Complex Surveys: A Guide to Analysis Using R

By Thomas Lumley. 2010

839 citations recorded by the source corpus.

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Citations in the corpus

Girls get smart, boys get smug: Historical changes in gender differences in math, literacy, and academic social comparison and achievement (2017, methodology): “In all cases analysis was undertaken using the survey package in R (Lumley, 2011).”
A re‐evaluation of fixed effect(s) meta‐analysis (2018, background): “Non-uniform weighting of this sort is standard in the statistical survey literature (Lumley, 2010), where close analogues of the following results can be found.”
Using SPSS to Analyze Complex Survey Data: A Primer (2020, methodology): “A complex survey design is usually applied in cases where simple random sampling is not convenient, feasible, efficient, or costeffective (e.g., Lewis, 2017; Heeringa et al., 2017; Lumley, 2010).”
Robust factor analysis in the presence of normality violations, missing data, and outliers: Empirical questions and possible solutions (2014, background): “It is important to realize that MI will not remove bias completely, but will reduce bias to a greater extent than listwise deletion or mean imputation, simply because non-responders are likely to be different (Lumley, 2010).”
lavaan.survey: An R Package for Complex Survey Analysis of Structural Equation Models (2014, methodology): “In addition, by leveraging the many features of the survey package (Lumley 2004, 2010, 2012) it provides users with the above features currently omitted from commercially available SEM software.”
Tests for Regression Models Fitted to Survey Data (2014, methodology): “For the implementation of GLMs in the survey package and for the Cox model (Lumley & Scott 2013), we have used the saddlepoint approximation with 2 = k, and this has also worked very well in simulations, even in extreme cases with small numbers of large clusters. Details of some of the simulations c”