Thanks for the link. I had not been aware of that.
On 29/07/14 15:27, Bert Gunter wrote:
1. If you are asking about statistics, this is the wrong list. Post
here instead: stats.stackexchange.com.
2. If you you are asking about what sorts of statistical analyses are
available in R, check the CRAN task views here:
http://cran.r-project.org/web/views/
3. If you are asking about how to program in R and have not already
done so, please read "An Introduction to R" or R web tutorial of your
choice before posting here further.
Bert
Bert Gunter
Genentech Nonclinical Biostatistics
(650) 467-7374
"Data is not information. Information is not knowledge. And knowledge
is certainly not wisdom."
Clifford Stoll
On Tue, Jul 29, 2014 at 6:01 AM, Sun Shine <phaedr...@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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