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

1. Kaedah
Menganalisis
Data
Prof. Dr. Md. Nasir Ibrahim
Post-doctoral, Sheffield Hallam University, UK
PhD, University of Tasmania, Australia
MA, McGill University, Canada
B.A. (Hons.), USM, Penang

2. Objectives
1
2
3
2

3. 3

4. D a t a
A n a l y s i s
is…………….
Definition
a process used by researchers for
reducing data to a story and interpreting
it to derive insights. The data analysis
process helps in reducing a large chunk
of data into smaller fragments, which
makes sense.
-LeCompte & Schensul (2013)
Prof. Dr. Nasir | UPSI

5. Widely used in the
natural and social
s c i e n c e s : b i o l o g y,
chemistry, psychology,
economics, sociology,
m a r k e t i n g , e t c .
PURPOSE
Find patterns and
averages, make
predictions, test causal
relationships, and
generalize results

6. The Purpose
6
Data analysis helps to make sense of our data otherwise they will remain a
pile of unwieldy information; perhaps a pile of figures.
To answer the research
questions and to help
determine the trends
and relationships
among the variables.

7. Characteristics of Quantitative and Qualitative
Research Process
Analysing and
Interpreting Data
Report and Evaluate
Collecting Data
Specifying a Purpose
Reviewing the Literatures
Identifying a Problem
Quantitative Characteristics Qualitative Characteristics
•Descriptive/Explanatory
Exploratory/Understanding a
Phenomena
•Major Role Justify Problem •Major Role Explore Problem
•Specific and Narrow
•Measurable/Observable
•General and Broad
•Participants’ Experience
•Pre-determined, Instruments
•Numeric Data, Large numbers
•General, emerging form Text or image data
•Small Number
•Statistical, Description of Trends
•Comparisons/Predictions
•Text Analysis, Description and Themes
•Larger Meanings of Findings
•Standard and Fixed
•Objective and Unbiased
•Flexible and Emerging
•Reflexive and Biased

8. Understanding
Quantitative
Research
Analysis

9. 9
Two types
Descriptive
Analysis
Inferential
• Collecting, summarising and describing
data.
• Numerical values obtained from the
sample that gives meaning to the data
collected.
• Use percentage, mean, median, mode,
range and Standard Deviation.
• Drawing conclusions based only on sample
data beyond the immediate data.
• E.g. Use t-test (difference between the
means of two independent groups),
ANOVA (to test the significance of
differences between means of two or
more groups).

10. Descriptive
10
You own a restaurant and want to know how your business is being
perceived by people. So, you give one of your staff, Amy, the
responsibility of carrying out a small survey of your customers to
see how they feel. Amy goes head on to collect information from
every client who visits your restaurant. At the end of the week, Amy
comes to you with a giant grin on her face and hands to you a long
list of numbers that contain how many points each client has given
your business. You stare down at this soup of numbers and scratch
your head as you have no idea what to make of it! You’re probably
considering firing Amy at the moment, right?
An example of real case

11. 11
Well, Amy’s efforts weren’t exactly in vain, but what she
should have done instead of just handing you a bunch of
numbers was to represent these numbers in a more
meaningful way so that you could make a proper inference
from them. This is where statistical analysis comes into
play.
An example of real case

12. Descriptive
12
Amy got hold of about 100 customer feedbacks. Say your
customers filled out a survey form where they scored your
restaurant on a scale of 1 to 10. She could have organized
these scores into a chart or graph to let you pictorially deduce
how many of your customers think your food rocks. Assuming
75 of them gave you more than 5 points, while 25 of them gave
you less than 5 points. It would serve you better if Amy had
created a Pie chart that clearly indicated what percentage of
your clients/customers love you. In this case, you could easily
see that 75% percent of your customers like you, so you’re right
on track!
An example of real case

