HSAUR2 (version 1.1-20)

CHFLS: Chinese Health and Family Life Survey

Description

The Chinese Health and Family Life Survey sampled $60$ villages and urban neighborhoods chosen in such a way as to represent the full geographical and socioeconomic range of contemporary China.

Usage

data("CHFLS")

Format

A data frame with 1534 observations on the following 10 variables.

R_region

a factor with levels Coastal South, Coastal Easth, Inlands, North, Northeast, Central West.

R_age

age of the responding woman.

R_edu

education level of the responding woman, an ordered factor with levels Never attended school < Elementary school < Junior high school < Senior high school < Junior college < University.

R_income

monthly income of the responding woman.

R_health

self-reported health status, an ordered factor with levels Poor < Not good < Fair < Good < Excellent.

R_height

height of the responding woman.

R_happy

self-reportet happiness of the responding woman, an ordered factor with levels Very unhappy < Not too happy < Somewhat happy < Very happy.

A_height

height of the woman's partner.

A_edu

level of education of the woman's partner, an ordered factor with levels Never attended school < Elementary school < Junior high school < Senior high school < Junior college < University.

A_income

montjly income of the woman's partner.

Details

Contemporary China is on the leading edge of a sexual revolution, with tremendous regional and generational differences that provide unparalleled natural experiments for analysis of the antecedents and outcomes of sexual behavior. The Chinese Health and Family Life Study, conducted 1999--2000 as a collaborative research project of the Universities of Chicago, Beijing, and North Carolina, provides a baseline from which to anticipate and track future changes. Specifically, this study produces a baseline set of results on sexual behavior and disease patterns, using a nationally representative probability sample. The Chinese Health and Family Life Survey sampled 60 villages and urban neighborhoods chosen in such a way as to represent the full geographical and socioeconomic range of contemporary China excluding Hong Kong and Tibet. Eighty-three individuals were chosen at random for each location from official registers of adults aged between 20 and 64 years to target a sample of 5000 individuals in total. Here, we restrict our attention to women with current male partners for whom no information was missing, leading to a sample of 1534 women. The data have been extracted as given in the example section.

References

William L. Parish, Edward O. Laumann, Myron S. Cohen, Suiming Pan, Heyi Zheng, Irving Hoffman, Tianfu Wang, and Kwai Hang Ng. (2003), Population-Based Study of Chlamydial Infection in China: A Hidden Epidemic. Journal of the American Medican Association, 289(10), 1265--1273.

Examples

if (FALSE) {

    ### for a description see http://popcenter.uchicago.edu/data/chfls.shtml
    library("TH.data")
    load(file.path(path.package(package="TH.data"), "rda", "CHFLS.rda"))

    tmp <- chfls1[, c("REGION6", "ZJ05", "ZJ06", "A35", "ZJ07", "ZJ16M", "INCRM",
                      "JK01", "JK02", "JK20", "HY04", "HY07", "A02", "AGEGAPM", 
                      "A07M", "A14", "A21", "A22M", "A23", "AX16", "INCAM", "SEXNOW", "ZW04")]

     names(tmp) <- c("Region",
                "Rgender",               ### gender of respondent
                "Rage",                  ### age of respondent
		"RagestartA",		 ### age of respondent at beginning of relationship 
                                         ### with partner A
                "Redu",                  ### education of respondent
                "RincomeM",              ### rounded monthly income of respondent
		"RincomeComp",		 ### inputed monthly income of respondent
                "Rhealth",               ### health condition respondent
                "Rheight",               ### respondent's height
                "Rhappy",                ### respondent's happiness
                "Rmartial",              ### respondent's marital status
                "RhasA",                 ### R has current A partner
                "Agender",               ### gender of partner A
                "RAagegap",              ### age gap
                "RAstartage",            ### age at marriage
                "Aheight",               ### height of partner A
                "Aedu",                  ### education of partner A
                "AincomeM",              ### rounded partner A income
                "AincomeEst",            ### estimated partner A income
                "orgasm",                ### orgasm frequency
                "AincomeComp",           ### imputed partner A income
                "Rsexnow",               ### has sex last year
                "Rhomosexual")           ### R is homosexual

    ### code missing values
    tmp$AincomeM[tmp$AincomeM < 0] <- NA
    tmp$RincomeM[tmp$RincomeM < 0] <- NA
    tmp$Aheight[tmp$Aheight < 0] <- NA

    olevels <- c("never", "rarely", "sometimes", "often", "always")
    tmpA <- subset(tmp, Rgender == "female" & Rhomosexual != "yes" & orgasm %in% olevels)

    ### 1534 subjects
    dim(tmpA)

    CHFLS <- tmpA[, c("Region", "Rage", "Redu", "RincomeComp", "Rhealth", "Rheight", "Rhappy",
                      "Aheight", "Aedu", "AincomeComp")]
    names(CHFLS) <- c("R_region", "R_age", "R_edu", "R_income", "R_health", "R_height", 
                      "R_happy", "A_height", "A_edu", "A_income")
    levels(CHFLS$R_region) <- c("Coastal South", "Coastal Easth", "Inlands", "North", 
                                "Northeast", "Central West")

    CHFLS$R_edu <- ordered(as.character(CHFLS$R_edu), levels = c("no school", "primary", 
                           "low mid", "up mid", "j col", "univ/grad"))
    levels(CHFLS$R_edu) <- c("Never attended school", "Elementary school", "Junior high school", 
                             "Senior high school", "Junior college", "University")
    CHFLS$A_edu <- ordered(as.character(CHFLS$A_edu), levels = c("no school", "primary", 
                           "low mid", "up mid", "j col", "univ/grad"))
    levels(CHFLS$A_edu) <- c("Never attended school", "Elementary school", "Junior high school", 
                             "Senior high school", "Junior college", "University")

    CHFLS$R_health <- ordered(as.character(CHFLS$R_health), levels = c("poor", "not good", 
                              "fair", "good", "excellent"))
    levels(CHFLS$R_health) <- c("Poor", "Not good", "Fair", "Good", "Excellent")

    CHFLS$R_happy <- ordered(as.character(CHFLS$R_happy), levels = c("v unhappy", "not too", 
                             "relatively", "very"))
    levels(CHFLS$R_happy) <- c("Very unhappy", "Not too happy", "Relatively happy", "Very happy")
}