The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Modeling Multilevel Data Structures
Abstract
data are becoming quite common in political science and provide numerous opportunities for theory testing and development. Unfortunately this type of data typically generates a number of statistical problems, of which clustering is particularly impor? tant. To exploit the opportunities of? fered by multilevel data, and to solve the statistical problems inherent in them, special statistical techniques are required. In this article, we focus on a technique that has become popular in educational statistics and sociology?multilevel analysis. In multilevel analysis, researchers build models that capture the layered structure of multilevel data, and determine how layers interact and impact a dependent variable of interest. Our objective in this article is to introduce the logic and statistical theory behind multilevel models, to illustrate how such models can be applied fruitfully in political science, and to call atten? tion to some of the pitfalls in multilevel analysis.
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
Hsiao, C. (2014) · doi:10.1017/cbo9781139839327 · Cited by 7,869
Designing Social Inquiry: Scientific Inference in Qualitative Research
King, G., Keohane, R. O., & Verba, S. (1994) · Princeton University Press · doi:10.1515/9781400821211 · Cited by 7,225
What To Do (and Not to Do) with Time-Series Cross-Section Data
Beck, N., & Katz, J. N. (1995) · American Political Science Review · doi:10.2307/2082979 · Cited by 6,840
Concept Misformation in Comparative Politics
Sartori, G. (1970) · American Political Science Review · doi:10.2307/1958356 · Cited by 3,318
Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems
Harville, D. A. (1977) · Journal of the American Statistical Association · doi:10.1080/01621459.1977.10480998 · Cited by 2,667
The Comparative Method: Moving Beyond Qualitative and Quantitative Strategies
Ragin, C. C. (2014) · doi:10.1525/9780520957350 · Cited by 2,130
Simultaneous Factor Analysis in Several Populations
Jöreskog, K. G. (1971) · Psychometrika · doi:10.1007/bf02291366 · Cited by 2,051
Bayes Estimates for the Linear Model
Lindley, D. V., & Smith, A. F. M. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00885.x · Cited by 2,025
Parametric empirical Bayes inference: theory and applications
Morris, C. N. (1983) · Journal of the American Statistical Association · doi:10.1080/01621459.1983.10477920 · Cited by 1,448 · cited 1×
“The first, is to simply adjust the GLS and EB estimators by incorporating estimates of the sampling variance of the (co)variance components (Morris 1983; Kackar and Harvill e 1984), possibly via bootstrapping (Laird and Louis 1987).”
MethodEstimation in Parallel Randomized Experiments
Rubin, D. B. (1981) · Journal of Educational Statistics · doi:10.3102/10769986006004377 · Cited by 269
Cited by
Context and Causal Mechanisms in Political Analysis
Falleti, T. G., & Lynch, J. F. (2009) · Comparative Political Studies · doi:10.1177/0010414009331724 · Cited by 909
Calculation, Community and Cues: Public Opinion on European Integration
Hooghe, L., & Marks, G. (2005) · European Union Politics · doi:10.1177/1465116505057816 · Cited by 873
The Individual–Institutional Nexus of Protest Behaviour
Dalton, R., Van Sickle, A., & Weldon, S. (2010) · British Journal of Political Science · doi:10.1017/s000712340999038x · Cited by 513
Van Hauwaert, S. M., & Van Kessel, S. (2018) · European Journal of Political Research · doi:10.1111/1475-6765.12216 · Cited by 513
Global Encyclopedia of Public Administration, Public Policy, and Governance
(2018) · Springer eBooks · doi:10.1007/978-3-319-20928-9 · Cited by 499
Metadata
- Authors
- Steenbergen, M. R., & Jones, B. S.
- Venue
- American Journal of Political Science
- Publisher
- JSTOR
- Published
- 2002-01
- Volume
- 46
- Issue
- 1
- Pages
- 218
- ISSN
- 0092-5853 (ISSN-L)
- OpenAlex
- W1968327343
- Cited by
- 1,503
- References
- 11
- FWCI
- 23.03
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Modeling Multilevel Data Structures. https://app.cochat.ai/papers/10.2307/3088424
- The paper
- Steenbergen, M. R., & Jones, B. S. (2002). Modeling Multilevel Data Structures. American Journal of Political Science. https://doi.org/10.2307/3088424
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.