A Nonparametric, Multiple Imputation-Based Method for the Retrospective Integration of Data Sets.

Journal Article

Complex research questions often cannot be addressed adequately with a single data set. One sensible alternative to the high cost and effort associated with the creation of large new data sets is to combine existing data sets containing variables related to the constructs of interest. The goal of the present research was to develop a flexible, broadly applicable approach to the integration of disparate data sets that is based on nonparametric multiple imputation and the collection of data from a convenient, de novo calibration sample. We demonstrate proof of concept for the approach by integrating three existing data sets containing items related to the extent of problematic alcohol use and associations with deviant peers. We discuss both necessary conditions for the approach to work well and potential strengths and weaknesses of the method compared to other data set integration approaches.

Full Text

Duke Authors

Cited Authors

  • Carrig, MM; Manrique-Vallier, D; Ranby, KW; Reiter, JP; Hoyle, RH

Published Date

  • January 2015

Published In

Volume / Issue

  • 50 / 4

Start / End Page

  • 383 - 397

PubMed ID

  • 26257437

Electronic International Standard Serial Number (EISSN)

  • 1532-7906

International Standard Serial Number (ISSN)

  • 0027-3171

Digital Object Identifier (DOI)

  • 10.1080/00273171.2015.1022641

Language

  • eng