Articles and Chapters Relating to Missing Data Analysis Procedures
This is NOT intended to be an exhaustive list (updated July 4, 2007)

Collins, L. M., Schafer, J. L., & Kam, C. M. (2001).  A comparison of 
  inclusive and restrictive strategies in modern missing data procedures.  
  Psychological Methods, 6, 330-351.

Graham, J. W. (2003).  Adding missing-data relevant variables to FIML-based 
  structural equation models.  Structural Equation Modeling, 10, 80-100.

Graham, J. W., Cumsille, P. E., & Elek-Fisk, E. (2002).  
  Methods for handling missing data.  In J. A. Schinka & W. F. 
  Velicer (Eds.).  Research Methods in Psychology (pp. 87-114).  
  Volume 2 of Handbook of Psychology (I. B. Weiner, Editor-in-Chief).  
  New York:  John Wiley & Sons.

     Latest thinking about missing data analysis.  Very practical orientation.
     Contains step-by-step instructions for doing multiple imputation
     with Schafer's NORM program.

Graham, J. W., & Donaldson, S. I. (1993).  Evaluating
  interventions with differential attrition:  The importance of
  nonresponse mechanisms and use of followup data.  Journal
  of Applied Psychology, 78, 119-128.

     This article examines the attrition issue.  Describes and
     illustrates the the EM algorithm.

Graham, J. W., & Hofer, S. M. (2000).  Multiple imputation in 
  multivariate research. In T. D. Little, K. U. Schnabel, & 
  J. Baumert, (Eds.), Modeling longitudinal and multiple-group 
  data:  Practical issues, applied approaches, and specific examples. 
  (pp. 201-218). Hillsdale, NJ:  Erlbaum.

     This chapter is a very user-friendly description of the use of
     Joe Schafer's NORM program, with an illustrative empirical
     example.

Graham, J. W., Hofer, S.M., Donaldson, S.I., MacKinnon,
  D.P., & Schafer, J.L. (1997).  Analysis with missing data in
  prevention research.  In K. Bryant, M. Windle, & S. West
  (Eds.), The science of prevention:  methodological advances
  from alcohol and substance abuse research. (pp. 325-366). 
  Washington, D.C.:  American Psychological Association.

     In the context of an empirical example, this chapter
     discusses, and illustrates the pros and cons of four
     acceptable, and readily available methods:  (a) raw data
     maximum likelihood with Amos; (b) multiple imputation with
     NORM; (c) multiple imputation with EMCOV; and (d) EM
     algorithm (with EMCOV) and bootstrap.  We show how the
     following "old" methods fall very short of desiriable
     treatment of missing data (listwise deletion, pairwise
     deletion, mean substitution).

Graham, J. W., Hofer, S. M., & MacKinnon, D. P.  (1996). 
  Maximizing the usefulness of data obtained with planned
  missing value patterns:  An application of maximum
  likelihood procedures.  Multivariate Behavioral Research, 31,
  197-218.

     In this article, we describe one aspect of what we call
     "planned missingness" with the "3-form design".  We
     illustrate the use of several acceptable missing data
     methods:  (a) raw-data maximum likelihood with Mx; (b) EM
     algorithm (EMCOV); (c) multiple group SEM.  Again, we
     show that older methods do not fare well.

Graham, J. W., Hofer, S. M., & Piccinin, A. M. (1994). 
  Analysis with missing data in drug prevention research.  In
  L. M. Collins and L. Seitz (eds.), Advances in data analysis
  for prevention intervention research.  National Institute on
  Drug Abuse Research Monograph Series (#142),
  Washington DC:  National Institute on Drug Abuse.

     In this chapter, we discuss at a very basic level several
     kinds of missing data.  Using the multiple group SEM
     approach, and the EM algorithm (with EMCOV), we analyze
     several different empirical datasets.   This chapter contains
     much valuable information about analysis with missing data. 
     However, what we say about multiple imputation is
     incorrect.

Graham, J. W., Olchowski, A. E., & Gilreath, T. D. (2007).  
  How Many Imputations are Really Needed? Some Practical 
  Clarifications of Multiple Imputation Theory.  Prevention Science.

Graham, J. W., Roberts, M. M., Tatterson, J. W., & Johnston, 
  S. E. (2002).  Data quality in evaluation of an alcohol-related 
  harm prevention program.  Evaluation Review, 26, 147-189.

     Gives an example of performing data quality analyses in the 
     missing data case.

Graham, J. W., & Schafer, J. L. (1999).  On the performance of 
  multiple imputation for multivariate data with small sample size.  
  In R. Hoyle (Ed.)  Statistical Strategies for Small Sample Research,  
  (pp. 1-29). Thousand Oaks, CA:  Sage.

     This chapter describes a simulation based on a population
     containing real empirical data.  The simulation illustrates the
     value of Joe Schafer's NORM program for analyzing data
     with very small samples (N=100, and N=50).  Results
     show that NORM performs very well even with these small
     samples.

Graham, J. W., Taylor, B. J., & Cumsille, P. E. (2001).  Planned 
  missing data designs in analysis of change.  In L. Collins & 
  A. Sayer (Eds.), New methods for the analysis of change, (pp. 335-353). 
  Washington, DC:  American Psychological Association.

Graham, J. W., Taylor, B. J., Olchowski, A. E., & Cumsille, P. E. (2006).  
  Planned missing data designs in psychological research.  
  Psychological Methods, 11, 323-343.

Hawkins, J. D., Graham, J. W., Maguin, E., Abbott, R., Hill,
  K.G., & Catalano, R. F.  (1997).  Exploring the effects of
  age of alcohol use initiation and psychosocial risk factors on
  subsequent alcohol misuse.  Journal of Studies on Alcohol,
  58, 280-290.

     This is a largely substantive article, but it very nicely
     illustrates the use of the Amos program.

Little, R.J.A., & Rubin, D.B. (1987).  Statistical analysis
  with missing data.  New York:  Wiley.

     This is an excellent book describing several methods (many
     variations of the EM algorithm) for dealing with analysis
     with missing data.  Some parts are tough reading.

Rubin, D.B. (1987).  Multiple imputation for nonresponse in
  surveys.  New York:  Wiley.

     This is the first word, and maybe the last on multiple
     imputation.  Tough reading.

Schafer, J.L. (1997).  Analysis of Incomplete Multivariate
  Data.  New York:  Chapman and Hall.

     This book is the basis for Joe's series of multiple imputation
     programs.  It is somewhat more readable than Rubin (1987)
     and Little & Rubin (1987).


Schafer, J. L., & Graham, J. W. (2002).  Missing data:  our 
  view of the state of the art. Psychological Methods, 7, 147-177.



Schafer, J. L., & Olsen, M. K. (1998).  Multiple imputation for 
  multivariate missing-data problems:  a data analyst's perspective.
  Multivariate Behavioral Research, 33, 545-571.

     Excellent for the serious user of NORM.