**Module Descritiption** This module summarizes the main approaches to identifying and handling missing data. Pre-requisites: this module assumes that you have a basic understanding of statistics **Learning Objectives** 1. Understand the types of missing data (what are they and why they matter) 2. Understand common methods of dealing with missing data (pros & cons) 3. Understand the applications of missing data (e.g. planned missingness, recognizing when missing data might be a problem in published research) **Readings** 1. Enders, C. K., & Ebooks Corporation. (2010). [Applied missing data analysis][1]. New York: Guilford Press. (Chapter 1 for general background on the challenges of missing data, and missing data mechanisms). Little, T. D., Jorgensen, T. D., Lang, K. M., & Moore, E. W. G. (2013). [On the joys of missing data][2]. Journal of pediatric psychology, 39(2), 151-162. 2. Enders, C. K., & Ebooks Corporation. (2010). [Applied missing data analysis][3]. New York: Guilford Press. (Chapter 2 for an overview of methods of missing data handling). 3. Little, T. D., & Rhemtulla, M. (2013). [Planned missing data designs for developmental researchers][4]. Child Development Perspectives, 7(4), 199-204. Zhou, H., & Fishbach, A. (2016). [The pitfall of experimenting on the web: How unattended selective attrition leads to surprising (yet false) research conclusions][5]. Journal of Personality and Social Psychology, 111(4), 493-504. doi:10.1037/pspa0000056 **Demonstrations** - An in-class example demonstrating the different outcomes (e.g., descriptive statistics, hypothesis conclusions) that result when you use different missing data handling methods **we don't have this...please add if you do **Assignments** Find a study with missing data (one of your own projects, or an existing data set). Identify the type of missingness. Decide on a method handling missingness and justify this approach. Complete the exercises in this [article][6] Some people are skeptical of imputation methods, because they believe these approaches are “making up data.” Is this approach accurate? Discuss as a group. Find a published paper where missing data might be a problem. How could the authors have better handled missing data? [1]: https://books.google.com/books/about/Applied_Missing_Data_Analysis.html?id=MN8ruJd2tvgC [2]: https://www.ncbi.nlm.nih.gov/pubmed/23836191 [3]: https://books.google.com/books/about/Applied_Missing_Data_Analysis.html?id=MN8ruJd2tvgC [4]: http://onlinelibrary.wiley.com/doi/10.1111/cdep.12043/abstract [5]: https://www.ncbi.nlm.nih.gov/pubmed/27295328 [6]: https://www.r-bloggers.com/imputing-missing-data-with-r-mice-package/