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Try creating a Case Summaries list using the Bangladesh data set:
1. Open bdeshc.sav
2. Under the Data menu, go to Sort Cases.
3. Sort By, select the variable prevalence low ac in males and females (acprvfm)
4. Click on Sort Order: Descending
5. Click on OK
6. Click on Statistics, Summarize: Case Summaries
7. Select district, acprvfm (Prevalence low arm circumference), lit (% 7+ years school ), meas (% measles immunization), and roof (% with brick or tin roof).
8. Click on Statistics: Cell Statistics - none and Continue
9. Click on OK
The output is split into two parts, but is to be seen as a whole. Here you should scroll down the page and see if those districts with higher prevalence of low arm circumference have lower literacy, percentages of measles immunization, and lower percentage of households with brick or tin roof (an indicator of wealth). In this particular data set there does not appear to be a clear relationships between these variables. The Lakshmipur district at the top, has a 95% measles immunization but a very high (27.9%) prevalence of low arm circumference. There are also cases of very low coverage of measles immunization yet low prevalence of measles immunization. The relationships are not clear when ranking the districts, but it can help identify some of the areas with severe malnutrition and lacking services.
A good way to examine the associations between malnutrition and variables such as health service and poverty estimation (e.g. roofing quality) is by scatterplotting (where each case is a district). A lesson in scatterplotting was shown previously in Chapter One Descriptive Analysis (page1).
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