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Cited by 283

The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.

Alternatives to Multilevel Modeling for the Analysis of Clustered Data

Huang, Francis L.·2016·The Journal of Experimental Education
DOI 10.1080/00220973.2014.952397
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Abstract

Multilevel modeling has grown in use over the years as a way to deal with the nonindependent nature of observations found in clustered data. However, other alternatives to multilevel modeling are available that can account for observations nested within clusters, including the use of Taylor series linearization for variance estimation, the design effect adjusted standard errors approach, and fixed effects modeling. Using 1,000 replications of 12 conditions with varied Level 1 and Level 2 sample sizes, the author compared parameter estimates, standard errors, and statistical significance using various alternative procedures. Results indicate that several acceptable procedures can be used in lieu of or together with multilevel modeling, depending on the type of research question asked and the number of clusters under investigation. Guidelines for applied researchers are discussed.

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Cites

Show all 56 references

Research works printed but not linked: 14

  1. 10.10983/pan/mpp004 · doi:10.10983/pan/mpp004
  2. Bell B., 2010, Dancing the sample size limbo with mixed models: How low can you go? Paper 197–2010 presented at the SAS Global Forum, Seattle, WA
  3. Bell R., 2002, Survey Methodology, 2, 169
  4. Chaplin D., 2003, Hierarchical linear models: Strengths and weaknesses. Paper presented at the annual meeting of the Association for Public Policy Analysis and Management
  5. 10.1177/014920639802400504 · doi:10.1177/014920639802400504 · Paper page
  6. Kish L., 1965, Survey sampling
  7. Kish L., 1974, Series B, 36, 1
  8. 10.1177/019394502400446414 · doi:10.1177/019394502400446414
  9. Meijer E., 1998, Assumptions, robustness, and estimation methods in multivariate modeling, 35
  10. Mundfrom D., 2002, Multiple Linear Regression Viewpoints, 28, 18
  11. Murnane R., 2011, Methods matter
  12. Newman D., 2010, Multiple Linear Regression Viewpoints, 36, 1
  13. Siller A.B., 2006, Paper presented at the 31st Annual SAS® Users Group International Conference, San Francisco, CA
  14. Snijders T., 2012, Multilevel analysis (2nd ed.)

Cited by

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Metadata

Authors
Huang, F. L.
Venue
The Journal of Experimental Education
Publisher
Informa UK Limited
Published
2014-10-17
Volume
84
Issue
1
Pages
175-196
ISSN
  • 0022-0973 (ISSN-L)
  • 1940-0683
DOI
10.1080/00220973.2014.952397
OpenAlex
W1982104375
Cited by
283
References
56
FWCI
11.00
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Alternatives to Multilevel Modeling for the Analysis of Clustered Data. https://app.cochat.ai/papers/10.1080/00220973.2014.952397
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Huang, F. L. (2016). Alternatives to Multilevel Modeling for the Analysis of Clustered Data. The Journal of Experimental Education. https://doi.org/10.1080/00220973.2014.952397

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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.