creators: Bokayeva M.S. Tyulyugenova L

Technologies (ICT)

creators: Bokayeva M.S. Tyulyugenova L.

analysis
Theoretical frameworks: grounded theory,
distributed cognition, activity theory
Presenting the findings: rigorous

notations,
stories, summaries

analysis to use on a set of data and the

relevant forms of pictorial presentation or data display. The decision is based on the scale of measurement of the data. These scales are nominal, ordinal and numerical.

Nominal scale
A nominal scale is where:
the data can be classified numerical or named categories, and
the order in which these categories can be written or asked is arbitrary.

Ordinal scale
An ordinal scale is where:
the data can be classified into non-numerical or named categories
an inherent order exists among the response categories.
Ordinal scales are seen in questions that call for ratings of quality (for example, very good, good, fair, poor, very poor) and agreement (for example, strongly agree, agree, disagree, strongly disagree).

Numerical scale
A numerical scale is:
where numbers represent the possible response categories
there is a natural ranking of the categories zero on the scale has meaning
there is a quantifiable difference within categories and between consecutive categories.

through the testing of a hypothesis.
In qualitative research, you are

either exploring the application of a theory or model in a different
context or are hoping for a theory or a model to emerge from the data. In other words, although you may have some ideas about your topic, you are also looking for ideas, concepts and attitudes often from experts or practitioners in the field.

15 17
User
Number of errors made
Internet use
< once a

day
once a day once a week
2 or 3 times a week once a month

Number of errors made

10

8

6

4

2

0

0

5

15

20

10
User

Number of errors made

web page activity

and meaning to the mass of collected data. It is

a messy, ambiguous, time- consuming, creative, and fascinating process. It does not proceed in a linear fashion; it is not neat.
Qualitative data analysis is
a search for general statements about relationships among categories of data."
Marshall and Rossman, 1990:111

Hitchcock and Hughes take this one step further:

"…the ways in which the researcher moves from a description of what is the case to an explanation of why what is the case is the case."
Hitchcock and Hughes 1995:295

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. Can provide a good balance between richness and replicability.

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

packages, e.g. SPSS
Qualitative data analysis tools
Categorization and theme-based analysis, e.g.

N6
Quantitative analysis of text-based data

further insight
Three such frameworks are:
Grounded Theory
Distributed Cognition
Activity Theory

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

cognitive system
Used for analyzing collaborative work
Focuses on information propagation &

transformation

with 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

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