The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Randomization-Based Inference about Latent Variables from Complex Samples
Abstract
Standard procedures for drawing inferences from complex samples do not apply when the variable of interest θ cannot be observed directly, but must be inferred from the values of secondary random variables that depend on θ stochastically. Examples are proficiency variables in item response models and class memberships in latent class models. Rubin's “multiple imputation” techniques yield approximations of sample statistics that would have been obtained, had θ been observable, and associated variance estimates that account for uncertainty due to both the sampling of respondents and the latent nature of θ. The approach is illustrated with data from the National Assessment for Educational Progress.
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.
Integrity signals
Registry
Cites
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.
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
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
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
RUBIN, D. B. (1976) · Biometrika · doi:10.1093/biomet/63.3.581 · Cited by 9,925
The Calculation of Posterior Distributions by Data Augmentation
Tanner, M. A., & Wong, W. H. (1987) · Journal of the American Statistical Association · doi:10.1080/01621459.1987.10478458 · Cited by 3,928
Marginal Maximum Likelihood Estimation of Item Parameters: Application of an EM Algorithm
Bock, R. D., & Aitkin, M. (1981) · Psychometrika · doi:10.1007/bf02293801 · Cited by 2,339
Statistical Analysis with Missing Data.
Chatfield, C., Little, R. J. A., & Rubin, D. B. (1988) · Journal of the Royal Statistical Society. Series A (Statistics in Society) · doi:10.2307/2982783 · Cited by 2,202
Nonparametric maximum likelihood estimation of a mixing distribution
Laird, N. (1978) · Journal of the American Statistical Association · doi:10.1080/01621459.1978.10480103 · Cited by 753
BILOG 3 : item analysis and test scoring with binary logistic models
Mislevy, R. J., & Bock, R. D. (1990) · Medical Entomology and Zoology · Cited by 541
Self-citationFormalizing Subjective Notions about the Effect of Nonrespondents in Sample Surveys
Rubin, D. B. (1977) · Journal of the American Statistical Association · doi:10.1080/01621459.1977.10480610 · Cited by 380
Estimating Latent Distributions
Mislevy, R. J. (1984) · Psychometrika · doi:10.1007/bf02306026 · Cited by 338
Self-citationA General Model for the Analysis of Multilevel Data
Goldstein, H., & McDonald, R. P. (1988) · Psychometrika · doi:10.1007/bf02294400 · Cited by 240
A Strong True-Score Theory, with Applications
Lord, F. M. (1965) · Psychometrika · doi:10.1007/bf02289490 · Cited by 180
Estimation of Latent Group Effects
Mislevy, R. J. (1985) · Journal of the American Statistical Association · doi:10.1080/01621459.1985.10478215 · Cited by 133
Self-citationMULTILEVEL ASPECTS OF VARYING PARAMETERS IN STRUCTURAL MODELS
Muthén, B., & Satorra, A. (1989) · Elsevier eBooks · doi:10.1016/b978-0-12-108840-8.50009-3 · Cited by 129
Estimating the Parameters of the Latent Population Distribution
Andersen, E., & Madsen, M. (1977) · Psychometrika · doi:10.1007/bf02293656 · Cited by 112
The Logistic Model and Estimation of Latent Structure
Sanathanan, L., & Blumenthal, S. (1978) · Journal of the American Statistical Association · doi:10.1080/01621459.1978.10480101 · Cited by 63
Cited by
Multiple imputation after 18+ years
Rubin, D. B. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476908 · Cited by 4,072
Frick, J., Kaiser, F. G., & Wilson, M. (2004) · Personality and Individual Differences · doi:10.1016/j.paid.2004.02.015 · Cited by 965
International Large-Scale Assessment Data: Issues in Secondary Analysis and Reporting
Rutkowski, L., Gonzalez, E., Joncas, M., & von Davier, M. (2010) · Educational Researcher · doi:10.3102/0013189x10363170 · Cited by 507
OECD (2005) · PISA · doi:10.1787/9789264010543-en · Cited by 465
Estimating Population Characteristics From Sparse Matrix Samples of Item Responses
Mislevy, R. J., Beaton, A. E., Kaplan, B., & Sheehan, K. M. (1992) · Journal of Educational Measurement · doi:10.1111/j.1745-3984.1992.tb00371.x · Cited by 447
Self-citation
Metadata
- Authors
- Mislevy, R. J.
- Venue
- Psychometrika
- Publisher
- Cambridge University Press (CUP)
- Published
- 1991-06
- Volume
- 56
- Issue
- 2
- Pages
- 177-196
- ISSN
- 0033-3123 (ISSN-L)
- 1860-0980
- OpenAlex
- W2014370213
- Cited by
- 477
- References
- 17
- FWCI
- 7.12
- Type
- journal article
- Language
- en
- Open access
- closed
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
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.
