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Home » Data » Weighting data » Guidance Needed on Weighting for Pooled DHS Data in Logistic Regression
Guidance Needed on Weighting for Pooled DHS Data in Logistic Regression [message #30319] Sat, 02 November 2024 08:49 Go to previous message
ygkim127 is currently offline  ygkim127
Messages: 3
Registered: November 2024
Member
Hello,

I am reaching out with a question regarding data weighting. I am conducting research on "The Effect of Girls' Empowerment on Adolescent Pregnancy in Sub-Saharan Africa," aiming to investigate whether increased aged 15-19 girls' empowerment has a positive effect on reducing adolescent pregnancy rates in this region.

I plan to pool data from 27 Sub-Saharan African countries and will be using DHS-7 and DHS-8 data from the IR datasets of these countries. The explanatory variable will be women's empowerment, while the dependent variable will be the pregnancy status of adolescents aged 15-19. I intend to perform logistic regression analysis using Stata.

Since I need the overall pooled set weights that can represent Sub-Saharan Africa, I want to ensure that I am correctly calculating and applying these weights. I have read previous posts on this users forum and the "Note on DHS standard weight de-normalization" file, but I would appreciate your guidance to confirm my understanding and approach.

I have conducted weight de-normalization for each country using the formula: V005×(total females age 15-49 in the country at the time of the survey)/(number of women age 15-49 interviewed in the survey)
I have extracted data only for married women aged 15-19 from each country.
I have used the "append" function to pool the data from the 27 countries into one dataset.
I want to apply weights when conducting logistic regression analysis, but I am unsure how to do so.
Please let me know if there are any mistakes in the sequence of these steps or in the weight de-normalization process. Additionally, I would greatly appreciate the exact Stata code for applying weights in the pooled dataset and conducting the logistic regression analysis.

I look forward to your response.

Thank you.
 
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