(This entry was written by Gina)
In my statistics class last semester, we learned about he various kinds of data; how to calculate it, enter it into statistical programs, make determinations about frequencies and standard deviations, determine levels of significance, etc. The thing was, we were using fake data created by our instructors so we would come to certain conclusions for testing purposes. The hard part, which they can’t really teach, is step one—determining how best to enter the data. For future statistical analysis, which OIT will need to prove effectiveness of the program, I have been quantifying data about the orphans who we have been assessing these last few weeks.
Age, birth-date, and number of people living in your house seem, from an American perspective, like easy numbers to enter. But in Africa, where no one knows how old they are because that’s just not that important here, or a “house” can mean any number of things, figuring these things out and accurately quantifying them presents unique challenges. How does one decide what number to enter when a four-year-old girl has had malaria so many times her care-giver can’t count them (one of which is at the moment she is being interviewed)? Is chronic neglect a major or minor medical issue? Do bananas qualify as a food group when no other fruits or vegetables are being consumed? Does the seven-year-old girl whose illiterate mother is only 20, and whose father disappeared, really not count as an orphan? These are interesting determinations to make. Most difficult is the determining why I feel, or anyone feels, that we are somehow capable of making these choices.
But we make our best estimates, consult with our counterparts, and hope that all this confusion will somehow come together, and the work we are doing now means that next year, it can be said with confidence that each child consumes four food groups regularly, has been tested for HIV (and is receiving any necessary treatment), and was able to go to school.
In my statistics class last semester, we learned about he various kinds of data; how to calculate it, enter it into statistical programs, make determinations about frequencies and standard deviations, determine levels of significance, etc. The thing was, we were using fake data created by our instructors so we would come to certain conclusions for testing purposes. The hard part, which they can’t really teach, is step one—determining how best to enter the data. For future statistical analysis, which OIT will need to prove effectiveness of the program, I have been quantifying data about the orphans who we have been assessing these last few weeks.
Age, birth-date, and number of people living in your house seem, from an American perspective, like easy numbers to enter. But in Africa, where no one knows how old they are because that’s just not that important here, or a “house” can mean any number of things, figuring these things out and accurately quantifying them presents unique challenges. How does one decide what number to enter when a four-year-old girl has had malaria so many times her care-giver can’t count them (one of which is at the moment she is being interviewed)? Is chronic neglect a major or minor medical issue? Do bananas qualify as a food group when no other fruits or vegetables are being consumed? Does the seven-year-old girl whose illiterate mother is only 20, and whose father disappeared, really not count as an orphan? These are interesting determinations to make. Most difficult is the determining why I feel, or anyone feels, that we are somehow capable of making these choices.
But we make our best estimates, consult with our counterparts, and hope that all this confusion will somehow come together, and the work we are doing now means that next year, it can be said with confidence that each child consumes four food groups regularly, has been tested for HIV (and is receiving any necessary treatment), and was able to go to school.
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