8, 430–457. doi: 10.1207/S15328007SEM0803_5 . · DOI · Paper page
  9. Goldstein, H., Carpenter, J., Kenward, M. G., & Levin, K. A. (2009). Multilevel models with multivariate mixed response types. Statistical Modelling., 9, 173–197. doi: 10.1177/1471082X0800900301 . · DOI · Paper page
  10. Goldstein, H., Carpenter, J., & Browne, W. J. (2014). Fitting multilevel multivariate models with missing data in responses and covariates that may include interactions and non-linear terms. Journal of the Royal Statistical Society: Series A (Statistics in Society), 177, 553–564. doi: 10.1111/rssa.12022 . · DOI
  11. Graham, J. W. (2003). Adding missing-data-relevant variables to FIML-based structural equation models. Structural Equation Modeling, 10, 80–100. doi: 10.1207/S15328007SEM1001_4 . · DOI · Paper page
  12. Graham, J. (2012). Missing data: analysis and design. New York: Springer. · DOI · Paper page
  13. Heckman, J. (1976). The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models. The Annals of Economic and Social Measurement, 5, 475–492.
  14. Howard, W., Rhemtulla, M., & Little, T. D. (2015). Using principal components as auxiliary variables in missing data estimation. Multivariate Behavioral Research, 50, 285–299. doi: 10.1080/00273171.2014.999267 . · DOI
  15. Little, R. J. A. (1993). Pattern-mixture models for multivariate incomplete data. Journal of the American Statistical Association, 88, 125–134. doi: 10.2307/2290705 . · DOI
  16. Little, R. J. A., & Yau, L. (1996). Intent-to-treat analysis for longitudinal studies with drop-outs. Biometrics, 52, 1324–1333. doi: 10.2307/2532847 . · DOI · Paper page
  17. Little, T. D., Jorgensen, T. D., Lang, K. M., & Moore, E. W. G. (2014). On the joys of missing data. Journal of Pediatric Psychology, 39, 151–162. doi: 10.1093/jpepsy/jst048 . · DOI · Paper page
  18. Little, T. D., Lang, K. M., Wu, W., & Rhemtulla, M. (2016). Missing data. In D. Cicchetti (Ed.), Developmental Psychopathology: Vol. 1. Theory and method (3rd ed., pp. 760–796). New York: Wiley. · DOI
  19. Liu, M., Taylor, J. M. G., & Belin, T. R. (2000). Multiple imputation and posterior simulation for multivariate missing data in longitudinal studies. Biometrics, 56, 1157–1163. doi: 10.1111/j.0006-341X.2000.01157.x . · DOI
  20. 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. doi: 10.3102/00346543074004525 . · DOI · Paper page
  21. Raghunathan, T. E., Lepkowski, J. M., Van Hoewyk, J., & Solenberger, P. (2001). A multivariate technique for multiply imputing missing values using a sequence of regression models. Survey Methodology, 27, 85–96.
  22. Rubin, D. B. (1978). Multiple imputations in sample surveys—a phenomenological Bayesian approach to nonresponse (Proceedings of the Survey Research Methods Section of the American Statistical Association, pp. 30–34).
  23. Savalei, V., & Rhemtulla, M. (2012). On obtaining estimates of the fraction of missing information from full information maximum likelihood. Structural Equation Modeling, 19, 477–494. doi: 10.1080/10705511.2012.687669 . · DOI
  24. 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. doi: 10.1198/106186002760180608 . · DOI · Paper page
  25. van Buuren, S. (2011). Multiple imputation of multilevel data. In J. Hox & J. Roberts (Eds.), Handbook of advanced multilevel analysis (pp. 173–196). Milton Park, UK: Routledge.
  26. von Hippel, P. T. (2009). How to impute interactions, squares, and other transformed variables. Sociological Methodology, 39, 265–291. doi: 10.1111/j.1467-9531.2009.01215.x . · DOI · Paper page
  27. Wu, W., Jia, F., & Enders, C. K. (2015). A comparison of imputation strategies for ordinal missing data on Likert scale variables. Multivariate Behavioral Research, 50, 484–503. doi: 10.1080/00273171.2015.1022644 . · DOI
  28. Yucel, R. M. (2008). Multiple imputation inference for multivariate multilevel continuous data with ignorable non-response. Philosophical Transactions of the Royal Society A, 366, 2389–2403. doi: 10.1098/rsta.2008.0038 . · DOI
  29. Zhao, J. H., & Schafer, J. L. (2013). pan: multiple imputation for multivariate panel or clustered data (Version 0.9) [R Package].
  30. Zhao, E., & Yucel, R. M. (2009). Performance of sequential imputation method in multilevel applications. In the Proceedings of the American Statistical Association Survey Research Methods Section (pp. 2800–2810).

Cited by

See all 268

Metadata

Authors
Lang, K. M., & Little, T. D.
Venue
Prevention Science
Publisher
Springer Science and Business Media LLC
Published
2016-04-04
Volume
19
Issue
3
Pages
284-294
ISSN
  • 1389-4986 (ISSN-L)
  • 1573-6695
DOI
10.1007/s11121-016-0644-5
OpenAlex
W2319889034
Cited by
268
References
17
FWCI
14.28
Type
journal article
Language
en
Open access
green
Licence
www.springer.com/tdm (tdm)
Funders
NSF (NSF 1053160)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Principled Missing Data Treatments. https://app.cochat.ai/papers/10.1007/s11121-016-0644-5
The paper
Lang, K. M., & Little, T. D. (2018). Principled Missing Data Treatments. Prevention Science. https://doi.org/10.1007/s11121-016-0644-5

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.