[R] analyzing qualitative data sets

Sun Shine phaedrusv at gmail.com
Tue Jul 29 15:01:23 CEST 2014

Hello list

I'm just beginning my PhD and am likely to be using lots of surveys in 
my data collection, and am wanting to get my head around the ideas about 
how best to approach the tasks in R.

The data sets I have collected so far for some preliminary practise with 
are made up of the following survey data:

(1) 25 observations x 15 variables of dichotomous nominal (categorical) 
data [basically, yes/ no responses with a couple of missing values]

(2) 25 obs x 14 var of ordinal rank data [5 item Likert-scale, with some 
missing values], and

(3) 23 observations of free text, typically in the form of one sentence 
or statement, and I will be using RQDA for that part.

So far, I have been able to piece together that I can use the Spearman 
method of the wilcox.text for #2 (ordinal data), but have yet to find 
anything that I can do for the nominal data. I was thinking of using 
frequency tables, but I don't seem to be able to find out too much info 
on it/ how to do that.
Anyway, I have three questions that I'd appreciate members of this list 
taking a swing at for ideas please.

(a) what types of analyses are available to apply to the data types 
above? I have been thinking about MCA using FactoMineR as well as MDS 
using MASS to visualise the data in high dimensional space, but I think 
that I haven't (yet!) figured out how to properly prepare my data sets 
for these, and most texts and tutorials seem to focus mostly on 
quantitative data analysis.

(b) is there anyway that I can automate the Spearman process so that it 
iterates across the set, otherwise it looks like I may have to manually 
take the two columns and keep comparing pairs until I have correlated 
all of the columns with all of the other columns - so is there anyway 
that I can automate this and get the test statistics and p values dumped 
in a table for summarising?

(c) after using RQDA to code the statements, is it feasible to 
reintroduce those codes back into the data set to explore correlations 
among the other columns and the units of coded text to see what 
variables co-occur?

Well, thanks for taking the time to read this - and I look forward to 
any thoughts/ suggestions that might help.


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