13. Descriptive Analysis
13
75%
Pie Chart
25%

14. Descriptive
14
Amy could also have calculated an average of all the scores to give
you an idea of the general client sentiment. So, if most of the clients
scored you between 6 and 7, your average would come to around
6.5, showing that it’s not all the bad, but you could do better. There
are many other ways to visualize data or draw an inference from a
limited set of data. These include other measures of central
tendency like mean, median and mode as well as measures of
dispersion, like variance and standard deviation. These values can
give you an idea of how varied the opinions of your customers are.
Altogether all these measures and visualizations could give you a
pretty good idea about how your business is doing.
An example of real case

15. The Power of PowerPoint | thepopp.com15
Descriptive Analysis
Mean, Median, Mode, Variance & SD
Customer
S.No.
Score
1 5
2 7
3 6
4 7
5 7
6 7
7 7
8 6
9 6
10 7
11 8
12 6
13 5
14 7
15 7
16 6
17 6

16. How to calculate Mean?
16
Customer Score
1 5
2 7
3 6
4 7
5 7
6 7
7 7
8 6
9 6
10 7
11 8
12 6
13 5
14 7
15 7
16 6
17 6
JUMLAH 110
෍ 𝑥
=
N
Jumlah
Jumlah Skor
Bil. Responden
Mean
17

17. How to calculate Mean?
17
Statistical Analysis
Mean 6.470588
Median 7
Mode 7
Variance 0.639706
Standard
Deviation
0.799816
Customer
S.No.
Score
1 5
2 7
3 6
4 7
5 7
6 7
7 7
8 6
9 6
10 7
11 8
12 6
13 5
14 7
15 7
16 6
17 6
JUMLAH 110
110
=
17
Jumlah
Skor
17

18. The mean may not be a fair
representation of the data,
because the average is easily
influenced by outliers (very small
or large values in the data set
that are not typical).
18

19. median
19
The point at which there are an equal number of data points whose values lie above and below
the median value.
Truly the middle of the data set. It
is the central value of the variable
that divide the series into two
equal parts in such a way that half
of the items lie above the value
and the remaining half lie below
this value. 3.
The next time you hear
an average reported,
look to see whether the
median is also reported.
If not, ask for it!

20. How to calculate Median?
20
No. 0f Customers Score
1 5
2 7
3 6
4 7
5 7
6 7
7 7
8 6
9 6
10 7
11 8
12 6
13 5
14 7
15 7
16 6
17 6
JUMLAH 110
Susun Skor
secara
ascending
ataupun
descending
No. 0f Customers Score
1 5
2 5
3 6
4 6
5 6
6 6
7 6
8 6
9 7
10 7
11 7
12 7
13 7
14 7
15 7
16 7
17 8
JUMLAH 110

21. How to calculate Median?
21
Statistical Analysis
Mean 6.470588
Median 7
Mode 7
Variance 0.639706
Standard
Deviation
0.799816
No. of Customers Score
1 5
2 5
3 6
4 6
5 6
6 6
7 6
8 6
9 7
10 7
11 7
12 7
13 7
14 7
15 7
16 7
17 8
JUMLAH 110
(17+1)
M =
2
Median = (n + 1) / 2
17
= (18)
2
= 9

22. How to calculate Mode?
22
Statistical Analysis
Mean 6.470588
Median 7
Mode 7
Variance 0.639706
Standard
Deviation
0.799816
No. of Customers Score
1 5
2 5
3 6
4 6
5 6
6 6
7 6
8 6
9 7
10 7
11 7
12 7
13 7
14 7
15 7
16 7
17 8
JUMLAH 110
1. Write the numbers in
your data set.
2. Order the numbers from
smallest to largest
3. Count the number of
times each number is
repeated
4. Identify the value (or
values) that occur most
often. In our example
set, ({5, 5, 6, 6, 6, 6, 6,
6, 7, 7, 7, 7, 7, 7, 7, 7,
8}), because 7 occurs
more times than any
other value, 7 is the
mode.

