This week’s Learning Resources include several different ways to identify and assess the issue of missing data in your database. From your assessment and knowledge of possible ways to manipulate data, and to overcome potential challenges presented by the missing data, you will be able to analyze and reach conclusions about your analysis. It is important to recognize when and how to apply the most viable solution to the missing data. In addition, it is important to learn to recognize when your data results could be compromised by missing data.
For this, you will analyze the impact of missing data on data analysis.
- Review this week’s Learning Resources.
Analysis of missing data. Include the following in your post:
- An explanation of the importance of handling missing data and skipping patterns and how missing data can affect data analysis
- A technique on how you may handle missing data appropriately with an explanation of why this technique works
- Choose one of the variables you selected for your Scholar-Practitioner Project (select one with missing values) and report proportion of missing for that variable
- Indicate the possible reason for the missing values in that variable.
Wang, R., Sedransk, J., & Jinn, J. H. (1992). Secondary data analysis when there are missing observations. Journal of the American Statistical Association, 7(420), 952–961.
Long, J. A., Bamba, M. I., Ling, B., & Shea, J. A. (2006). Missing race/ethnicity data in Veterans Health Administration based disparities research: A systematic review. Journal of Health Care for the Poor and Underserved, 17(1), 128–140.
Langkamp, D. L., Lehman, A., & Lemeshow, S. (2010). Techniques for handling missing data in secondary analyses of large surveys. Academic Pediatrics, 10(3), 205–210.