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Analysing results

Research obviously generates data, which, depending on your study design and methods, ultimately turn into numbers or words (text, field notes, interview transcripts, etc.) or a combination of both.

Other than very small scale projects in which you can use packages such as Excel to manage your data, if your project generates numbers which need analysing, then you will need to use a computer-based statistical package to analyse your results. There are many packages available and it is a good idea to see what your university or healthcare organisation supports. One of the most commonly used packages is SPSS™ (Statistical Package for the Social Sciences) which provides all the statistical analysis you are likely to need and takes you through how to enter your data, name your variables, etc., with a good help website. There are also numerous books on the market and courses with details on how to use SPSS and the various statistical tests; your organisation may well have a course available or you might be able to discuss your project with a statistician who can guide you.

Data gathered through qualitative methods tend to be more unwieldy and time-consuming to analyse than numerical results, and methods need to be devised to analyse (e.g. through textual analysis) and code the data. Computer packages such as Nvivo™, ATLAS.ti™ and QSR-NUD*IST™ are available to assist with the analysis and coding of qualitative data such as interview transcripts, video recordings and focus group recordings. Care must be taken when using computer-based packages as much of the data is by its very nature open to interpretation and therefore analysis usually involves a number of individuals who work together to identify themes, code them and cross-check the analysis. Pope, Ziebland and Mays (2000) discuss ways in which healthcare researchers can approach the analysis of qualitative data. They summarise the key points as follows:

  • ‘Qualitative research produces large amounts of textual data in the form of transcripts and observational field notes
  • The systematic and rigorous preparation and analysis of these data is time consuming and labour intensive
  • Data analysis often takes place alongside data collection to allow questions to be refined and new avenues of inquiry to develop
  • Textual data are typically explored inductively using content analysis to generate categories and explanations; software packages can help with analysis but should not be viewed as short cuts to rigorous and systematic analysis
  • High quality analysis of qualitative data depends on the skill, vision, and integrity of the researcher; it should not be left to the novice’

See Further Reading section for more information on data analysis.

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