| 来源类型 | Project |
| 规范类型 | 研究项目 |
| Modelled Weighting and Data Fusion on Gender Mobile Pilot | |
| Sarah Hughes; Jonathan Gellar | |
| 开始日期 | 2019 |
| 结束日期 | 2020 |
| 资助机构 | FinMark Trust |
| 语种 | 英语 |
| 概述 | Mathematica worked with FinMark Trust’s (FMT) research facility Insight2Impact (i2i) to develop a procedure to eliminate bias in short message service (SMS) survey data using a statistical technique called multilevel regression with post stratification (MRP).", |
| 摘要 | Our procedure successfully corrected for problems of the representativeness of the SMS survey, especially when using a small amount of representative data to calibrate the estimates. FMT wants to further explore whether similar adjustments will allow us to make inferences about gender-specific outcomes—such as the social, economic, and health status of women and girls who are underrepresented in SMS surveys. In this project, Mathematica is working with FMT to apply our predictive model to women-specific outcomes in four countries in Africa and Asia: Kenya, Tanzania, Uganda and Pakistan. The project's primary funders are the Bill and Melinda Gates Foundation and the MasterCard Foundation. |
| URL | https://www.mathematica.org/our-publications-and-findings/projects/modelled-weighting-and-data-fusion-on-gender-mobile-pilot |
| 来源智库 | Mathematica Policy Research (United States) |
| 资源类型 | 智库项目 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/491201 |
| 推荐引用方式 GB/T 7714 | Sarah Hughes,Jonathan Gellar. Modelled Weighting and Data Fusion on Gender Mobile Pilot. 2019. |
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