The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Planned missing data designs in psychological research.
Abstract
The authors describe 2 efficiency (planned missing data) designs for measurement: the 3-form design and the 2-method measurement design. The 3-form design, a kind of matrix sampling, allows researchers to leverage limited resources to collect data for 33% more survey questions than can be answered by any 1 respondent. Power tables for estimating correlation effects illustrate the benefit of this design. The 2-method measurement design involves a relatively cheap, less valid measure of a construct and an expensive, more valid measure of the same construct. The cost effectiveness of this design stems from the fact that few cases have both measures, and many cases have just the cheap measure. With 3 brief simulations involving structural equation models, the authors show that compared with the same-cost complete cases design, a 2-method measurement design yields lower standard errors and a higher effective sample size for testing important study parameters. With a large cost differential between cheap and expensive measures and small effect sizes, the benefits of the design can be enormous. Strategies for using these 2 designs are suggested.
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Citations
Cites
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
Self-citationAnalyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation
King, G., Honaker, J., Joseph, A., & Scheve, K. (2001) · American Political Science Review · doi:10.1017/s0003055401000235 · Cited by 1,834
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×
“This procedure yields maximum likelihood param- eter estimates in the missing data case (P. D. Allison, 1987; Graham et al., 1994; B. Muthén et al., 1987).”
MethodStructural Factor Analysis Experiments with Incomplete Data
McArdle, J. J. (1994) · Multivariate Behavioral Research · doi:10.1207/s15327906mbr2904_5 · Cited by 198
Cited by
Missing data analysis: making it work in the real world
Graham, J. W. (2009) · Annual Review of Psychology · doi:10.1146/annurev.psych.58.110405.085530 · Cited by 6,163
Self-citationMissing Data: Five Practical Guidelines
Newman, D. A. (2014) · Organizational Research Methods · doi:10.1177/1094428114548590 · Cited by 1,411
Westfall, J., Kenny, D. A., & Judd, C. M. (2014) · Journal of Experimental Psychology: General · doi:10.1037/xge0000014 · Cited by 928
Collins, L. M., Dziak, J. J., & Li, R. (2009) · Psychological Methods · doi:10.1037/a0015826 · Cited by 431
Ullman, J. B., & Bentler, P. M. (2012) · Handbook of Psychology, Second Edition · doi:10.1002/9781118133880.hop202023 · Cited by 330
Metadata
- Authors
- Graham, J. W., Taylor, B. J., Olchowski, A. E., & Cumsille, P. E.
- Venue
- Psychological Methods
- Published
- 2006
- Volume
- 11
- Issue
- 4
- Pages
- 323-343
- ISSN
- 1082-989X (ISSN-L)
- 1939-1463
- OpenAlex
- W1989356938
- Cited by
- 573
- References
- 5
- FWCI
- 10.07
- Type
- journal article
- Language
- en
- Open access
- closed
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