2002
DOI: 10.2307/3088424
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Modeling Multilevel Data Structures

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Cited by 1,157 publications
(751 citation statements)
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References 128 publications
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“…We use multilevel analysis since neglecting the hierarchical structure of the EES data could lead to an underestimation of standard errors and spurious inferences (Steenbergen and Jones 2002). A multilevel approach corrects for dependence of observations within countries (intra-class correlation) and makes adjustments to both within and between parameter estimates for the clustered nature of the data (Snijders and Bosker 1999).…”
Section: Resultsmentioning
confidence: 99%
“…We use multilevel analysis since neglecting the hierarchical structure of the EES data could lead to an underestimation of standard errors and spurious inferences (Steenbergen and Jones 2002). A multilevel approach corrects for dependence of observations within countries (intra-class correlation) and makes adjustments to both within and between parameter estimates for the clustered nature of the data (Snijders and Bosker 1999).…”
Section: Resultsmentioning
confidence: 99%
“…These authors do not take into consideration the multilevel structure of their data and their results are based in OLS linear regression estimations. 13 However, ignoring the contextual layer of the data leads to the violation of the assumption that observations are independent, and this produces lower estimated standard errors, thus increasing the probability of Type I errors (see Steenbergen & Jones, 2002). Indeed, the estimates of OLS in these types of data produce unbiased coefficients but smaller sample variance, which generates spurious significance tests.…”
Section: Methodsmentioning
confidence: 99%
“…We use fixed effects multi-level models accounting for the clustered nature of our dataset (Steenbergen & Jones, 2002). The models mirror the hierarchical nature of the dataset, with respondents being the highest level, then parties, and then selections.…”
Section: Methodsmentioning
confidence: 99%