Hi Azhar, 

You will need to look at mixed-methods approaches to help you.  It's about how two different types of data can work together in synergy. I expect if you do some reading on Pragmatism - a popular mixed-methods approach, this will help you make sense of how qualitative and quantitative data can compliment, compare and contrast and help you explore your research questions.

http://mmr.sagepub.com/content/4/1/6

http://rjh.goingeast.ca/2011/11/05/pragmatism-and-mixed-methods-research/

THanks Arun for the links, will surely go throught them.

You simply collect some quantitative data---and analyse it with your method of choice;  then do the same for the qualitative part of your study; the qualitative complements the quantitative by 'painting a picture' of it.

The key question is the purpose that the qualitative data will serve. In mixed methods terms, the kind of design you would follow is summarized as QUANT --> qual, so the issue is the role that a smaller qualitative follow-up study would play in the overall design.

In general there are two such purposes. One is to explore unanswered questions from the quantitative research, such as when you have unexpected results. This requires you to complete your quantitative analysis before you collect your qualitative data. The second purpose that a qualitative follow-up can pursue is to provide illustrative materials that show how and why your quantitative methods produced the results that they did.

If your goal is primarily illustrative, it might be simpler to include some strategically placed open-ended questions in the original survey, rather than doing a separate set of qualitative interviews.

I agree with David; neat.

Thank you Prof.Adams and Prof.Morgan for the differnt angles to look at the combination of these methods. Will certainly go through it for better analysis.

The following papers may help, vide: the pros and cons of using mixed methods (Johnson and Onwuegbuzie, 2004, p. 21); and the recommendations for the design/implementation/report of a mixed methods study (Hanson et al, 2005, pp. 232-233).

• Hanson, W., E., Creswell, J., W. , Plano Clark, V., L. , Petska, K., S. Petska and Creswell, J. D. (2005) Mixed Methods Research Designs in Counseling Psychology, Journal of Counseling Psychology, 52, 2, pp. 224-235.
• Johnson, R. B. and Onwuegbuzie, A. J. (2004) Mixed Methods Research: A Research Paradigm Whose Time Has Come, Educational Researcher, 33, 7, pp. 14-26.

En utilisant des analyses factorielles de correspondances (AFC) qui permettent de traiter en même temps des variales qualitatives préalablement codées et des variables quantitatives, par exemple.

Perhaps look at grounded approach as well.

The mixed methods approach will certainly assist. All you need to do is to employ appropriate  qualitative data collection methods that are  aligned to study objective and questions

Just lectured about this issue yesterday.  Promoted Dr. Morgan's Priority-Sequence model (1998) for developing mixed methods for health research.  Glad to see Dr. Morgan has shared his expertise because this is who I would have suggested.  Jennifer Greene's early work in program evaluation also offers a pragmatic take on mixed methods that can be considered outside of evaluation contexts:

Greene, J.C., Benjamin, L., & Goodyear, L. (2001). The merits of mixing methods in evaluation. Evaluation, 7(1): 25-44.

Greene, J.C., Caracelli, V|.J., & Graham, W.F. (1989). Toward and conceptual framework for mixed-methods evaluation designs. Educational Evaluation and Policy Analysis, 11(3): 255-274.

As I recall, anchoring quantitative scales to qualitative anchoring points is always a good idea. What is the qualitative anchor for a "1"? What is a qualitative anchor for a "10"?, etc. A "1" without a qualitative anchor or a "10" without a qualitative anchor requires a big leap in faith that the scale satisfies "face validity"--not to mention the more challenging "construct validity." It might be helpful to review the American Psychological Association's definitions in this regard. My sense is that a lot of consumer satisfaction scales are not properly used in inferential statistical analyses.  Analysts often do not report the characteristics of the measures they use. Are we talking about nominal, ordinal, interval, or ratio scales? See: http://graphpad.com/support/faqid/1089/ I typically ignore as unreliable reports that do not make known these important distinctions. But then, again, I'm "old school."

I'm saying this while not knowing the details of your study; but essentially, the quantitative part of your study might ask consumers about the features of a product or service via a pre-determined questionnaire; while the qualitative part might ask more open questions about how consumers FEEL about using the product or service.

The qualitative complements the quantitative.

@ Prof Adams, Mr. Noble and Prof. Morgan.

My PhD research is about the consumer resistance to innovation specifically in the passenger cars. Attempting to see the effect of 5 various barriers identified by Ram (1989) (namely Usage, Value, Risk, Image and Tradition) on consumers who own passenger cars. Why are they not using the innovations embedded in the car. The innovation here are related to engine, infotainment, aesthetics, confort and safety.

Questionnaire is my basic instrument to collect the data, however, I have plans to do some interviews to extract some hidden psychological factors which I assume may not be possible with the questionnaire.

Azhar,

I inadvertently deleted my previous response to your question. It is good to know the concepts that you are trying to operationalize in your study. Are your quantitative scales nominal, ordinal, interval, or ratio? Does the Dam framework that you are using provide scales with known metrics to operationalize its concepts? My suggestion is to use qualitative anchoring points along the range of numerical values of your scales. Qualitative anchoring points describe the "meaning" to be ascribed to the numerical values of quantitative scales. These qualitative anchoring points can be obtained simply by using q-sort methodology from a convenience sample of judges or by using the more complex Delphi process with a convenience sample of judges to elicit information. I'm inclined toward the use of q-sort methodology as more cost-effective than the Delphi process. See: http://betterevaluation.org/en/evaluation-options/qmethodology and http://comm.eval.org/HigherLogic/System/DownloadDocumentFile.ashx?DocumentFileKey=693904e1-2d45-4f3f-b4af-ac19a8786445&forceDialog=0. Your budget constraint may help you to decide.

Thank you Mr. Noble. I will furthur read these mothodologies and attempt to inculcate the same in my data collection. However, I am still in the process of designing my questionnaire hence have not completely decided what my scales will be. I have seen some related topics where the scales used are Ordinal which I have not yet decided.

code the qualitative factors in to quantitative levels as ,1,2,3 or a,b, c and then enter into the selected soft ware of data analysis and get the final analysis with relavent values or

 give a value system of numbering on to all the data and information after classification of the text into scientific systematc method

It will be any risk to convert qualitative data into quantitative data. The two types of data have very difference manner. But if you insist to do so, you can group or classify the qualitative data by looking its similarity, and then rank them according to your hypothesis from the most favorable to the least. You can then put score on it. I hope this will help to solve your problem

Best wishes,

Haryanto

Thank you. I have learnt a lot from the feedback.

Thanks for the insights. Very helpful.