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DATA ANALYSIS, INTERPRETATION
AND PRESENTATION
OVERVIEW
 Qualitative and quantitative
 Simple quantitative analysis
 Simple qualitative analysis
 Tools to support data analysis
 Theoretical frameworks: grounded theory,
distributed cognition, activity theory
 Presenting the findings: rigorous notations,
stories, summaries
WHY DO WE ANALYZE DATA
The purpose of analysing data is to obtain usable and useful
information. The analysis, irrespective of whether the data is
qualitative or quantitative, may:
• describe and summarise the data
• identify relationships between variables
• compare variables
• identify the difference between variables
• forecast outcomes
The Research specifically DataAnalysis.pptx
SCALES OF MEASUREMENT
into a non-
Many people are confused about what type of
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.
When using a quantitative methodology, you are normally testing theory 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.
The Research specifically DataAnalysis.pptx
The Research specifically DataAnalysis.pptx
The Research specifically DataAnalysis.pptx
The Research specifically DataAnalysis.pptx
The Research specifically DataAnalysis.pptx
The Research specifically DataAnalysis.pptx
The Research specifically DataAnalysis.pptx
The Research specifically DataAnalysis.pptx
GRAPHICAL REPRESENTATIONS
give overview of data
Number of errors made
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
1 3 5 7 9 11 13 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
Visualizing log data
Interaction
profiles of players
in online game
Log of web page
activity
QUALITATIVE ANALYSIS
"Data analysis is the process
of bringing order, structure
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
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. Can provide a good balance
between richness and replicability.
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
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
SUM MARY
• The data analysis that can be done depends on the
data gathering that was done
• Qualitative and quantitative data may be gathered
from any of the three main data gathering approaches
• Percentages and averages are commonly used in
Interaction Design
• Mean, median and mode are different kinds of
‘average’ and can have very different answers for the
same set of data
• Grounded Theory, Distributed Cognition and Activity
Theory are theoretical frameworks to support data
analysis
• Presentation of the findings should not overstate the
evidence
The Research specifically DataAnalysis.pptx

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The Research specifically DataAnalysis.pptx

  • 2. OVERVIEW  Qualitative and quantitative  Simple quantitative analysis  Simple qualitative analysis  Tools to support data analysis  Theoretical frameworks: grounded theory, distributed cognition, activity theory  Presenting the findings: rigorous notations, stories, summaries
  • 3. WHY DO WE ANALYZE DATA The purpose of analysing data is to obtain usable and useful information. The analysis, irrespective of whether the data is qualitative or quantitative, may: • describe and summarise the data • identify relationships between variables • compare variables • identify the difference between variables • forecast outcomes
  • 5. SCALES OF MEASUREMENT into a non- Many people are confused about what type of 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.
  • 6. When using a quantitative methodology, you are normally testing theory 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. GRAPHICAL REPRESENTATIONS give overview of data Number of errors made 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 1 3 5 7 9 11 13 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
  • 16. Visualizing log data Interaction profiles of players in online game Log of web page activity
  • 17. QUALITATIVE ANALYSIS "Data analysis is the process of bringing order, structure 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
  • 18. 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. Can provide a good balance between richness and replicability.
  • 19. 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
  • 20. 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
  • 21. SUM MARY • The data analysis that can be done depends on the data gathering that was done • Qualitative and quantitative data may be gathered from any of the three main data gathering approaches • Percentages and averages are commonly used in Interaction Design • Mean, median and mode are different kinds of ‘average’ and can have very different answers for the same set of data • Grounded Theory, Distributed Cognition and Activity Theory are theoretical frameworks to support data analysis • Presentation of the findings should not overstate the evidence