Qualitative Data Analysis Notes

Structure of the data:

Nominal data: gender, occupation, marital status, religious beliefs and so on
Order data: education, job title, hospital level

Numerical data divided into two categories

Count data
Metering data

Descriptive qualitative data analysis ( "single categorical variable")

Chart method (bar chart, FIG consistent (produced just 18 years), and the like)
Numerical calculation (the ratio of the relative risks and odds ratios)

Associations of qualitative variables ( "multiple categories Study variables")

Contingency Analysis: Two the two study, a time study of the relationship between two variables
Correspondence Analysis: "variable relationship," "ordering relations", "polynomial regression (generalized)"
High dimensional column associated with the best Pictured: "FIG mosaic"
FIG consistent (the amount of information provided by a little more), when the relationship between the variables corresponding sample studied may be consistent with FIG.

Front diagram method, the following numerical method is
the same if the samples taken and the overall structure of the sample, the percentage of the ranks can easily use, or can not easily use

Fit Test: Verify that a thing is right

Set the null hypothesis: The default is usually the case, most of the cases, the main case, it is assumed that rarely occurs as a backup

Correspondence analysis and regression analysis

The correlation coefficient:
$\frac{\sum{(x_i-xjz)(yi-yjz)}}{\delta_x*\delta_y}$
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1. Composition analysis points

2. Canonical Correlation Analysis

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Analysis of the code corresponding to R

library(MASS)
data("Suicide")
A1<-xtabs(Freq~age.group+method2,subset=sex=="female",data=Suicide)
A2<-xtabs(Freq~+age.group+method2,subset=sex=="male",data=Suicide)
AA1<-matrix(A1,ncol=8)
AA2<-matrix(A2,ncol=8)
rownames(AA1)=c("F15","F30","F45","F60","F80")
colnames(AA2)=c("poison","gas","hang","drown","gun","knife","jump","other")
rownames(AA2)=c("M15","M30","M45","M60","M80")
colnames(AA2)=c("poison","gas","hang","drown","gun","knife","jump","other")
A<- rbind(AA1,AA2)

ca<-corresp(A,nf=2)
plot(ca)
abline(v=0,h=0,lty=5)