[R] analyzing qualitative data sets

Sun Shine phaedrusv at gmail.com
Tue Jul 29 20:00:21 CEST 2014

Thanks, I'll look into that further.


On 29/07/14 16:44, Richard M. Heiberger wrote:
> For your item 2,
> (2) 25 obs x 14 var of ordinal rank data [5 item Likert-scale, with
> some missing values], and
> I recommend the likert function in the HH package
> install.packages("HH")
> library(HH)
> ?likert
> Rich
> On Tue, Jul 29, 2014 at 9:01 AM, Sun Shine <phaedrusv at gmail.com> wrote:
>> 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.
>> Cheers
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