23. Understanding
Qualitative
Research
Analysis

24. 24
“…to discover and describe issues in the
field or structures and processes in
routines and practices. Often, qualitative
data analysis combines approaches of a
rough analysis of the material (overviews,
condensation, summaries) with approaches
of a detailed analysis (elaboration of
categories, hermeneutic interpretations or
identified structures). The final aim is often
to arrive at generalizable statements by
comparing various materials or various
texts or several cases.”
Flick, U. (2014). The SAGE handbook of qualitative data analysis. SAGE. P. 5

25. 25
The analysis of qualitative data can have several aims.:=A
Subjective experiences of a specific individual or group. Can compare and contrast on the case/s (individual or
group) and its special features and the links between them.
Explaining such differences (e.g. circumstances which make it more likely students to learn drawing with
a specific situation more successful than other students).
Based on the phenomenon under study from the analysis of empirical material (e.g. a theory of art making), a
theory is developed.

26. 26
Three essential things take place:
Data Organization Data Reduction Data Interpretation
Method of classifying
and organising data
sets to make them
more useful.
Summarisation and
categorisation. It
helps in finding
patterns and themes
in the data for easy
identification and
linking.
Conducted in both top-
down or bottom-up
fashion to make
meaning.

27. 27
A step-by-step Guide
Organisation of the
collected data
Identification of
Framework
Second Order AnalysisDescriptive AnalysisSorting the Data
QDA is the range of processes and procedures whereby the data that have been collected is
explained based on the understanding or interpretation of the people and situations we are
investigating. QDA is usually based on an interpretative philosophy. The idea is to examine the
meaningful and symbolic content of qualitative data.
Transcribe the
interviews,
translate the
data, record the
details, label the
contents
The framework
is the coding
plan to
structure, label,
and define data
The data is
sorted based on
category and
theme
Describing the
data based on
research
questions
Identification
and
consolidation of
recurrent
themes,
patterns
present in the
data

28. 28
Five Type sof Qualitative Analysis
Descriptive – What’s the data?
Interpretative – What was meant by the data?
Refers to a cluster of analytic methods for deciphering texts
or visual data that have a storied kind.
Involves real text not invented, created and artificial text.
Five distinct phases: Familiarization, Identifying a thematic
framework, Coding, Charting, and Mapping; and Interpretation.
Analysis and development of theories happen when you
have collected the information.

29. What’s Content Analysis
(CA)
Content analysis is the study of documents
and artifacts, which might be texts of various
formats, paintings, pictures, audio or video.
Social scientists use content analysis to
examine patterns in communication in a
replicable and systematic manner.
29
Publisher: Routledge
Year: 1999Sage Publications, 2004
Dr. Klaus H. Krippendorff

30. 30
 Can be both quantitative (focused on
counting and measuring) and qualitative
(focused on interpreting and
understanding).
 In both types, you categorize or “code”
words, themes, and concepts within the
texts and then analyze the results.
Prof. Dr. Nasir | UPSI 30

31. How to conduct content analysis
Prof. Dr. Nasir | UPSI 31
5 STEPS:
Select the content
you will analyze
Choose the texts that you will analyze. If there are
only a small amount of texts that meet your
criteria, you might analyze all of them. If there is a
large volume of texts, you can select a sample.
1. Develop a set of
rules for coding
Organize the units of meaning into the previously
defined categories. it’s important to clearly define
the rules for what will and won’t be included to
ensure that all texts are coded consistently.
3. Analyze results and
draw conclusions
Find patterns and draw
conclusions in response to
your research question and
make inferences.
5.
Define the units
and categories of analysis
The unit(s) of meaning that will be coded. The set of
categories that you will use for coding. Categories
can be objective characteristics (e.g. female, aged
40-50, lawyer, mother) or more conceptual (e.g.
trustworthy, corrupt, conservative, family oriented).2.
Code the text
according to the rules
Go through each text and record all relevant data in the
appropriate categories. This can be done manually or aided
with computer programs, such as QSR NVivo, Atlas.ti and
Diction, which can help speed up the process of counting
and categorizing words and phrases.4.

