Dealing With Missing Data in Developmental Research

Approaches to handling missing data have improved dramatically in recent years and researchers can now choose from a variety of sophisticated analysis options. The methodological literature favors maximum likelihood and multiple imputation because these approaches offer substantial improvements over older approaches, including a strong theoretical foundation, less restrictive assumptions, and the potential for bias reduction and greater power. These benefits are especially important for developmental research where attrition is a pervasive problem. This article provides a brief introduction to modern methods for handling missing data and their application to developmental research.

[1]  Roderick J. A. Little,et al.  Statistical Analysis with Missing Data: Little/Statistical Analysis with Missing Data , 2002 .

[2]  Craig K. Enders,et al.  Using the SPSS Mixed Procedure to Fit Cross-Sectional and Longitudinal Multilevel Models , 2005 .

[3]  J. Schafer,et al.  A comparison of inclusive and restrictive strategies in modern missing data procedures. , 2001, Psychological methods.

[4]  D. Rubin.  INFERENCE AND MISSING DATA , 1975 .

[5]  Craig K Enders,et al.  Missing not at random models for latent growth curve analyses. , 2011, Psychological methods.

[6]  Craig K. Enders,et al.  A Comparison of Item-Level and Scale-Level Multiple Imputation for Questionnaire Batteries , 2012 .

[7]  B. Muthén,et al.  Growth modeling with nonignorable dropout: alternative analyses of the STAR*D antidepressant trial. , 2011, Psychological methods.

[8]  Richard M Lerner,et al.  Use of missing data methods in longitudinal studies: the persistence of bad practices in developmental psychology. , 2009, Developmental psychology.

[9]  R. Lerner,et al.  Why Missing Data Matter in the Longitudinal Study of Adolescent Development: Using the 4-H Study to Understand the Uses of Different Missing Data Methods , 2010, Journal of youth and adolescence.

[10]  J. Schafer,et al.  Missing data: our view of the state of the art. , 2002, Psychological methods.

[11]  E. Foster,et al.  Alternative Methods for Handling Attrition , 2004, Evaluation review.

[12]  Craig K Enders,et al.  Analyzing longitudinal data with missing values. , 2011, Rehabilitation psychology.

[13]  Leland Wilkinson,et al.  Statistical Methods in Psychology Journals Guidelines and Explanations , 2005 .

[14]  A. Acock.  Working With Missing Values , 2005 .

[15]  D. Rubin,et al.  Fully conditional specification in multivariate imputation , 2006 .

[16]  Joseph L Schafer,et al.  On the performance of random‐coefficient pattern‐mixture models for non‐ignorable drop‐out , 2003, Statistics in medicine.

[17]  J. Graham,et al.  Missing data analysis: making it work in the real world. , 2009, Annual review of psychology.

[18]  D G Altman,et al.  Missing covariate data within cancer prognostic studies: a review of current reporting and proposed guidelines , 2004, British Journal of Cancer.

[19]  Donald Hedeker,et al.  Application of random-efiects pattern-mixture models for miss-ing data in longitudinal studies , 1997 .

[20]  Keith F. Widaman,et al.  III. MISSING DATA: WHAT TO DO WITH OR WITHOUT THEM , 2006 .

[21]  Judith D. Singer,et al.  Using SAS PROC MIXED to Fit Multilevel Models, Hierarchical Models, and Individual Growth Models , 1998 .

[22]  Gregory Camilli,et al.  Application of a Method of Estimating DIF for Polytomous Test Items , 1999 .

[23]  J. Graham.  Adding Missing-Data-Relevant Variables to FIML-Based Structural Equation Models , 2003 .