Why the qualitative approach is essential for every research project?

“Recently, it was conducted a global survey which sought to answer the following question: Please answer honestly. How in your opinion could be solved the problem of lack of food in many countries in the world?”

The survey was a failure because in Africa nobody knew what food means. In France nobody knew what honesty means. Nobody knew in Western Europe what having lack of something means. In China no one knew what is having your own opinion. In Arabs countries none knew what is solving a problem. In South America, nobody knew what the word “please” means. In North America, no one knew that other countries exist.”

The above parody is just that, a parody. However, it illustrates very well how the ambiguity of such terms as happiness, leadership or being modern are constantly challenging social researchers. “The more ambiguous and elastic our concepts, the less possible is to quantify our data in a meaningful way” (Dey, 1993) Can we measure happiness all over the world if the meaning of it may strongly vary from one country to another? It may be not possible assuming right away a quantitative approach. And it is precisely here where the qualitative one finds its place. Qualitative techniques may bring the not measurable concepts into the “realm” of the measurable. Indeed it is “an opportunity to explore a subject in as real manner as is possible” (Robson, 2002).

For instance, what is a great place to work? We can quantify work by the unemployment rate in every country, as well as places where people work just collecting information on the number of companies. But great place to work? We could actually build another funny story as above, or just try to understand what people think what a “great place to work” is. And this is precisely what Great Place to Work did to measure a prior ambiguous concept.

This company on a year basis publishes a best workplace ranking both nationwide and worldwide. To do so they built up a model formed by categories of analysis like trust, enjoy or pride, among others. In turn each of them is divided into indicators. These indicators give way to a questionnaire that is used in a survey among a set of companies in every country. As they report in their website, “this model has been confirmed through over 25 years´ worth of analysis of employees´ own opinions“.

Therefore, the quantification of a priori ambiguous concepts is preceded (or even accompanied) by a qualitative analysis. This analysis consists of doing a categorization of what people think it is a great place to work. If the result of a quantitative study is usually a graphic or a statistic table, the result of a qualitative study is based on categorizations.

Finally, although the categorization process is frequently used as way to identify measurable units referred to ambiguous concepts, as in the above example, it may be used independently, i.e. doing a categorization of happiness, for instance, may have as the only aim to obtain an accurate understanding of this concept, regardless it is going to be or not measured later.

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Reference

BBC Radio 4. Interview to Manuel Castells “Alternative ecomomic cultures”. Retrieved from http://www.bbc.co.uk/programmes/b01n9yg1
Dey, I. (1993). Qualitative data analysis: A user-friendly guide. Routledge. Seen in Lewis, Philip, Mark NK Saunders, and Adrian Thornhill. Research methods for business students. Pearson, 2009.
Lewis, Philip, Mark NK Saunders, and Adrian Thornhill. Research methods for business students. Pearson, 2009.
Marketing directo. C. Chaguaceda (Coca-Cola): “No se mide igual la felicidad que la venta de botellas de Coca-Cola” Retrieved from http://www.marketingdirecto.com/actualidad/gente/c-chaguaceda-coca-cola-no-se-mide-igual-la-felicidad-que-la-venta-de-botellas-de-coca-cola/
Robson, C. (2002). Real world research: a resource for social scientists and practitioner-researchers (Vol. 2). Oxford: Blackwell. Seen in Lewis, Philip, Mark NK Saunders, and Adrian Thornhill. Research methods for business students. Pearson, 2009.
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