32. Coding Qualitative Data:
How to Code Qualitative Research
• What is coding in qualitative research?
1. Coding is the process of labeling and organizing your qualitative data to
identify different themes and the relationships between them.
2. When coding, you assign labels to words or phrases that represent important
(and recurring) themes in each response.
3. These labels can be words, phrases, or numbers; we recommend using words
or short phrases, since they’re easier to remember, skim, and organize.
4. Thematic analysis is done to find common themes and concepts. Thematic
analysis extracts themes from text by analyzing the word and sentence
structure.
30

33. Types of Coding
33
Two Types of Coding
Deductive Coding
Deductive coding is also called concept-driven
coding. Start with a predefined set of codes.
These codes might come from previous
research, or you might already know what
themes you’re interested in analyzing.
Inductive Coding
Also called open coding, starts from scratch
and creates codes based on the qualitative
data itself. You don’t have a set codebook; all
codes arise directly from the responses.

34. Deductive Coding
34
 Deductive coding is a top down approach where you
start by developing a codebook with your initial set
of codes (pre-set coding schemes)
 This set could be based on your research questions
or an existing research framework or theory.
 Researcher’s setup the codes based on emerging
themes and define them according to the source
(e.g. literature review, support, etc.).
 Once the coding scheme is established, the
researcher applies the codes to the text.
What is deductive Coding?

35. Inductive Coding
35
 Involves the conversion of raw, qualitative
data into more useful quantitative data.
Unlike deductive analysis, inductive
research does not involve the testing of
pre-conceived hypotheses, instead
allowing the theory to emerge from the
content of the raw data.
What is Inductive Coding?

36. 36
What’s Narrative Inquiry
 Relatively new qualitative methodology.
 The study of experience understood narratively.
 Narrative inquirers think narratively about experience
throughout inquiry.
 Uses a recursive, reflexive process of moving from field (with
starting points in telling or living of stories) to field texts
(data) to interim and final research texts.
 Commonplaces of temporality, sociality, and place create a
conceptual framework within which different kinds of field
texts and different analyses can be used.
 Highlights ethical matters as well as shapes new theoretical
understandings of people’s experiences.
Title: Handbook of narrative
inquiry : mapping a
methodology
Author(s): D. Jean Clandinin
Publisher: Sage
Year: 2007

37. 37
What’s Narrative Analysis
(NA)
The social constructionist perspective is that all
‘narratives sit at the intersection of history,
biography, and society’ (Liamputtong and Ezzy
2005: 132); they are dependent on the context
of the teller and the listener; and are not
intended to represent ‘truth’.
Title: Handbook of narrative
inquiry : mapping a
methodology
Author(s): D. Jean Clandinin
Publisher: Sage
Year: 2007

38. How to analyse?
 Texts are analysed within their social, cultural, and historical
context.
 Deconstructed to search for themes and subthemes, in order
to build up a theory grounded in the data.
 Different researchers have their own style of narrative
analysis.
 The Foucauldian examines multiple voices and to draw out
which voices were silenced and which were powerful. ‘The
interpretation we call truth is the one that is attached to
power’ (Byrne-Armstrong 2001: 113).
38

39. Narrative Analysis
Prof. Dr. Nasir | UPSI 39
Step-by-Step
Biographical Details
looking for explanatory factors such
as age, gender, education,
experience, and so on.
01
Coding
Developing categories, themes and
sub-themes using participants’ own
language to describe each theme
03
Life History (Flick,
von Kardorff and
Steinke 2004)
Reducing and re-
ordering narratives;
writing up are
interwoven processes
05
Summarising Story
Summarise each participant’s story
without losing the meaning.
Capturing the significant ideas or
issues.02
Creating Metaphor
Highlighting ‘quotable quotes’, pulling
out one phrase to represent each
participant; Creating Metaphor
04

40. What’s Discourse Analysis
(DA)
A research method for studying written or spoken language
in relation to its social context. It aims to understand how
language is used in real life situations.
Discourse analysis is a common qualitative research
method in many humanities and social science disciplines,
including linguistics, sociology, anthropology, psychology
and cultural studies.
40
Publisher: Routledge
Year: 1999

