Hi, just had supervision and am supervisor kept banging on about the need to be extremely reasoned as to why I am using a qualiative approach. My main thought was because I cant work SPSS (I did not share!) but beyond that obviously it because my primary concern is gaining an insight into subjective understanding of participants but I could not think beyond that...Any thoughts?

It really depends on your research question to be honest, as you should let your question guide your selection of methods, not whether you're rubbish at them or not! Have a good look at your research question and the best way to gather the information you'll need to answer that question. Don't fret if it turns out you need a more quantitative approach - your university should have a stats department that can offer you guidance and support.

Do you have your aims and objectives sorted yet? These should also help point you in the right direction.

This made me laugh! I can relate to your situation, I am rubbish at statistics myself.

Search in i Tunes for Berkeley Statistics with a guy named Fletcher Ibser. He is really really good in explaining basic concepts of statistics to get you started. Find SPSS courses already provided from the uni, or gather up with other PhD students and find someone to give you classes. I am pretty sure that many PhD students need to learn some statistical software.

Knowing some statistics can be useful in many occasions. Good luck, you can always mix qualitative with quantitative research

A few suggestions, but like Lindalou said, it'd depend on your research question:

Inductive approach is recommended when:
- the field is stagnant
- it needs new ideas/concepts
- there's much controversy on previous findings
- not previously done, so no insight from the point of view of those involved in the phenomena to be studied
- you could also potentially argue that none of the available models fully explains the phenomena and therefore you need number new ideas (no 2 above)

I can probably think of a few more if none of these apply but to be honest, I don't think you can justify doing qualitative solely based on not being to work on SPSS. Sorry as I'm sure this is not what you want to hear! It's just that if the methodology is not appropriate to the research question, they you're going to struggle when it comes to interpretation and relating findings to the theory plus the dreaded viva!

Don't they have courses you could attend? Or maybe you could go to some of the stats lectures for other courses? Sups also help with that!!

I agree with the others; it depends on your research question. Quantitative analysis can show relationships but it can't tell us -why- or give us perspective the way qualitative research can. If you're looking for causality, motivation, perceptions, and mulitdirectional/complex relationships, then qual is the way to go.

Qual research can be aggravating because it is so tied to philosophical and theoretical orientations, which can turn off a lot of people and confuse the hell out of many a phd student. Approaches can be phenomenology, ethnography, grounded theory among others. Each of the approaches and the whole menu of data collection options depends on what you want to find out and your perspective/attitude/angle on it.

If you're having a problem with your sup, your topic might lend itself to a mixed methods approach or case study where you can do the bulk of your work from a qual approach but collect some quant data to support it or add to your understanding of the topic. I'm sure your uni offers trainings on SPSS or you can find someone more skilled in it to run the analysis for you.

Best of luck!
-nic

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trust me, when you're half way through transcription and then in the lengthy process of analysis and interpretation, statistics seem far more appealing. All you have to do is read a chapter on whatever test you need to use and learn it - probably take you a week, rather than the months and months of agony you'll have to go through with qual.

I'm currenty writing up my qual findings so may be slightly biased ;-)