This is a not easy question. There are many methods to do this. For example see:

Data Reduction Techniques for

Large Qualitative Data Sets

http://web.stanford.edu/~thairu/07_184.Guest.1sts.pdf

It is not an easy process. Use data reduction techniques. Follow the techniques presented by Stanford University in the given attachment.

http://web.stanford.edu/~thairu/07_184.Guest.1sts.pdf

We can use data reduction tool, factor analysis for the reduction of questions for a qualitative research study. As for extra theoretical data, usually I keep them.

@Mahfuz

Does factor analysis apply to qualitative data ?

Dear Mohamed

When one collects the qualitative data, one has to be focused on certain themes and issues, derived on the basis of the research question and the survey of reserach. The researcher has to be very careful that s/he does not get astray and be precise. Otherwise, it would be confusing and may even lead to wrong conclusions. One has to repress the desire to go into remotely relevant issues. Of course, collateral data is always helpful for building context. But for that also one has to decide clearly whether or not the information is relevant to  building the specific context. When this norm is followed, the problem you are raising would not arise.

Handling of abundant data is cumbersome no doubt. You can use define and apply search string to filter unnecessary data. I fully agree with @Debi, one has to be focused on certain themes and issues only.

The latest and recent approach is to have a Systematic Literature Review. (SLR).

I think it can be useful if you are taking a very interpretative approach to your data to confine yourself to just a limited manageable  'sample'. That 'sample' is unlikely to be representative but putting a restriction on its size may help mitigate confirmation bias by only focusing on the data that suits our prejudices and ignoring the rest. 

dear researcher, when collecting data you have to collect as much as possible because data colelctors cannot go the field again and again. however, when collecting data, researcher keep "OBJECTIVES OF THE STUDY" in their mind. Data have to be collected to suit within the scope of the study. data collection takes time. You can use the dat collected to fit for your research objectives. other data can be used in some other occassations where appropriate.   

I agree with @Debi to some extent - one has to be focussed on the research question & objective, but whether or not you allow the interviewee to 'stray' from the topic will depend upon your methodological approach. In psychosocial studies which use the Free Association Narrative Interview  method (see Hollway & Jefferson's 'Doing Qualitative Research Differently' 2nd edition, 2013) or Tom Wengraf's Biographical Narrative Interpretive Method (BNIM, sorry I don't have a specific reference, but try via UK's Assoc for Psychosocial Studies), such 'excursions' are a valuable part of the data, providing insight into what is sayable/unsayable, remark-able/avoided.  And of course a reflexive journal is essential for keeping an eye on one's own 'stuff', especially when co-constructing stories as data, and in analysing the detailed interactions in interviews.

If you find yourself overwhelmed by the sheer volume of data, a good qualitative analysis programme like Nvivo, (which I prefer) or Atlas, can be very helpful in sorting, ordering and managing all sorts of qual data.

Dear Mohamed,

As I see it, you have several choices as to how to deal with the data.

1. Use the guiedlines for data saturation to realize when you have all the information you need to reach adequate interpretation. When you feel "saturated" stop analyzing and start writing.

2. A large data-set is an opportunity to split up the team and work on diferent groups of data. Each may take a portion and analyze it. Then get back together and see what you have in common and what is different. This should bring about a richer analysis.

3. Treat the data as if it is more than one sample. Analyse "one" sample and reach your conclusions, then anaylyze the "second" sample as if anew, and see if you reach the same conclusion.

CAVEAT: Be careful not ti fall into the trap of thinking the large data-set enables better generalizability. IT DOESN"T! The sample is not randomized (I assume) and you are not working within a positivist framework (I assume).

Best of luck.

Cobi.

@Jacob

Thanks for the constructive and informative comment which informs on the basics that we tend to sometimes forget.

Look here http://www.sagepub.com/upm-data/43144_12.pdf

One option to handle large amounts of text data is to use Latent Dirichlet Allocation for topic modelling which is a probabilistic method of deriving topics from a large corpus. This can then serve as the basis for further analysis of those topics, or perhaps comparison of topics between datasets. It really depends on what you want to do with the data and the kind of research questions that you are asking.

http://tedunderwood.com/2012/04/07/topic-modeling-made-just-simple-enough/

https://code.google.com/p/topic-modeling-tool/

A simple answer....Divide it into several publications on specific topics. That is, maybe you asked a general, exploratory question, as is often the case with qualitative research. You discover you have data on 20 sub-topics. Consider writing about each sub-topic separately. It's a great problem to have!  In one study I conducted on culture in child sexual abuse, I ended up with data on disclosure, family issues, psychotherapy, the role of religion, etc. I focused on disclosure for my first publications.

Thank you Lisa, you highlight an important avenue for me to explore.

Thanks for your good question. I also learned a lot from the various responses given.Just like Lisa mine was small I only managed two publications. I think the publication route is smart.

I have written about this in the methodological appendix to my book, The National Origins of Policy Ideas (Princeton University Press, 2014) with Ove Pedersen.  We used Atlas.ti qualitative data analysis software to help organize both interview and some document data in order to systematically analyze it.  This is time consuming but effective in plowing through thousands of pages of information.

Thank you Dr Campbell, I am grateful for the additional reference.

Problem of plenty? Asset (huge data) becoming headache/ liability (huge task of data processing)?

Appreciate the valid suggestions given by Nageswar Rao, Lisa and Costas Drossos.

Dear Mohamed,  

Rich data in fact makes me much satisfied than poor data. After collection of  some part of the data,  (3-4 interviews, field notes)  I usually read it all  looking for structures and ground for asking  other detalied question in the field. After leaving cultural scene in the field I read all many times to feel  much overloaded with all dimensions of the contents and have ideas  for emic codes and categories. Sometimes  I also use etic categories.  Similar advices you'll find in J. Spradley's, U. Flick's, G. Graham's, M. Angrosino's books.

I agree with Rabinarayan and others that there is a real trade-off between having a large, rich data set versus all of the effort that goes into analyzing that much qualitative data. Part of Lisa's suggestion of writing multiple papers is the idea that there are probably multiple, related research questions that can be addressed with this data.

So, one approach would be to focus on the most central research question and the data that is most directly connected to that. That would give a place to start, and during that process you would also learn more about the overall data resources that you have.

Hi Mohamed - I may be a little biased since I'm a community manager at QSR but I really think a tool like NVivo can help with the "swamped by data" feeling. You can import all your material, manage it in one place..and use some quick ways to categorise it by theme. I agree with David that you need to focus on your central research question  - but by having all your data in a tool like NVivo you can easily hone in on what is relevant without losing any gems.