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IS360 Fall 2008 :: Blog :: Robson Part IV: Qualitative data

December 04, 2008

Robson Part IV: Qualitative data
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Robson uses the term “attractive nuisance” to advise against our falling into the trap of being so taken in by the advantages of qualitative data that we fail to consider its limitations and incidental problems.  Some of the caveats to be borne in mind are:

·         The researcher may only know roughly in advance what he/she is looking for. As such, it is difficult, especially for beginning researchers, to determine when enough data has been collected.

·         Because it usually reflects individuals’ interpretation of events, the qualitative research of which it is a part is less able to be generalized than quantitative research.

·         The research problem, not our aversion or liking for a particular research method, should be the overriding determinant for the type of data, collection methods, and research design we adopt.

·         The fact that qualitative designs are not fixed but emerge as the study unfolds is a good reason to be well prepared when undertaking such research.

·         Being well prepared includes the mental readiness to rigorously analyze decisions points reached and succinctly explain conclusions made.

·         Because qualitative data comes in the form of words, pictures, or objects, qualitative studies can generate huge amounts of data, often requiring a lot of care to manage.

Robson also gives an in-depth discussion on the use of the NUD*IST software for analyzing the large amounts of qualitative data necessarily generated in the course large scale studies such as a doctoral dissertation might entail. But one requires having the software installed in order to fully understand this discussion. I know we have SPSS installed in the computers at the lab. Does anyone know what the situation is regarding NUD*IST, NVivo, or any qualitative research software at that? 

Posted by IS360 Fall 2008 - Anaga Ojo


Comments

  1. I am also wondering about NUD*IST. I used SPSS last year so I am familiar to SPSS but NUD*IST is unknown tool for me. After reading this book I realized I need to learn this tool in the near future.

    Yoonmi LeeYoonmi Lee on Thursday, 04 December 2008, 10:52 Pacific Standard Time # |

  2. Thanks, Anaga, of the nice summary of Robson’s cautions in contemplating qualitative research design. Attempting qualitative research is much more ‘unchartered’ and difficult than attempting quantitative research design where comparatively, there are many more established roadmaps for the novice researcher to follow.

    Doris ShimabukuroDoris Shimabukuro on Thursday, 04 December 2008, 11:22 Pacific Standard Time # |

  3. Yes, it is more difficult for beginner research to do qualitative research. Data in form of words or non-numeric is not easy to interpret when compare with numeric data. I'm also curious how to use NUD*IST to analyze those kind of research.

    Pimpaka PrasertsilpPimpaka Prasertsilp on Thursday, 04 December 2008, 18:18 Pacific Standard Time # |

  4. Qualitative design emerges over the time. The researcher may develop his/her ideas and methods during the study. while the quantitaive research where the researcher have pre-specifications for what he/she is doing and how he/she will do it. This issue also contributes to the fact that qualitative research is harder. The qualitative research developes over time and this may push the researcher to change his analysis approach and also the researcher involvement which may also lead to many validity threats. Having a good quality qualitative research depends on having good invistigators.

    Shaimaa EwaisShaimaa Ewais on Friday, 05 December 2008, 15:16 Pacific Standard Time # |

  5. I think NUD*IST  is just a tool. The quality of the research is still up to the researcher.

    Mark YoungMark Young on Monday, 08 December 2008, 11:30 Pacific Standard Time # |

  6. NUDIST or the current version NVIVO helps identify patterns in ones data.

    Sam OjoSam Ojo on Friday, 12 December 2008, 15:31 Pacific Standard Time # |

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