Data Analysis
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Data Analysis

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Data Analysis Data Analysis Presentation Transcript

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