The data were downloaded from measure DHS as SPSS file, cleaned, and exported to STATA/MP version 16.0 for analysis. We used the household weight (hv005) to deal with the survey weights in the PSM which is the inverse of its household selection probability multiplied by the inverse of the household response rate in the stratum. The matched frequencies and percentages were calculated.
The mean and mean differences in hemoglobin concentration were calculated using paired t‐test and independent t‐test, respectively. The association between livestock ownership and anemia was computed using chi‐square statistics. The relative risks (RR) with a 95% CI were computed using binomial regression model.
PSM was used to determine the effect of livestock ownership on child anemia. Since DHS data are cross‐sectional in nature, the inference is difficult without statistical adjustments. The PSM method allows for the design and analysis of observational data while accounting for the randomization issue via baseline variables (Austin, 2011 (link)). The propensity score is thus the probability of treatment assignment conditional on observed baseline characteristics.
The mean and mean differences in hemoglobin concentration were calculated using paired t‐test and independent t‐test, respectively. The association between livestock ownership and anemia was computed using chi‐square statistics. The relative risks (RR) with a 95% CI were computed using binomial regression model.
PSM was used to determine the effect of livestock ownership on child anemia. Since DHS data are cross‐sectional in nature, the inference is difficult without statistical adjustments. The PSM method allows for the design and analysis of observational data while accounting for the randomization issue via baseline variables (Austin, 2011 (link)). The propensity score is thus the probability of treatment assignment conditional on observed baseline characteristics.
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