1. Analysing and Interpreting Data Chapter 11

2. O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.2 ‘All meanings, we know, depend on the key of interpretation.’ -George Eliot

3. O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.3 Effective Data Analysis Effective data analysis involves  keeping your eye on the main game  managing your data  engaging in the actual process of quantitative and / or qualitative analysis  presenting your data  drawing meaningful and logical conclusions

4. O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.4 The Big Picture Analysis should be approached as a critical, reflective, and iterative process that cycles between data and an overarching research framework that keeps the big picture in mind

5. O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.5 Managing Data Regardless of data type, managing your data involves  familiarizing yourself with appropriate software  developing a data management system  systematically organizing and screening your data  entering the data into a program  and finally ‘cleaning’ your data

6. O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.6 Statistics Being able to do statistics no longer means being able to work with formula It’s much more important for researchers to be familiar with the language and logic of statistics, and be competent in the use of statistical software

7. O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.7 Data Types Different data types demand discrete treatment, so it’s important to be able to distinguish variables by  cause and effect (dependent or independent)  measurement scales (nominal, ordinal, interval, and ratio)

8. O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.8 Descriptive Statistics Descriptive statistics are used to summarize the basic feature of a data set through  measures of central tendency (mean, mode, and median)  dispersion (range, quartiles, variance, and standard deviation)  distribution (skewness and kurtosis)

9. O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.9 Inferential Statistics Inferential statistics allow researchers to assess their ability to draw conclusions that extent beyond the immediate data, e.g.  if a sample represents the population  if there are differences between two or more groups  if there are changes over time  if there is a relationship between two or more variables

10. O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.10 Selecting Statistical Tests Selecting the right statistical test relies on  knowing the nature of your variables  their scale of measurement  their distribution shape  types of question you want to ask

11. O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.11 Presenting Quantitative Data Presenting quantitative data often involves the production of graphs and tables These need to be 1. selectively generated so that they make relevant arguments 2. informative yet simple, so that they aid reader’s understanding

12. O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.12 Qualitative Data Analysis (QDA) In qualitative data analysis there is a common reliance on words and images to draw out rich meaning But there is an amazing array of perspectives and techniques for conducting an investigation

13. O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.13 The QDA Process Qualitative data analysis creates new understandings by exploring and interpreting complex data from sources without the aid of quantification Data source include  interviews  group discussions  observation  journals  archival documents, etc

14. O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.14 Uncovering and Discovering Themes The methods and logic of qualitative data analysis involve uncovering and discovering themes that run through raw data, and interpreting the implication of those themes for research questions

15. O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.15 More on the QDA Process Qualitative data analysis generally involves  moving through cycles of inductive and deductive reasoning  thematic exploration (based on words, concepts, literary devises, and nonverbal cues)  exploration of the interconnections among themes Qualitative data analysis software can help with these tasks

16. O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.16 Specialist QDA Strategies There are a number of paradigm and discipline based strategies for qualitative data analysis including  content analysis  discourse analysis  narrative analysis  conversation analysis  semiotics  hermeneutics  grounded theory

17. O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.17 Presenting Qualitative Data Effective presentation of qualitative data can be a real challenge You’ll need to have a clear storyline, and selectively use your words and/or images to give weight to your story

18. O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.18 Drawing Conclusions Your findings and conclusions need to flow from analysis and show clear relevance to your overall project Findings should be considered in light of  significance  current research literature  limitations of the study  your questions, aims, objectives, and theory