Missing Data

• Asparouhov, T. & Muthén, B. (2025). Multiple imputation with Mplus. Version 4, Jan 6, 2025.
  
  
• Muthén, B, Muthén, L. & Asparouhov, T. (2016). Regression and Mediation Analysis Using Mplus. Chapter 10.
  
  
• Muthén, B. (2015). General and specific factors in selection modeling. In M. Rosén et al. (eds.), Cognitive Abilities and Educational Outcomes, Methodology of Educational Measurement and Assessment (pp. 223-236). Switzerland, Springer International Publishing. DOI: 10.1007/978-3-319-43473-5_12
  
  
• Muthén, B., Asparouhov, T., Hunter, A. & Leuchter, A. (2011). Growth modeling with non-ignorable dropout: Alternative analyses of the STAR*D antidepressant trial. Psychological Methods, 16, 17-33. (Download scripts)
  
  
• Enders, C.K. (2011). Missing not at random models for latent growth curve analyses. Forthcoming in Psychological Methods, 16, 1-16.
  
  
• Asparouhov, T. & Muthén, B. (2010). Chi-square statistics with multiple imputation. Version 2, July 27,2010.
  
  
• Enders, C.K. (2010). Applied missing data analysis. New York: Guilford Press.
  
  
• Asparouhov, T. & Muthén, B. (2010). Weighted lease squares estimation with missing data. August 14, 2010.
  
  
• Asparouhov, T. & Muthén, B. (2008). Auxiliary variables predicting missing data. May 5, 2008.
  
  
• Muthén, B., Jo, B. & Brown, H. (2003). Comment on the Barnard, Frangakis, Hill & Rubin article, Principal stratification approach to broken randomized experiments: A case study of school choice vouchers in New York City. Journal of the American Statistical Association, 98, 311-314. (Barnard et al. article). For background information and analyses using Mplus, see Mplus Web Note #5 and Jo (2002), Sensitivity of causal effects under ignorable and latent ignorable missing-data mechanisms, Draft.
  
  
• Muthén, B. & Asparouhov, T. (2002). Using Mplus monte carlo simulations in practice: A note on non-normal missing data in latent variable models. Mplus Web notes: No. 2. Version 2, March 22, 2002.
  
  
• Muthén, B., Kaplan, D. & Hollis, M. (1987). On structural equation modeling with data that are not missing completely at random. Psychometrika, 52, 3, 431-462.
  
  

Related Short Courses

• Topic 4: Advanced growth modeling, missing data analysis, and survival analysis

• Topic 9: Bayesian analysis using Mplus (May 2011 version)

• Handouts and other topics can be found on the Short Courses page.

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