1. DATA ANALYSIS –
USING
COMPUTERS
GROUP X

2. MEMBERS
Lim Pau Lin
Ziana binti Hj Mahmud
Hjh Normaslinah binti Hj Mohd Noor/Asri
Nur Adyani binti Hj Yusop
Nuwairani Azyyati binti Hj Sahidi
Abdul Wafi Sia bin Abdullah Sia

3. CONTENTS
 Types of software
 Functions
 Selection of software
 Research articles
 Validity and reliability issues
 References
 Questions

4. GENERAL TYPES OF SOFTWARE
 Word processors: designed for production and revision
of text and helpful for transcribing, writing up or editing
field notes, etc
 Word retrievers: Specialise in finding all of the
instances of words, phrases, and combinations of these
you are interested in locating. Some have content-
analytic capabilities
 Text based managers: Organise text more
systematically for search and retrieval. They search for
and retrieve various combinations of
words, phrases, coded segments, memos, or other
materials

5.  Code-and-retrieve programs: Software developed
specifically by qualitative researchers – helps divide
texts into segments or chunks, attach codes to the
chunks, and find and display all instances of coded
chunks (or combination of chunks)
 Theory builders: Also researcher-developed. Usually
include code-and-retrieve capabilities but also allow you
to make connections between codes, to develop higher-
order classifications and categories, to formulate
prepositions or assertions. Organised around a system
of rules or based on a formal logic.
 Conceptual network builders: Helps you to build and
test theory but you work with systematically-built graphic
networks. You can see your variables as nodes, linked
with other nodes by specific relationships. Networks are
not just casually hand-drawn but are real ‘semantic
networks’ that develop from your data and the
relationships you see among them.

6. WHAT FUNCTIONS TO LOOK FOR?
 Coding
 Memoing/annotation
 Data linking
 Search and retrieval
 Conceptual/theory development
 Data display
 Graphics editing
 Other things to consider: flexibility and user
friendliness

7. WHICH IS THE ‘BEST’ CAQDAS PACKAGE…?
 This is perhaps the most frequently asked question we receive
– however, it is impossible to answer! As such, they each have
their own advantages and disadvantages.
 Software packages do not provide you with a methodological
or analytic framework.
 The tools available may support certain tasks differently
 However, as the researcher you should remain in control of
the interpretive process and decide which of the available
tools within a software can facilitate your approach to analysis
most effectively.
 Thinking about and using CAQDAS software should not
necessarily be any different from other types of software
package – just because a tool or function is available to you
does not mean you will need or have to use it.
 You therefore need to think clearly about what it is you are
looking to the software to help you with. Do not choose a
package simply because it seems the most sophisticated.

8. SOME GENERAL QUESTIONS TO ASK WHEN CHOOSING A CAQDAS
PACKAGE
• What kind(s) and amount of data do you have, and
how do you want to handle it?
• What is your theoretical approach to analysis and
how well developed is it at the outset?
• Do you have a well defined methodology?
• Do you want a simple to use software which will
mainly help you manage your thinking and
thematic coding?
• Are you more concerned with the language, the
terminology used in the data, the comparison and
occurrence of words and phrases across cases or
between different variables ?

9. • Do you want both thematic and quantitative content
information from the data?
• Do you want a multiplicity of tools (not quite so
simple) enabling many ways of handling and
interrogating data?
• How much time do you have to ‘learn’ the software?
• How much analysis time has been built into the
project?
• Are you working individually on the project or as part
of a team?
• Is there a package – and peer support – already
available at your institution or place of work?

10. LIST OF CAQDAS SOFTWARE
 Atlas.ti  TAMS Analyser
 HyperRESEARCH  Transana
 MAXqda(&MAXdictio)  webQDA
 N6  Weft QDA
 Nvivo  & many more
 Qualrus
 QDA Miner Specialised software is not free.
 Code-A-Text Free demos are provided for 30-
 Dedoose days for some. Price range from
US$150 – US$1200 depending
 The Ethnograph
on academic, institutional or
 HyperResearch
professional users. Some require
 Kwalitan you to email them for pricing.
 Qualifiers

11. RESEARCH ARTICLES
Title Purpose Data
Analysis
Research 1 Simplifying Qualitative Show how MS Word can MS Word
Data Analysis Using be used for coding,
General Purpose retrieval and other
software tools qualitative analysis
functions
Research 2 Using Nvivo to Analyse To find out what NVivo
Qualitative Classroom successful teachers who
Data on Constructivist use CLE, especially
Learning Environments authentic learning, do in
the classroom and how
students behave in such
context.
Research 3 Teacher leadership in (in To illuminate the different Qualrus
action) ways in which teacher
leadership manifests itself
in schools

12. Title Purpose Data
Analysis
Research Understanding mothers’ Small scale study on NUD*IST
4 experiences of infant Australian mothers'
daycare: a new experiences of infant day
approach using care
computer-assisted
analysis of qualitative
data
Research Learning, Beliefs, and To find out the student QSR N6
5 Products: Students' perspective in project
Perspectives with based learning
Project-based Learning To explore how learners
created projects and how
they chose to complete
the learning tasks
Rssearch 6 The psychological To help facilitate the QSR N6
contract and job storage, management and
satisfaction: analysis of data, enabling
experiences of a group researchers to realise the
of casual workers. interpretive component of
the research.

