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Analysing and Interpreting Data . Chapter 11. ‘All meanings, we know, depend on the key of interpretation.’ -George Eliot. Effective Data Analysis. Effective data analysis involves keeping your eye on the main game managing your data

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Analysing and interpreting data

Analysing and Interpreting Data

Chapter 11


‘All meanings, we know, depend on the key of interpretation.’

-George Eliot

O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.


Effective data analysis
Effective Data Analysis interpretation.’

  • 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

O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.


The big picture
The Big Picture interpretation.’

  • 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

O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.


Managing data
Managing Data interpretation.’

  • 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

O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.


Statistics
Statistics interpretation.’

  • 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

O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.


Data types
Data Types interpretation.’

  • 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)

O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.


Descriptive statistics
Descriptive Statistics interpretation.’

  • 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)

O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.


Inferential statistics
Inferential Statistics interpretation.’

  • 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

O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.


Selecting statistical tests
Selecting Statistical Tests interpretation.’

  • 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

O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.


Presenting quantitative data
Presenting Quantitative Data interpretation.’

  • Presenting quantitative data often involves the production of graphs and tables

  • These need to be

    • selectively generated so that they make relevant arguments

    • informative yet simple, so that they aid reader’s understanding

O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.


Qualitative data analysis qda
Qualitative Data Analysis (QDA) interpretation.’

  • 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

O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.


The qda process
The QDA Process interpretation.’

  • 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

O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.


Uncovering and discovering themes
Uncovering and Discovering Themes interpretation.’

  • 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

O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.


More on the qda process
More on the QDA Process interpretation.’

  • 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

O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.


Specialist qda strategies
Specialist QDA Strategies interpretation.’

  • 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

O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.


Presenting qualitative data
Presenting Qualitative Data interpretation.’

  • 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

O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.


Drawing conclusions
Drawing Conclusions interpretation.’

  • 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

O'Leary, Z. (2005) RESEARCHING REAL-WORLD PROBLEMS: A Guide to Methods of Inquiry. London: Sage. Chapter 11.


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