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Cited by 374

The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.

Two Multilevel Modeling Techniques for Analyzing Comparative Longitudinal Survey Datasets

Fairbrother, Malcolm·2014·Political Science Research and Methods

Abstract

Increasing numbers of comparative survey datasets span multiple waves. Moving beyond purely cross-sectional analyses, multilevel longitudinal analyses of such datasets should generate substantively important insights into the political, social and economic correlates of many individual-level outcomes of interest (attitudes, behaviors, etc.). This article describes two simple techniques for extracting such insights, which allow change over time in y to be a function of change over time in x and/or of a time-invariant x. The article presents results from simulation studies that assess the techniques in the presence of complications that are likely to arise with real-world data, and concludes with applications to the issues of generalized social trust and postmaterialist values, using data from World/European Values Surveys.

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Metadata

Authors
Fairbrother, M.
Venue
Political Science Research and Methods
Published
2013-10-14
Volume
2
Issue
1
Pages
119-140
ISSN
  • 2049-8470 (ISSN-L)
  • 2049-8489
OpenAlex
W1976692586
Cited by
374
References
65
FWCI
10.32
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Two Multilevel Modeling Techniques for Analyzing Comparative Longitudinal Survey Datasets. https://app.cochat.ai/papers/10.1017/psrm.2013.24
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Fairbrother, M. (2014). Two Multilevel Modeling Techniques for Analyzing Comparative Longitudinal Survey Datasets. Political Science Research and Methods. https://doi.org/10.1017/psrm.2013.24

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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.