Weighting and Imputation

In order for the findings of statistical analysis to be generalisable, the sample on which the analysis is based should be representative of the population from which it is drawn. But it is well known that some groups are under-represented in social science surveys: they may be harder to contact in the first place, less likely to agree to participate in the survey, or less likely to answer particular questions even if they do agree to participate.

This short module will introduce students to the techniques used by survey statisticians to overcome these problems. Weighting is used to deal with the problem of certain groups being under-represented in the sample; imputation is used to deal with missing answers to individual questions. Students will learn how and why weighting and imputation work, and will be taken through practical lab-based exercises which will teach them how to work with secondary data containing weights or imputed values.

• University Students from Tier 1 Departments
• Further details regarding eligibility criteria are available here

Students should have a good working knowledge of quantitative data analysis, up to the level of bivariate correlation. Familiarity with Stata will be helpful, but is not essential.

Number of sessions: 1

Stata

This module is not assessed.

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