Advanced Handling of Missing Data (Intensive)

PrerequisitesUpdated

This module is part of the Social Science Research Methods Centre training programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

This course provides an introduction to handling missing data in quantitative research. We will first discuss the challenges of missing data in different fields, and shortcomings of ad-hoc methods such as listwise deletion, carrying the last value forward, or mean imputation. We will then introduce multiple imputation methods as a more advanced method to handle missingness. The concepts will be illustrated with social science data examples using the software Amelia in R.

• Mphil and PhD students from participating departments taking the Social Science Research Methods Centre training programme as part of their research degree

• Good knowledge of quantitative data analysis and regression models.
• Basic knowledge in R.
• Bring your laptop (and a data project) to the sessions.

Number of sessions: 1

1. Challenges of missing data 2. Ad-hoc methods 3. Multiple Imputation Techniques 4. Lab: Multiple Imputation with Amelia II

• Students will develop knowledge of how to handle missing data

• To teach students how to handle missing data.

Presentations, demonstrations and practicals

• James Honaker and Gary King, "What to do About Missing Values in Time Series Cross-Section Data" American Journal of Political Science Vol. 54, No. 2 (April, 2010): Pp. 561-581. Article PDF
• Gary King, James Honaker, Anne Joseph, and Kenneth Scheve. "Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation", American Political Science Review, Vol. 95, No. 1 (March, 2001): Pp. 49-69. Article
• Jason Sorens and William Ruger, Does Foreign Investment Really Reduce Repression? International Studies Quarterly, Volume 56, Issue 2, pages 427–436, June 2012. Article
• Andrew Gelman and Jeniffer Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, CHAPTER 25: Missing-data imputation. Cambridge University Press, Cambridge (2006).

Readings available as pdfs via http://www.ssrmc.group.cam.ac.uk/modules/core/missing-data

• To gain maximum benefits from the course it is important that students do not see this course in isolation from the other MPhil courses or research training they are taking.
• Responsibility lies with each student to consider the potential for their own research using methods common in fields of the social sciences that may seem remote. Ideally this task will be facilitated by integration of the SSRMC with discipline-specific courses in their departments and through reading and discussion.

• This is an intensive, one-day module.

Once in 2015/16