The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
General Multi-Level Modeling with Sampling Weights
Abstract
In this article we study the approximately unbiased multi-level pseudo maximum likelihood (MPML) estimation method for general multi-level modeling with sampling weights. We conduct a simulation study to determine the effect various factors have on the estimation method. The factors we included in this study are scaling method, size of clusters, invariance of selection, informativeness of selection, intraclass correlation, and variability of standardized weights. The scaling method is an indicator of how the weights are normalized on each level. The invariance of the selection is an indicator of whether or not the same selection mechanism is applied across clusters. The informativeness of the selection is an indicator of how biased the selection is. We summarize our findings and recommend a multi-stage procedure based on the MPML method that can be used in practical applications.
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
On the Variances of Asymptotically Normal Estimators from Complex Surveys
Binder, D. A. (1983) · International Statistical Review / Revue Internationale de Statistique · doi:10.2307/1402588 · Cited by 982
Cited by
The Math and Science Engagement Scales: Scale development, validation, and psychometric properties
Wang, M.-T., Fredricks, J. A., Ye, F., Hofkens, T. L., & Linn, J. S. (2016) · Learning and Instruction · doi:10.1016/j.learninstruc.2016.01.008 · Cited by 592
Campbell, O., Bann, D., & Patalay, P. (2020) · doi:10.1101/2020.06.12.20129312 · Cited by 484
Wang, M.-T., Fredricks, J., Ye, F., Hofkens, T., & Linn, J. S. (2019) · European Journal of Psychological Assessment · doi:10.1027/1015-5759/a000431 · Cited by 214
Willging, C. E., Green, A. E., & Ramos, M. M. (2016) · Implementation Science · doi:10.1186/s13012-016-0507-2 · Cited by 58
“Sampling weight and design information will be used to appropriately weight cases in analyses via multilevel pseudo maximum likelihood methods [77, 78] and Mplus [79].”
MethodAreepattamannil, S., Cairns, D., & Dickson, M. (2020) · Journal of Science Teacher Education · doi:10.1080/1046560x.2020.1753309 · Cited by 59
Metadata
- Authors
- Asparouhov, T.
- Venue
- Communications in Statistics - Theory and Methods
- Publisher
- Informa UK Limited
- Published
- 2006-04
- Volume
- 35
- Issue
- 3
- Pages
- 439-460
- ISSN
- 0361-0926 (ISSN-L)
- 1532-415X
- OpenAlex
- W1998449083
- Cited by
- 296
- References
- 1
- FWCI
- 4.27
- 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.
