The DOI matches its Crossref record; 90 of the 90 references it cites were found in a registry or an index.
Multiple imputation as a flexible tool for missing data handling in clinical research
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Citations
Cites
Statistical Power Analysis for the Behavioral Sciences
Muller, K. (1989) · Technometrics · doi:10.1080/00401706.1989.10488618 · Cited by 99,167
Statistical power analysis for the behavioral sciences
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Multiple regression: testing and interpreting interactions
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Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701
Statistical Analysis With Missing Data
Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,265
Inference from iterative simulation using multiple sequences
Gelman, A., & Rubin, D. B. (1992) · Statistical Science · doi:10.1214/ss/1177011136 · Cited by 16,627
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
A test of missing completely at random for multivariate data with missing values
Little, R. J. A. (1988) · Journal of the American Statistical Association · doi:10.1080/01621459.1988.10478722 · Cited by 8,963
Cited by
Shanahan, L., Steinhoff, A., Bechtiger, L., Murray, A. L., Nivette, A., Hepp, U., Ribeaud, D., & Eisner, M. (2022) · Psychological Medicine · doi:10.1017/s003329172000241x · Cited by 675
Shanahan, L., Steinhoff, A., Bechtiger, L., Murray, A. L., Nivette, A. E., Hepp, U., Ribeaud, D., & Eisner, M. (2020) · Zurich Open Repository and Archive (University of Zurich) · doi:10.17863/cam.57251 · Cited by 499
Champion, L., Economides, M., & Chandler, C. (2018) · PLOS ONE · doi:10.1371/journal.pone.0209482 · Cited by 225
“In order to rule out the possibility that attrition could have influenced our estimates of Cohen’s d, we used multiple imputation (MI) to impute missing values and re-calculated Cohen’s d using the full imputed dataset [45].”
MethodPilot randomized trial of self-guided virtual reality exposure therapy for social anxiety disorder
Zainal, N. H., Chan, W. W., Saxena, A. P., Taylor, C. B., & Newman, M. G. (2021) · Behaviour Research and Therapy · doi:10.1016/j.brat.2021.103984 · Cited by 84
Factor structure and internal consistency of a Swedish version of the pain catastrophizing scale
Kemani, M. K., Grimby‐Ekman, A., Lundgren, J., Sullivan, M., & Lundberg, M. (2019) · Acta Anaesthesiologica Scandinavica · doi:10.1111/aas.13246 · Cited by 43
Metadata
- Authors
- Enders, C. K.
- Venue
- Behaviour Research and Therapy
- Publisher
- Elsevier BV
- Published
- 2017-11
- Volume
- 98
- Pages
- 4-18
- ISSN
- 0005-7967 (ISSN-L)
- OpenAlex
- W2556400449
- Cited by
- 355
- References
- 90
- FWCI
- 19.15
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Multiple imputation as a flexible tool for missing data handling in clinical research. https://app.cochat.ai/papers/10.1016/j.brat.2016.11.008
- The paper
- Enders, C. K. (2017). Multiple imputation as a flexible tool for missing data handling in clinical research. Behaviour Research and Therapy. https://doi.org/10.1016/j.brat.2016.11.008
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.
