HIVChange {QCA3} | R Documentation |
Data set from Cronqvist and Berg-Schlosser(2006), examining the HIV prevalence in Sub-Saharan Africa.
data(HIVChange)
A data frame with 12 observations on the following 7 variables.
LIT00
GENDEREQ
MORTALITY
AGRARGDP
HIVChange
Country
NCase
LIT00
measures socio-economic factors. GENDEREQ
measures
the situation of women. MORTALITY
measures the awareness of HIV
threat. AGRARGDP
measures the impact of migration.
Cronqvist. L. and Berg-Schlosser, D. 2006. Determining The Conditions Of Hiv/Aids Prevalence In Sub-Saharan Africa: Employing New Tools Of Macro-Qualitative Analysis. In Innovative Comparative Methods For Policy Analysis: Beyond The Quantitative-Qualitative Divide. Benoit Rihoux and Heike Grimm (Eds).Springer.
cond <- c("LIT00", "GENDEREQ", "MORTALITY", "AGRARGDP") ## example in p161: not exactly the same solution. This one is correct too (tell me if you don't think so) reduce(HIVChange,"HIVChange",cond,expl="positive",rem="incl",contr="negative",nlevels=c(2,2,3,2),pre="pass",NCase="NCase",Cases="Country") reduce(HIVChange,"HIVChange",cond,expl="negative",rem="incl",contr="positive",nlevels=c(2,2,3,2),pre="pass",NCase="NCase",Cases="Country") ## example in p163 reduce(HIVChange,"HIVChange",cond,expl="positive",rem="incl",contr="negative",nlevels=c(2,2,3,2),pre="pass",NCase="NCase",Cases="Country") reduce(HIVChange,"HIVChange",cond,expl="negative",rem="incl",contr="negative",nlevels=c(2,2,3,2),pre="pass",NCase="NCase",Cases="Country") ## C.A.R is positive, all other three are nagetive