Data Analysis


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Data Analysis

  1. 1. Analysing Data Level 3 Independent Study
  2. 2. Today’s Session <ul><li>Today we will try to answer the following questions: </li></ul><ul><ul><li>What kinds of data are we dealing with? </li></ul></ul><ul><ul><li>How do we find meanings in the raw data we collect? </li></ul></ul><ul><ul><li>How do we display/represent those meanings effectively? </li></ul></ul><ul><ul><li>How do we interpret these meanings and use them to answer our initial research questions and/or satisfy the aims of our study? </li></ul></ul><ul><ul><li>Can we identify a process of data analysis and interpretation which we could usefully adopt? </li></ul></ul>
  3. 3. Today’s Session <ul><li>This is an opportunity to refocus and in particular to: </li></ul><ul><ul><li>Appreciate how closely the collection and analysis of data is interconnected </li></ul></ul><ul><ul><li>Understand the connection between methods of analysis and our overarching methodological leanings </li></ul></ul><ul><ul><li>Understand the range of appropriate methods available for quantitative and qualitative data analysis </li></ul></ul>
  4. 4. Refocus <ul><li>Can you articulate answers to the following: </li></ul><ul><ul><li>What is/are your research question/s? </li></ul></ul><ul><ul><li>What are your methodological leanings? </li></ul></ul><ul><ul><li>What procedures/strategies/methods have you adopted for collecting the data which will inform your study? </li></ul></ul><ul><ul><li>Have those procedures/strategies/methods been influenced by your proposed analytical methods? </li></ul></ul><ul><ul><li>How will you represent that data? </li></ul></ul>
  5. 5. Preparing for analysis <ul><li>Make sure your data is in an easily accessible form. This will depend on the type of study you are conducting: </li></ul><ul><ul><li>Fixed design studies give more expected data sets – classically surveys and experiments </li></ul></ul><ul><ul><li>Flexible designs evolve from early data collection and more moveable research questions – classically action research, case studies, ethnographic research, grounded theory studies </li></ul></ul>
  6. 6. Quantitative data analysis <ul><li>Nearly all fixed design studies yield statistical data and there are invariably some numerical data in all research projects. </li></ul><ul><ul><li>Advantages – there are rules for the analysis of numerical data with software to facilitate that analysis </li></ul></ul><ul><ul><li>Disadvantages – it can be complex and there is a steep learning curve </li></ul></ul><ul><li>No matter what the data your priority is to summarise and display in an effective way. </li></ul>
  7. 7. Different types of categorical data <ul><li>Data that fall within different categories </li></ul><ul><ul><li>Tick the relevant box indicating whether you are male of female </li></ul></ul><ul><ul><li>Often treated as coded data (female – ‘1’; male ‘0’) </li></ul></ul><ul><li>Data that fall into one of several different categories </li></ul><ul><ul><li>What is your marital status: tick the relevant box </li></ul></ul><ul><ul><ul><li>Married  widowed | divorced | separated | never married </li></ul></ul></ul><ul><li>Data which fall into one of several different ordered categories </li></ul><ul><ul><li>Which category of degree did you obtain: tick the relevant box </li></ul></ul><ul><ul><ul><li>First | Upper Second | Lower Second | Third | Unclassified </li></ul></ul></ul><ul><ul><li>How do you rate your experience of the course so far: circle the one that most corressponds </li></ul></ul><ul><ul><ul><li>Excellent | Very Good | Good | Satisfactory | Poor | Very Poor </li></ul></ul></ul>
  8. 8. Summarising & displaying <ul><li>Tables and bar charts </li></ul><ul><ul><li>many eyes </li></ul></ul><ul><ul><li>periodic table of visualisation </li></ul></ul><ul><li>Figures </li></ul><ul><li>Using Excel to generate displays </li></ul><ul><li>Using SPSS to give the results of different statistical tests </li></ul>
  9. 9. Different types of continuous or measured data <ul><li>Data that can take on any value </li></ul><ul><ul><li>How old are you? </li></ul></ul><ul><ul><ul><li>___ years </li></ul></ul></ul><ul><ul><li>What is your date of birth </li></ul></ul><ul><ul><ul><li>dd / mm / yy </li></ul></ul></ul><ul><ul><li>What age group are you? </li></ul></ul><ul><ul><ul><li>Under 20 | 20-29 | 30-39 | 40-49 | 50-59 | 60 or above </li></ul></ul></ul>
  10. 10. Different types of continuous or measured data <ul><li>Summary statistics </li></ul><ul><li>Simple averages/arithmetic mean </li></ul><ul><li>Differences in variability </li></ul><ul><li>Differences in distribution </li></ul><ul><li>Beware SPSS – see your supervisor for guidance </li></ul><ul><li>NB research questions often come down to asking whether there are differences between things or whether there are relationships between them. But then you have to interpret these differences/relationships: why do they exist and what significance do they have for answering your initial research question. </li></ul>
  11. 11. Qualitative data analysis <ul><li>Words, words, words </li></ul><ul><li>Interviews, field notes, documents, images, photographs,video etc etc. </li></ul><ul><li>[there can be a place for the quantitative analysis of such data] </li></ul><ul><li>How do we do justice to the richness and compexity of such data? </li></ul>
  12. 12. Qualitative data analysis <ul><li>Vast amount of literature on the area </li></ul><ul><ul><li>See the bibliography on webct & the bookmarks on the unit blog </li></ul></ul><ul><li>A good starting point is: </li></ul><ul><ul><li>http:// </li></ul></ul><ul><li>General pointers to a complex area: </li></ul><ul><ul><li>Reduce and organise </li></ul></ul><ul><ul><li>Edit </li></ul></ul><ul><ul><li>Summarise </li></ul></ul><ul><ul><li>Code </li></ul></ul><ul><ul><li>Note </li></ul></ul><ul><ul><li>Conceptualise </li></ul></ul><ul><ul><li>Display </li></ul></ul><ul><ul><li>Interpret </li></ul></ul>
  13. 13. Qualitative data analysis <ul><li>Dedicated software packages </li></ul><ul><ul><li>Transana </li></ul></ul><ul><ul><li> </li></ul></ul><ul><ul><li>NVivo </li></ul></ul><ul><ul><li>Express Scribe </li></ul></ul><ul><li>As with SPSS, beware!! Ask your supervisor if you think your work may be facilitated by the use of such software </li></ul>
  14. 14. Refocus <ul><li>What data sets are you working with/will be working with in the very near future? </li></ul><ul><ul><ul><li>How might you analyse this data? </li></ul></ul></ul>
  15. 15. Process <ul><li>When is it most appropriate to start data analysis? </li></ul><ul><li>Immersion in/Intimacy with the data </li></ul><ul><li>Interrogating the data </li></ul><ul><li>Emerging understandings </li></ul><ul><ul><li>Categories/Codes/Patterns </li></ul></ul><ul><ul><li>Exceptions/Absences </li></ul></ul><ul><ul><li>Evidence! </li></ul></ul><ul><li>Iteration/Triangulation </li></ul>
  16. 16. Summary <ul><li>Description  Analysis  Interpretation  </li></ul><ul><li>Synthesis  Representation </li></ul>Reflexivity Reflexivity Reflexivity Reflexivity
  17. 17. Future Sessions <ul><li>Report Writing </li></ul><ul><li>Student-led poster sessions </li></ul><ul><li>See the calendar in webct for dates/rooms </li></ul>