Regression diagnostics for survey data
AbstractDiagnostics for linear regression models are included as options in Stata and many other statistical packages and are now readily available to analysts. However, these tools are generally aimed at ordinary or weighted least-squares regression and do not account for stratification, clustering, and survey weights that are features of datasets collected in complex sample surveys. The ordinary least-squares diagnostics can mislead users because the variances of model parameter estimates will usually be estimated incorrectly by the standard procedures. The variance or standard-error estimates are an intimate part of many diagnostics. In this presentation, I summarize research that has been done to extend some of the existing diagnostics to complex survey data. Among the linear regression techniques I cover are leverages, DFBETAS, DFFITS, the forward search method for identifying influential points, and collinearity diagnostics, like variance inflation factors and variance decompositions.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Stata Users Group in its series DC09 Stata Conference with number 15.
Date of creation: 11 Aug 2009
Date of revision:
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-08-16 (All new papers)
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F Baum).
If references are entirely missing, you can add them using this form.