Tuesday, March 31, 2009

Why frequentist statistical approches still win in analysis of market research data?

I recently read part two of Ray Kent's article 'Rethinking Data Analysis-Some alternatives to frequentist approaches' in the latest issue of the International Journal of Market Research(Vol. 51 Issue 2). The article makes a case for looking at alternatives such as bayesian statistics, configurational and fuzzy set analysis, association rules in data mining, neural network analysis, chaos theory and the theory of the tipping point when data does not meet the requirements of frequentist approaches.

My point of view on the article is :
  1. As someone who works in this field, it is annoying to be constantly told about limitations of frequentist methods that I am aware of.
  2. The reasons for lack of adoption of newer more appropriate techniques in market research are more basic than researcher knowledge(or lack of in this case), challenges in presenting results or client adoption.

Here are some reasons why a lot of market researchers continue to rely primarily on frequentist approaches:

  1. Most researchers are not statisticians thus find it hard to understand and apply complex newer techniques. In fact most market research companies don't have an adequate number of statisticians on board.
  2. Companies need to put money, research and time behind these techniques in order to sell them to clients(we see this trend among data analysis software companies like SAS, Sawtooth software, Latent Gold etc). Without this, it is difficult for lone researchers to push newer ways of analysing data to clients.
  3. Researchers prefer to be 'shown' and not 'told' how these new techniques are applicable to their industry. A lot more collaboration is needed among academicians and practicing researchers to apply these techniques to relevant data in order to see the merits. Trying to replicate results of published articles in real time still falls under 'exploratory research not paid for by the client'.
  4. While it makes sense to argue for an approach that looks at data using a variety of techniques, in reality researchers are pushed for time and looking at various alternatives is very hard.

It feels good to get that off my chest...


Mike said...

I've been really enjoying your work here, insightful and enjoyable.

Anuradha said...

Thanks Mike.