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MIS 2007 Analysis Methods

MIS 2007 Analysis Methods. Weighting and Data Analysis Allen HIghtower, CDC Kenya. Weighting and Data Analysis. MIS sample design and weighting Calculation of weights Adjustment for non-response Normalized/standardized weights Data analysis. MIS Multi-Stage Stratified Cluster Design.

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MIS 2007 Analysis Methods

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  1. MIS 2007Analysis Methods Weighting and Data Analysis Allen HIghtower, CDC Kenya RBM-MERG Malaria Indicator Training Workshop, 9th-12th September 2008, Lusaka, Zambia

  2. Weighting and Data Analysis • MIS sample design and weighting • Calculation of weights • Adjustment for non-response • Normalized/standardized weights • Data analysis RBM-MERG Malaria Indicator Training Workshop, 9th-12th September 2008, Lusaka, Zambia

  3. MIS Multi-Stage Stratified Cluster Design RBM-MERG Malaria Indicator Training Workshop, 9th-12th September 2008, Lusaka, Zambia

  4. Multi-stage cluster samples • Unequal probability of selection • difference in probability of selection • difference in behaviors across sub-groups • biased results • Simple random sample • equal probability of selection • self weighting RBM-MERG Malaria Indicator Training Workshop, 9th-12th September 2008, Lusaka, Zambia

  5. Why Weight? • To correct for unequal probability of selection • To produce results that are representative of the larger population from which the sample was drawn • To adjust for non–response RBM-MERG Malaria Indicator Training Workshop, 9th-12th September 2008, Lusaka, Zambia

  6. Steps in Calculating Sampling Weights • Calculate probabilities of selection • Convert the probabilities into weights • Normalize or standardize the weights in order to reflect the correct sample size RBM-MERG Malaria Indicator Training Workshop, 9th-12th September 2008, Lusaka, Zambia

  7. Calculating Probabilities of Selection • The probability of selection depends upon the sample design used • For the MIS three-stage sample design the probability of selection is the joint probability of selection at the three stages of sample selection • Pi = Pa * Pb * Pc RBM-MERG Malaria Indicator Training Workshop, 9th-12th September 2008, Lusaka, Zambia

  8. Probabilities of selection • Pa = P cluster sleeted in NASSEP IV # Cs selected / # Cs available • Pb = P cluster selected in MIS # Cs selected / # Cs available • Pc = P household selected in MIS # HHs selected / # HHs available • Pi = Overall P HH selected in MIS for cluster i RBM-MERG Malaria Indicator Training Workshop, 9th-12th September 2008, Lusaka, Zambia

  9. Calculating the Sampling Weight • The sampling weight assigned to a household in a given cluster is the inverse of its probability of selection • Wi= 1/Pi • Wi = weight for households in the ith cluster • Pi = probability of selection for households in the ith cluster RBM-MERG Malaria Indicator Training Workshop, 9th-12th September 2008, Lusaka, Zambia

  10. Standardized/Normalized Weights • Standardized weights are used to avoid generating incorrect standard errors and confidence intervals • Weights are standardized to the sample size and have a mean of 1.0. RBM-MERG Malaria Indicator Training Workshop, 9th-12th September 2008, Lusaka, Zambia

  11. Calculating Standardized Weights • wi' = wi n /  wi ni where • wi’ = standardized weight for cluster i • wi = sampling weight for cluster i • n = total sample size • ni = sample size for cluster i • The sum should equal the number sampled. RBM-MERG Malaria Indicator Training Workshop, 9th-12th September 2008, Lusaka, Zambia

  12. Adjusting weights for non-response • Consider adjusting weights for non-response at the household and individual level - when non-response is higher than expected - when response rates differ by demographic characteristics – sex, education ... RBM-MERG Malaria Indicator Training Workshop, 9th-12th September 2008, Lusaka, Zambia

  13. Calculating non-response adjustment • The non-response adjustment factor (A) is the inverse of the response rate (r) Ai = 1/ri The final weight adjusted for non-response is the product of the standardized weight and the adjustment factor Final Weight = wi' * Ai RBM-MERG Malaria Indicator Training Workshop, 9th-12th September 2008, Lusaka, Zambia

  14. MIS Weights • Household weights = HW • Individual weights = HW /HRR • Lab weights = IW */LRR • HRR= HH response rate • LRR= Lab response rate RBM-MERG Malaria Indicator Training Workshop, 9th-12th September 2008, Lusaka, Zambia

  15. Data Analysis • Software programs designed for complex surveys should be used to analyze MIS data. • SUDAAN, SAS, SPSS, and Stata have procedures to account for the multi-stage stratified sampling design, and produce reliable standard errors and confidence intervals. RBM-MERG Malaria Indicator Training Workshop, 9th-12th September 2008, Lusaka, Zambia

  16. Sample SUDAAN Code • PROC CROSSTAB DATA=MIS1 filetype=sas design=WR; • NEST strata1 qhnassep /psulev=2 missunit; • WEIGHT Weight; • TABLE Sex*Parasite; • CLASS Sex Parasite; • RUN; RBM-MERG Malaria Indicator Training Workshop, 9th-12th September 2008, Lusaka, Zambia

  17. Sample SAS Code • PROC SURVEYFREQ data=MIS1; • WEIGHT weight; • STRATA strata1; • CLUSTER qhnassep; *** EA ID variable **; • TABLES parasite / cl row deff; *** Y/N Parasitemia **; • RUN; RBM-MERG Malaria Indicator Training Workshop, 9th-12th September 2008, Lusaka, Zambia

  18. Sample Data RBM-MERG Malaria Indicator Training Workshop, 9th-12th September 2008, Lusaka, Zambia

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