ITEC 3230 Lecture Notes - Activity Theory, Percentile, Data Analysis
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Data analysis, interpretation and
Main aims of this chapter…
• Discuss the difference between qualitative and
quantitative data and analysis.
• Be able to analyze data gathered from
questionnaires, interviews, and observation
• Be aware of the kind of software packages
that are available to help your analysis.
• Identify some of the common pitfalls in data
analysis, interpretation, and presentation.
• Be able to interpret and present evaluation
findings in a meaningful and appropriate
Quantitative and qualitative
• Quantitative data – expressed as numbers
• Qualitative data – difficult to measure sensibly as
numbers, e.g. count number of words to measure
• Quantitative analysis – numerical methods to
ascertain size, magnitude, amount
• Qualitative analysis – expresses the nature of
elements and is represented as themes, patterns,
Quantitative (except for open-ended questions: observation. Filter into different data sets (e. g. per age) Demographics, time spend on task, # of people involved. Mean: add up values and divide by number of data points (be careful with that!) Median: middle value of data when ranked. Mode: figure that appears most often in the data: percentages, graphical representations give overview of data. Number of errors made e d a m s r o r r e f o r e b m u. 2 or 3 times a week once a month e d a m s r o r r e f o r e b m u. Boxplots are very good for comparing treatments/ designs! Simple qualitative analysis: unstructured - are not directed by a script. Rich but not replicable: structured - are tightly scripted, often like a questionnaire. Replicable but may lack richness: semi-structured - guided by a script but interesting issues can be explored in more depth.