This document discusses different approaches to analyzing qualitative and quantitative data from research. It addresses questions like what types of data are common, how to find meanings and patterns, and how to display results effectively. The document provides an overview of quantitative data analysis methods like statistical tests and summarizing data in tables and charts. It also discusses qualitative data analysis, including reducing and organizing text data, coding, conceptualizing, and interpreting meanings. The goal is to help researchers choose appropriate analysis methods based on their research questions, methodological approach, and type of data collected.

1. Analysing Data Level 3 Independent Study

2. 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?

3. 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

4. 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?

5. 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

6. 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.

7. 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

8. 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

9. 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

10. 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.

11. 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?

12. 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

13. 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

14. Refocus What data sets are you working with/will be working with in the very near future? How might you analyse this data?

15. 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

16. Summary Description  Analysis  Interpretation  Synthesis  Representation Reflexivity Reflexivity Reflexivity Reflexivity

17. Future Sessions Report Writing Student-led poster sessions See the calendar in webct for dates/rooms