Correcting for Selective Nonresponse in the National Longitudinal Survey of Youth Using Multiple Imputation
By Adam Davey, M. Shanahan, Joseph L. Schafer. 2001
89 citations recorded by the source corpus.
Subfields: Sociology and Political Science, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/e9c1882ef9a0f84124d9768ccc2365c8f93f7d26
How researchers see this paper
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In Sociology and Political Science
- Claude Fernet
- Stéphanie Austin
- F. Guay
- Marylène Gagné
- Jae Kwang Kim
- C. Sénécal
- Ian R. White
- S. Seaman
In Statistics and Probability
- Jae Kwang Kim
- Graham Kalton
- J. Michael Brick
- Wayne A. Fuller
- F. Guay
- Marylène Gagné
- Stéphanie Austin
- Claude Fernet
Citations in the corpus
- Predicting intraindividual changes in teacher burnout: The role of perceived school environment and motivational factors (2012, methodology): “Although these results suggest no selection bias, it is generally considered inappropriate to disregard missing values by using a listwise deletion of cases (Davey et al., 2001; Peugh & Enders, 2004).”
- On the bias of the multiple‐imputation variance estimator in survey sampling (2006, methodology): “Multiple imputation (MI) has been proposed as a method for estimating the precision of sample estimates in the presence of imputed values (see Rubin (1987, 1996)). MI is applied to a data set with missing items by repeating the process of assigning values for each of the missing values M times, crea”
- When does quality of relationships with coworkers predict burnout over time? The moderating role of work motivation (2010, background): “According to Davey, Shanahan, and Schafer (2001), ‘‘Failure to deal with missing data results in a loss of statistical power because partially complete cases typically are discarded from the analysis.”