Father-child interaction

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1990 Doug White: Single Factor Analysis Reliability in Comparative and Ethnographic Observations: The Example of High Inference Father-Child Interaction Measures, Journal of of Quantitative Anthropology 2: 109-150.

[edit] Father and child

  • v54 Father's closeness
  • v369 or 379 (use one only) Sex of parental caretaker
  • v991 Father's caretaking role
  • v613 Male caretaking role

Annie Dihn correlates

  • v614 Final authority over upbringing (r=-.44)
  • v353 Sex of parents in residence: Early boy (r=-.31)

[edit] Start

help.start()
library(foreign) #the format for the input dta file is Stata, which is foreign, it was made from Spss
getwd() #see your working director name: you might want to set it to
% setwd("C:/Program Files/R//R-2.6.2/")
setwd("desktop")
#See: http://web.csb.ias.edu/library/foreign/html/read.dta.html 
sccs<-read.dta("http://intersci.ss.uci.edu/wiki/pub/SCCSvar1-2008NoMapStata8.dta")#download 1st time by right clicking the url and saving to your working directory
sccs<-read.dta("SCCSvar1-2008NoMapStata8.dta") 
attach(sccs)
plot(v891,v893,xlab="Int War",ylab="Ext War-Attacked") #(test whether data has been read)
length(v891) # check that length is 186 as for SCCS
library(gmodels)
CrossTable(v891,v893,expected=TRUE,prop.chisq=TRUE,fisher=TRUE,dnn=c("v891 Int War","v893 External War:Attacked")) #delete these options if not needed
names(sccs)
length(sccs) #number of variables
  1. v54 Father's closeness
  2. v369 or 379 (use one only) Sex of parental caretaker
  3. v991 Father's caretaking role
  4. v613 Male caretaking role
  5. correlates
  6. v614 Final authority over upbringing (r=-.44)
  7. v353 Sex of parents in residence: Early boy (r=-.31) 
x1=as.numeric(v54)
x2=as.numeric(v369)
x3=as.numeric(v991)
x4=as.numeric(v613)
x5=as.numeric(v353) #these are numeric with NA, 186 cases
cols<-na.exclude(data.frame(x1,x2,x3,x4,x5)) # reverts to categorical, right # cases
x1=as.numeric(cols[,1])
x2=as.numeric(cols[,2]) 
x3=as.numeric(cols[,3]) 
x4=as.numeric(cols[,4]) 
x5=as.numeric(cols[,5]) 
length(x5);length(x5) #check matching l.enths
mat <- cbind(x1,x2,x3,x4,x5) #this binds them in the proper alignment, and have checked this against Spss
factanal(mat,factors=1,rotation="none")
  1. Problem here is with N=24 there are too few cases, too much random error perhaps. Pairwise correlation is needed. 






x1=as.numeric(v892)
x2=as.numeric(v893)
x3=as.numeric(v894)
x4=as.numeric(v895)
x5=as.numeric(v900) #these are numeric with NA, 186 cases
z5=as.numeric(x5)

cols<-na.exclude(data.frame(x1,x2,x3,x4,x5)) # reverts to categorical, right # cases 

x1=as.numeric(cols[,1])
x2=as.numeric(cols[,2]) 
x3=as.numeric(cols[,3]) 
x4=as.numeric(cols[,4]) 
x5=as.numeric(cols[,5]) 
length(x5);length(x5) #check matching l.enths
mat <- cbind(x1,x2,x3,x4,x5) #this binds them in the proper alignment, and have checked this against Spss
factanal(mat,factors=1,rotation="none")
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