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data cleaning

Getting to better data: who does the editing?

Markus Goldstein's picture

In a previous post I talked about some issues with collecting gender disaggregating data in practice. Susan Watkins helpfully pointed me to a number of papers which provide more systematic and thoughtful evidence on data collection issues that a lot of us face and I thought it would be useful to summarize some of them here.  

Robustly wrong? New methods for cleaning survey data on incomes: Guest post by Martin Ravallion

Survey responses to questions on incomes (and other potentially sensitive topics) are likely to contain errors, which could go in either direction and be found at all levels of income. There is probably also non-random selection in terms of who agrees to be interviewed, implying that we get the weights wrong too (as used to "gross-up" the sample estimates to the population).