JMASM 32: Multiple Imputation of Missing Multilevel, Longitudinal Data: A Case When Practical Considerations Trump Best Practices?

Article Sidebar

Published May 1, 2013
DOI : https://doi.org/10.56801/10.56801/v12.i.660

Main Article Content

Jennifer E. V. Lloyd
University of British Columbia
Jelena Obradović
Stanford University
Richard M. Carpiano
University of British Columbia
Frosso Motti-Stefanidi
University of Athens, Greece

Abstract

A pedagogical tool is presented for applied researchers dealing with incomplete multilevel, longitudinal data. It explains why such data pose special challenges regarding missingness. Syntax created to perform a multiply-imputed growth modeling procedure in Stata Version 11 (StataCorp, 2009) is also described.

Article Details

Issue
Vol. 12 (2013): Issue 1
Section
Articles