1. Data Management and Analysis
What in the world am I going to do with all this data?
O'Leary, Z. (2004) The Essential Guide to Doing Research. London: Sage Chapter Twelve

2. Reflective Analysis
While computer programs can facilitate analysis, it is the researcher who needs to strategically, creatively, and intuitively analyse their data
O'Leary, Z. (2004) The Essential Guide to Doing Research. London: Sage Chapter Twelve

3. Keeping an overall sense of the project
Analysis should be approached as a critical, reflexive, and iterative process that cycles between data and an overarching research framework
It is crucial for researchers to keep a keen sense of the overall project and think their way through analysis
O'Leary, Z. (2004) The Essential Guide to Doing Research. London: Sage Chapter Twelve

4. Managing the Data Managing data involves:
familiarizing yourself with appropriate software
developing a data management system
systematically organizing the data
conducting a preliminary screening
and entering the data into a computer program
O'Leary, Z. (2004) The Essential Guide to Doing Research. London: Sage Chapter Twelve

5. Crunching the Numbers
Being able to do statistics no longer means being able to work with formula
It is much more important for student researchers to be familiar with the language and logic of statistics, and be competent in the use of statistical software
O'Leary, Z. (2004) The Essential Guide to Doing Research. London: Sage Chapter Twelve

6. 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)
and measurement scales (nominal, ordinal, interval, and ratio)
O'Leary, Z. (2004) The Essential Guide to Doing Research. London: Sage Chapter Twelve

7. Descriptive Statistics
Descriptive statistics are used to summarize the basic feature of a data:
measures of central tendency (mode, median, and mean)
dispersion (range, quartiles, variance, and standard deviation)
and distribution (skewness and kurtosis)
O'Leary, Z. (2004) The Essential Guide to Doing Research. London: Sage Chapter Twelve

8. Shape of the Data
O'Leary, Z. (2004) The Essential Guide to Doing Research. London: Sage Chapter Twelve

9. Inferential Statistics
Inferential statistics allow researchers to assess their ability to draw conclusions that extend beyond the immediate data, i.e.)
if a sample represents the population
if there are differences between two or more groups
if there are changes over time
or if there is a relationship between two or more variables
O'Leary, Z. (2004) The Essential Guide to Doing Research. London: Sage Chapter Twelve

10. Selecting the right statistical test
Selecting the right statistical test relies on knowing:
the nature of your variables
their scale of measurement
their distribution shape
and the types of question you want to ask
O'Leary, Z. (2004) The Essential Guide to Doing Research. London: Sage Chapter Twelve

11. Working with Words
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 on the precise focus of, and techniques for, conducting analysis
O'Leary, Z. (2004) The Essential Guide to Doing Research. London: Sage Chapter Twelve

12. Logic and Methods
The methods and logic of qualitative data analysis involve:
uncovering and discovering themes that run through the raw data
and interpreting the implication of those themes for research questions
O'Leary, Z. (2004) The Essential Guide to Doing Research. London: Sage Chapter Twelve

13. Funnelling Towards Meaning
O'Leary, Z. (2004) The Essential Guide to Doing Research. London: Sage Chapter Twelve

14. Cycles of Reasoning Qualitative data analysis often involves:
moving through cycles of inductive and deductive reasoning
thematic exploration (based on word, concepts, literary devices, and nonverbal cues)
and exploration of the interconnections among themes
O'Leary, Z. (2004) The Essential Guide to Doing Research. London: Sage Chapter Twelve

15. Qualitative Data Analysis Strategies
There are a number of paradigm/discipline based strategies for qualitative data analysis including:
overarching methodologies
content, discourse, narrative, and conversation analysis
semiotics
hermeneutics
and grounded theory
O'Leary, Z. (2004) The Essential Guide to Doing Research. London: Sage Chapter Twelve

16. 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
and finally your questions, aims, objectives, and theory
O'Leary, Z. (2004) The Essential Guide to Doing Research. London: Sage Chapter Twelve