Format

Send to

Choose Destination
J Sch Psychol. 2010 Feb;48(1):5-37. doi: 10.1016/j.jsp.2009.10.001.

An introduction to modern missing data analyses.

Author information

1
Arizona State University, USA. Amanda.Baraldi@asu.edu

Abstract

A great deal of recent methodological research has focused on two modern missing data analysis methods: maximum likelihood and multiple imputation. These approaches are advantageous to traditional techniques (e.g. deletion and mean imputation techniques) because they require less stringent assumptions and mitigate the pitfalls of traditional techniques. This article explains the theoretical underpinnings of missing data analyses, gives an overview of traditional missing data techniques, and provides accessible descriptions of maximum likelihood and multiple imputation. In particular, this article focuses on maximum likelihood estimation and presents two analysis examples from the Longitudinal Study of American Youth data. One of these examples includes a description of the use of auxiliary variables. Finally, the paper illustrates ways that researchers can use intentional, or planned, missing data to enhance their research designs.

PMID:
20006986
DOI:
10.1016/j.jsp.2009.10.001
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center