Data Analysis, Interpretation and Presentation

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Presentation transcript:

Data Analysis, Interpretation and Presentation

Aims Discuss the difference between qualitative and quantitative data and analysis. Enable you to analyze data gathered from: Questionnaires. Interviews. Observation studies. Make you aware of software packages that are available to help your analysis. Identify common pitfalls in data analysis, interpretation, and presentation. Enable you to interpret and present your findings in appropriate ways. www.id-book.com

Quantitative and qualitative Quantitative data – expressed as numbers Qualitative data – difficult to measure sensibly as numbers, e.g. count number of words to measure dissatisfaction Quantitative analysis – numerical methods to ascertain size, magnitude, amount Qualitative analysis – expresses the nature of elements and is represented as themes, patterns, stories Be careful how you manipulate data and numbers! www.id-book.com

Simple quantitative analysis Measures of Central Tendency Mean: add up values and divide by number of data points Median: middle value of data when ranked Mode: figure that appears most often in the data Measures of Variation Standard Deviation Range and Interquartile range 95% Confidence Interval Percentages Be careful not to mislead with numbers! Graphical representations give overview of data www.id-book.com 4

Visualizing log data Interaction profiles of players in online game www.id-book.com

Visualizing log data Log of web page activity www.id-book.com

Web analytics www.id-book.com

Simple qualitative analysis Recurring patterns or themes Emergent from data, dependent on observation framework if used Categorizing data Categorization scheme may be emergent or pre-specified Looking for critical incidents Helps to focus in on key events www.id-book.com

Tools to support data analysis Spreadsheet – simple to use, basic graphs Statistical packages, e.g. SPSS Qualitative data analysis tools Categorization and theme-based analysis Quantitative analysis of text-based data Nvivo and Atlas.ti support qualitative data analysis CAQDAS Networking Project, based at the University of Surrey (http://caqdas.soc.surrey.ac.uk/) www.id-book.com

Theoretical frameworks for qualitative analysis Basing data analysis around theoretical frameworks provides further insight Three such frameworks are: Grounded Theory Distributed Cognition Activity Theory www.id-book.com

Grounded Theory Aims to derive theory from systematic analysis of data Based on categorization approach (called here ‘coding’) Three levels of ‘coding’ Open: identify categories Axial: flesh out and link to subcategories Selective: form theoretical scheme Researchers are encouraged to draw on own theoretical backgrounds to inform analysis www.id-book.com

Code book used in grounded theory analysis www.id-book.com

Excerpt showing axial coding www.id-book.com

Distributed Cognition The people, environment & artefacts are regarded as one cognitive system Used for analyzing collaborative work Focuses on information propagation & transformation www.id-book.com

Activity Theory Explains human behaviour in terms of our practical activity in the world Provides a framework that focuses analysis around the concept of an ‘activity’ and helps to identify tensions between the different elements of the system Two key models: one outlines what constitutes an ‘activity’; one models the mediating role of artifacts www.id-book.com

Individual model www.id-book.com 16

Engeström’s (1999) activity system model P312 Figure 8.17 Engeström’s (1999) activity system model. The tool element is sometimes referred to as the mediating artifact Source: Reproduced from Engeström, Y. (1999) Perspectives on Activity Theory, CUP. www.id-book.com 17

Presenting the findings Only make claims that your data can support The best way to present your findings depends on the audience, the purpose, and the data gathering and analysis undertaken Graphical representations (as discussed above) may be appropriate for presentation Other techniques are: Rigorous notations, e.g. UML Using stories, e.g. to create scenarios Summarizing the findings www.id-book.com