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Below results based on the criteria 'diagnostics'
Total number of records returned: 3
Representative Bureaucracy and Harder Questions: A Response to Meier, Wrinkle, and Polinard
Nielsen, Laura B.
Wolf, Patrick J.
In a recently published article, Meier, Wrinkle, and Polinard (1999) reach the tantalizing conclusion that increases in the representation of minority teachers in the public school bureaucracy actually enhance the academic achievement of both minority and Anglo groups of students. However, diagnostic and statistical tests on their data suggest that their analysis may suffer from specification, selection, and categorization limitations. When corrections for these problems are introduced into the analysis, the results that are the basis for the Meier, Wrinkle and Polinard conclusions change significantly, thereby undermining our confidence in the validity of
Not Asked and Not Answered: Multiple Imputation for Multiple Surveys
We present a method of analyzing a series of independent cross-sectional surveys in which some questions are not answered in some surveys and some respondents do not answer some of the questions posed. The method is also applicable to a single survey in which different questions are asked, or different sampling methods used, in different strata or clusters. Our method involves multiply-imputing the missing items and questions by adding to existing methods of imputation designed for single surveys a hierarchical regression model that allows covariates at the individual and survey levels. Information from survey weights is exploited by including in the analysis the variables on which the weights were based, and then reweighting individual responses (observed and imputed) to estimate population quantities. We also develop diagnostics for checking the fit of the imputation model based on comparing imputed to non-imputed data. We illustrate with the example that motivated this project --- a study of pre-election public opinion polls, in which not all the questions of interest are asked in all the surveys, so that it is infeasible to impute each survey separately.
Diagnostics for multivariate imputation
We consider three sorts of diagnostics for random imputations: (a) displays of the completed data, intended to reveal unusual patterns that might suggest problems with the imputations, (b) comparisons of the distributions of observed and imputed data values, and (c) checks of the fit of observed data to the model used to create the imputations. We formulate these methods in terms of sequential regression multivariate imputation [Van Buuren and Oudshoom 2000, and Raghunathan, Van Hoewyk, and Solenberger 2001], an iterative procedure in which the missing values of each variable are randomly imputed conditional on all the other variables in the completed data matrix. We also consider a recalibration procedure for sequential regression imputations. We apply these methods to the 2002 Environmental Sustainability Index (ESI), a linear aggregation of 68 environmental variables on 142 countries, with 22% missing values.