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1
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library(survey)
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library(srvyr)
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library(readstata13)
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library(dplyr)
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library(tidyr)
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6
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# Load dataset
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path_to_ukhls_folder<- "C:/Users/laure/OneDrive/Documents/AIM CISC Project/Data/UKDA-6614-stata/stata/stata13_se/ukhls"
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setwd(path_to_ukhls_folder)
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main_df<- read.dta13("j_indresp.dta", generate.factors = T, convert.factors = T, nonint.factors = T)
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11
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# Created weighted and unweighted survey design objects
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13
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options(survey.lonely.psu="remove")
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weighted_svy_df<- as_survey_design(main_df, ids = j_psu, strata = j_strata, weights = j_indinus_lw)
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unweighted_svy_df<- as_survey_design(main_df, ids = j_psu, strata = j_strata)
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16
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# Created grouped ethnicity variable for both weighted and unweighted data
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ethn_levels<- levels(main_df$j_ethn_dv)
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19
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weighted_svy_df<- weighted_svy_df%>%
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mutate(ethnic_group = ifelse(j_ethn_dv %in% ethn_levels[c(2,14,11)], "white",
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ifelse(j_ethn_dv %in% ethn_levels[c(3,4,5,6,19)], "asian",
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ifelse(j_ethn_dv %in% ethn_levels[c(7,8,9)], "black",
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ifelse(j_ethn_dv == "missing", "missing",
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"other")))))
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unweighted_svy_df<- unweighted_svy_df%>%
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mutate(ethnic_group = ifelse(j_ethn_dv %in% ethn_levels[c(2,14,11)], "white",
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ifelse(j_ethn_dv %in% ethn_levels[c(3,4,5,6,19)], "asian",
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ifelse(j_ethn_dv %in% ethn_levels[c(7,8,9)], "black",
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ifelse(j_ethn_dv == "missing", "missing",
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"other")))))
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33
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34
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# Create age variable for both weighted and unweighted data
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weighted_svy_df<- weighted_svy_df%>%
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mutate(age = as.numeric(as.character(j_age_dv)))
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38
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unweighted_svy_df<- unweighted_svy_df%>%
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40
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mutate(age = as.numeric(as.character(j_age_dv)))
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41
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# Calculate mean age for black respondents for both weighted and unweighted data
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svymean(~age, subset(weighted_svy_df, ethnic_group == "black"))
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44
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svymean(~age, subset(unweighted_svy_df, ethnic_group == "black"))
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46
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# SE for weighted data is 0.9789
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48
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# SE for unweighted data is 0.5051
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