13. SIMPLIFYING QUALITATIVE DATA ANALYSIS USING
GENERAL PURPOSE SOFTWARE TOOLS
Analyse text from key informant interviews, focus
groups, document reviews and open ended survey
questions.
Used word functions such as table, table sort, insert
file, find/replace, and insert comment

14. PROCESS FOR USING WORD FOR CODING AND
RETRIEVAL OF QUALITATIVE DATA
Step 1
• Formatting interview data into tables
Step 2
• Develop a theme codebook
Step 3
• Add columns and codes to capture face sheet data
Step 4
• Coding text rows with one or with multiple theme codes
Step 5
• Sorting data tables and finding patterns
Step 6 and • Code validation/correction and merging of data tables
7

15. STEP 1: FORMATTING INTERVIEW DATA INTO
TABLES

16. STEP 2: DEVELOP A THEME CODEBOOK

17. STEP 3: ADD COLUMNS AND CODES TO
CAPTURE FACE-SHEET DATA

18. STEP 4: CODING TEXT ROWS WITH ONE OR
WITH MULTIPLE THEME CODES

19. STEP 5: SORTING DATA TABLES AND FINDING
PATTERNS

20. STEP 6 AND 7:CODE VALIDATION/ CORRECTION AND
MERGING OF DATA TABLES
 Code validation within one interview data table
 Once sorted by theme code and sequence number, analyse
all text segment for each code and decide whether all
segments are instances of a particular category or if
corrections are needed.
 Merging data tables
 Data tables from all interviews or subsets of them appropriate
to one’s study can be merged (e.g. particular gender)
 Once merging has occurred, code validation actoss
transcripts can be done. To do this, sort by theme code, face-
sheet codes such as participant ID and sequence number
 Once merged and sorted, analyse all text segments for each
code and decide whether the text segments are all instances
of a particular category.

21. EXAMPLE OF USING COLOUR AND INSERT
COMMENT

23. RESEARCH USING NVIVO
 Purpose of the study:
 Sheds light on the ‘nuts and bolts’ of the qualitative data
analysis.
 Address a more open approach to reporting and help
researchers better understand how NVivo is used in an
actual classroom study.
 Research Questions
 Why are the ‘family characteristics’ of constructivist
learning environments in which authentic materials and
activities are using regularly?
 What are the students’ responses, in terms of their
social (interaction with other students and interaction
with the teacher), cognitive, emotional/affective, and
other learning behaviours – to these kinds of activities
and materials?

24.  Data collected through:
 Classroom Observations
 Formal and informal interviews (with both students and
teachers)
 Field Notes
 Students’ Products
 Artifacts.

25.  Why Nvivo?
 Structural design of the software (easy).
 Nature of the research study.
 Ease of the searching for relationships.
 Time saved.
 Rigor.

26. USING NVIVO IN THE ANALYSIS OF DIFFERENT
DATA SOURCES
 Interviews and Field Notes:
 The transcribed interview data and filed notes were transferred
into electronic formats in the early stages of the study. They were
only converted from a word format (.doc.extension) into rich text
file format (.rtf extension) in order to process them as NVivo
document files and use the NVivo’s rich text and visual coding
features. After completing these conversions all the interview files
as well as field note files were transferred into the NVivo
Document Browser.
 Observation
 Videotapes recorded in the real classrooms for the observation
purpose were transformed from visual and verbal expressions to
written text after encoding the transcripts.
 Students’ work
 Most of the student works were kept in student portfolio folders
and files. The written part of the student works was entered as
text files using document browser of NVivo, and ready for coding
and further analysis.

27. FINDINGS
(WITH REGARDS TO USING THE NVIVO FOR THE
STUDY)
 NVivo helps tremendously from conceptualization
and coding of the data to an entire research report
saving time and energy of researchers.
 Drawback – need time to explore the software and
your computer can crash or you can forget to save
your work.
 Expensive

28. DOCUMENT BROWSER

29. NODE BROWSER

30. MAIN AND SUB CODES IN NODE BROWSER

31. EXAMPLE OF CODING

33. USING CAQDAS :VALIDITY
To enhance validity
• Can assist the management of larger samples
• Given that a reliable and stable code is
applied, they offer facilities to retrieve all
information about a certain topic.
This increases the trustworthiness of qualitative
findings considerably because these facilities can
ensure that the hypotheses developed are really
grounded in the data and not based on a single and
highly untypical incidents.

34. ETHICAL ISSUES
Similar to how data is collected and analysed in
other instances:
 Anonymity of participants
 Confidentiality
 Make sure that the data is safe, password protected. If
storing data online, needs ethics clearance.
 Ensure that the data is rich in description but not
skewed into the direction where the researcher
wants it to be.

35. REFERENCES AND SOURCES
Fielding, N.G. and Lee, R.M (1998). Computer Analysis and
Qualitative Research. London: SAGE Publications.
Grant, M.M. (2011). Learning, Beliefs, and Products: Students'
Perspectives with Project-based Learning, Interdisciplinary
Journal of Problem-based Learning:Vol. 5(2), Article 6.
Kelle, U (1995). Computer-aided Qualitative Data Analysis: Theory,
Methods and Practice. London: SAGE Publications.
La Pelle, N. (2004). Simplifying qualitative data analysis using
general purpose software tools. Field Methods, Vol 16 (1), 85-
108
Miles, M.B. & Huberman, A.M. (1994). Qualitative Data Analysis.
California: SAGE Publications.
Ozkan, B.C. (2004). Using Nvivo to analyse qualitative classroom
data on constructivist learning environments. The Qualitative
Report, Vol 9 (4), 589-603.

36. QUESTIONS
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