The Results May Surprise You: Analyzing Sex-Disaggregated Data.

Quisumbing discusses how to conduct a gender analysis if you have a limited data set. Quisumbing argues that there are many different gender analyses that researchers can conduct, even with a limited data set. These include using sex of household head to determine whether women-headed households are more likely to lower food consumption in the instance of an unexpected negative event, or ‰ÛÏshock.‰Û She also describes how you can analyze data that is a bit more extensive, including examining the differential impact of parental characteristics by child gender. Quisumbing's article includes a number of different examples of these types of data analysis. Overall, her main point suggests that, if possible, researchers should collect the best sex-disaggregated data that they can; however, even if their sex-disaggregated data is not perfect, they still may be able to conduct some basic yet interesting analysis. While not a how-to-guide, the piece provides gender analysis examples and ideas on what to do with quantitative data on gender (or data that does not directly address gender).

Citation: 
Quisumbing, A. 2014. The Results May Surprise You: Analyzing Sex-Disaggregated Data. Washington, D.C.: IFRPI.
Year: 
2014
Media Type: 
Working Paper
Geographic Focus: 
Banana Cassava Cereals Gender Gender analysis Gender equity Maize Production Rice Sweet Potato Value Chain Women's Empowerment

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