A Bayesian multilevel modeling approach to time-series cross-sectional data
Political Analysis, ISSN: 1047-1987, Vol: 15, Issue: 2, Page: 165-181
2007
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- 218Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
The analysis of time-series cross-sectional (TSCS) data has become increasingly popular in political science. Meanwhile, political scientists are also becoming more interested in the use of multilevel models (MLM). However, little work exists to understand the benefits of multilevel modeling when applied to TSCS data. We employ Monte Carlo simulations to benchmark the performance of a Bayesian multilevel model for TSCS data. We find that the MLM performs as well or better than other common estimators for such data. Most importantly, the MLM is more general and offers researchers additional advantages. © 2007 Oxford University Press.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=34047236599&origin=inward; http://dx.doi.org/10.1093/pan/mpm006; https://www.cambridge.org/core/product/identifier/S1047198700006446/type/journal_article; https://dx.doi.org/10.1093/pan/mpm006; https://www.cambridge.org/core/journals/political-analysis/article/abs/bayesian-multilevel-modeling-approach-to-timeseries-crosssectional-data/C8345F30FE7CBF04B71ECFC321869D1F; https://ssrn.com/abstract=1447746; https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1447746
Cambridge University Press (CUP)
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