Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

Click here to submit your manuscript to SPPS

Click here for more information on Research and Evaluation in Education and Psychology, 3e

Sign In to gain access to subscriptions and/or personal tools.
Educational and Psychological Measurement
This Article
Right arrow Full Text (OnlineFirst PDF)
Right arrow All Versions of this Article:
0013164409332225v1
69/6/929    most recent
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Request Reprints
Right arrow Add to My Marked Citations
Citing Articles
Right arrow Citing Articles via Google Scholar
Right arrow Citing Articles via Scopus
Google Scholar
Right arrow Articles by Yoo, J. E.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Complore   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati   Add to Twitter  
What's this?

Article

The Effect of Auxiliary Variables and Multiple Imputation on Parameter Estimation in Confirmatory Factor Analysis

Jin Eun Yoo*

Korea Institue for Curriculum and Evaluation

* To whom correspondence should be addressed. E-mail: jineun.yoo{at}gmail.com.


  Abstract
This Monte Carlo study investigates the beneficiary effect of including auxiliary variables during estimation of confirmatory factor analysis models with multiple imputation. Specifically, it examines the influence of sample size, missing rates, missingness mechanism combinations, missingness types (linear or convex), and the absence or presence of the auxiliary variables on convergence failure, bias, standard error, and confidence interval coverage of parameters. Including auxiliary variables in the imputation model is found to improve parameter estimation in most cases, particularly with the convex type of missingness and the nonignorable cases caused by MAR and absence of auxiliary variables in the imputation model. The results of this study can be applied to test validity studies where item selection is needed because of the presence of many alternative items (e.g., instrument development from an item bank). Implications and recommendations for proper imputation are discussed.

First published on March 4, 2009, doi:10.1177/0013164409332225

Educational and Psychological Measurement 2009;69:929.

A more recent version of this article appeared on December 1, 2009

Add to CiteULike CiteULike   Add to Complore Complore   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati   Add to Twitter Twitter    What's this?