Have you come across this tendency as well? Do you believe in the supremacy and higher academic viability of quantitative research over qualitative research design ? please share your reasons and reflections. Thank you very much.
Sorry I came across this question late:
I am a mixed-method fan, and sometimes I will use one or the other, or both. My own bias towards quantitative research is what I perceive as being a matter of their 'scientificness'. We have latched onto 'science', be it true, applied, natural, social etc. Laboratories and people who use complex data often manage it quantitatively. Not only is this practical, but you can explore the data to determine relationships etc. Lots of qualitative work involves smaller numbers of subjects, and also phenomena like feelings, value, opinion etc. One is not 'better' than the other - they are just different. The problem (IMO) is that whilst it is good to know how to explore and express big data ... the skills of literary argumentation are less focused on. For an example, if look at the elements of a taught PhD, you will often see that the 'P' has retreated into the background and been superseded by 'Sc'.
If you can reduce something down to a final, meaningful number that shows your point, then it makes you feel quite secure in your finding, and more confident in your ability to 'defend' your assertion. It is (depending on your view of statistics etc.) often more clear, and easier to confidently express than trying to use words alone. Qualitative data can easily become vague if unsupported by other corroborating data. Things like interview responses can appear isolated or opinion based, unless they are tied to wider research & contexts, & you 'prove' your point. IMO, the data is no less valid, but it is hard to express confidently, and in a way that withstands scrutiny.
It is sometimes funny to see when people with perfectly good qualitative data do not link it convincingly, but rather put the 10 numbers they have into SPSS, press the two-tailed regression-based multivariate button and say that because 6/10 people thought X (0.031), then their result was 'statistically significant' and has strong implications for future research .... now that's the power of numbers ;-)
The following link doesn't directly address your question, but the discussions may provide some relating thoughts. Here's the link:
http://www.researchgate.net/post/What_methodology_do_you_prefer_Qualitative_or_Quantitative_Why
Sorry I came across this question late:
I am a mixed-method fan, and sometimes I will use one or the other, or both. My own bias towards quantitative research is what I perceive as being a matter of their 'scientificness'. We have latched onto 'science', be it true, applied, natural, social etc. Laboratories and people who use complex data often manage it quantitatively. Not only is this practical, but you can explore the data to determine relationships etc. Lots of qualitative work involves smaller numbers of subjects, and also phenomena like feelings, value, opinion etc. One is not 'better' than the other - they are just different. The problem (IMO) is that whilst it is good to know how to explore and express big data ... the skills of literary argumentation are less focused on. For an example, if look at the elements of a taught PhD, you will often see that the 'P' has retreated into the background and been superseded by 'Sc'.
If you can reduce something down to a final, meaningful number that shows your point, then it makes you feel quite secure in your finding, and more confident in your ability to 'defend' your assertion. It is (depending on your view of statistics etc.) often more clear, and easier to confidently express than trying to use words alone. Qualitative data can easily become vague if unsupported by other corroborating data. Things like interview responses can appear isolated or opinion based, unless they are tied to wider research & contexts, & you 'prove' your point. IMO, the data is no less valid, but it is hard to express confidently, and in a way that withstands scrutiny.
It is sometimes funny to see when people with perfectly good qualitative data do not link it convincingly, but rather put the 10 numbers they have into SPSS, press the two-tailed regression-based multivariate button and say that because 6/10 people thought X (0.031), then their result was 'statistically significant' and has strong implications for future research .... now that's the power of numbers ;-)
- Badges
- Science topic
- Similar topics
- Philosophy
- Philosophy Of Science
- Reasoning