ITEC 3230 Lecture Notes - Activity Theory, Percentile, Data Analysis

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Published on 17 Apr 2013
School
York University
Department
Information Technology
Course
ITEC 3230
Chapter 8:
Data analysis, interpretation and
presentation
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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
studies.
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
manner.
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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
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Document Summary

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.

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