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 > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
