xolanincala7-ai/Critical-Replication-of-Davod-Ahmadi-Food-Water-Insecurity-in-Sub-Saharan-Africa-

Critical replication of Davod Ahmadi’s 2019 study on food and water insecurity in Sub‑Saharan Africa, using Afrobarometer Round 6 data and RStudio to reproduce and improve the original analyses — my strongest replication project in the MA‑E‑Science programme. # Critical-Replication-of-Davod-Ahmadi-Food-Water-Insecurity-in-Sub-Saharan-Africa- Critical replication of Davod Ahmadi’s 2019 study on food and water insecurity in Sub‑Saharan Africa, using Afrobarometer Round 6 data and RStudio to reproduce and improve the original analyses — my strongest replication project in the MA‑E‑Science programme. # Critical Replication: Food & Water Insecurity in Sub‑Saharan Africa Part of my MA‑E‑Science portfolio — please read this project (.pdf file) # Summary This project is my strongest replication study, critically re‑examining Davod Ahmadi’s (2019) analysis of the relationship between food insecurity and water insecurity across 32 Sub‑Saharan African countries. Using Afrobarometer Round 6 survey data (2016) and replicating the author’s statistical models in RStudio, I not only reproduced the original findings but also improved the methodology by applying ordinal logistic regression — a more appropriate model for the ordinal nature of the data. # Project Preview Screenshot: github.com # Data Source Afrobarometer Round 6 Household Survey (2016) — 49,137 respondents across 32 SSA countries. World Bank GDP PPP (2018) — used to strengthen economic groupings and contextual analysis. # Methods Replication of descriptive tables, sample characteristics, and bivariate analyses. Re‑implementation of multinomial logistic models in R. Critical improvement: substitution with ordinal logistic regression to better capture ordered outcomes. Careful recoding of variables to correct methodological inconsistencies in the original study. # Results Confirmed Ahmadi’s finding: water insecurity is strongly associated with food insecurity. Socio‑demographic factors (income, education, residence) significantly shape food security outcomes. My improved regression revealed clearer, more robust association …