41. 41
You make interpretations based on both the details of the material itself and on contextual knowledge.
Step 1: Define the research question and
select the content of analysis
Begin with a clearly defined research question.
Then, select a range of material that is appropriate
to answer it.
Step 3: Analyze the content for themes
and patterns
Closely examine various elements of the material –
such as words, sentences, paragraphs, and overall
structure – and relating them to attributes, themes,
and patterns relevant to your research question.
Step 2: Gather information and theory on the
context
Establish the social and historical context in which the
material was produced and intended to be received.
Understanding the real-life context of the discourse,
you can also conduct a literature review on the topic
and construct a theoretical framework to guide your
analysis.
Step 4: Review your results and draw
conclusions
Once you have assigned particular attributes to
elements of the material, reflect on your results to
examine the function and meaning of the language
used. Here, you will consider your analysis in relation
to the broader context that you established earlier to
draw conclusions that answer your research question.

42. An Example of
Discourse Analysis
42

43. What’s Grounded Theory
Analysis (GTA)
 A type of scientific research concerned with the
emerging concepts of social phenomena.
 It refers to situations where data collection is
conducted in an unstructured way (Joubish,
Khurram, Ahmed, Fatima, & Haider, 2011)
43
Publisher: Routledge
Year: 1999

44. 44
Three stages (Strauss & Corbin):
Open Coding Axial Coding Selective Coding
 Take your textual
data and break it
up into discrete
parts.
 In vivo
 Research denoted
 Breaking down of
core themes.
 The process of
relating codes
(categories and
concepts) to each
other, via a
combination of
inductive and
deductive
thinking.
 Selecting one
central category that
connects all the
codes from your
analysis and
captures the
essence of your
research.

45. How to do Open Coding?
Reading the interview transcript line-by-line
and interesting statements are marked
45
Statements which belong together are
summarized in one category
Characteristics and dimension of the
category are developed out of the data
Codebook with all categories and
subcategories also their characteristics and
dimension
Reorganisation of
Subcategories and
Summarisation of
Categories
Reading the other
transcripts line-by-line
Memo Writing

46. An example of interview transcript

47. An example of
Code Memo

48. An example of coding

49. Axial Coding
49
What’s Axial Coding?
Axial coding is a qualitative research technique that involves relating data together in order to
reveal codes, categories, and subcategories ground within participants’ voices within one’s collected
data. In other words, axial coding is one way to construct linkages between data.
Axial coding is the breaking
down of core themes
during qualitative data
analysis. Axial coding in
grounded theory is the
process of relating codes
(categories and concepts) to
each other, via a
combination of inductive and
deductive thinking.

50. Axial Coding
50
Example of Axial Coding
Axial coding. Axial coding is the breaking down of core themes during qualitative data analysis.
Axial coding in grounded theory is the process of relating codes (categories and concepts) to each
other, via a combination of inductive and deductive thinking.
• Describing relevant
excerpt selected
• Creating a code that
reflects the description
of the excerpt (with the
research question in
mind)

51. Selective Coding
51
Selective coding is the process of choosing one category to be the core category, and relating all other categories to
that category. The essential idea is to develop a single storyline around which all everything else is draped. There is a
belief that such a core concept always exists.
 Connect data to
discover patterns
 Core category
 More abstract
 More difficult part

52. P l a n n i n
M e t h o ds
Usin
Evaluation
Ground rules Dialo ue
C l a s s
M a n a g e m e n t Learning
C o n t r a c t s
How to
teach
M e t h o d s Questioning
W o r k- b a se d
rac t ic e
Lectures
30/10/20105 - w20
The issue
Form s of
asse ssme n t
Markin
A s s e s s m e n t
W hit eboard
Overhead
r o e c t o r s
Data
P r u c t i o n
S c r e e n - b a s e d Size
M e d i a
U s i n
m ent
Handouts
Hum our
Yourself
http://chks.wested.org/using_results/resilience

53. 53
Pengajaran Seni Berkualiti

54. Understanding
Visual Research
Analysis

55.  A visual analysis is used to
communicate how the aesthetic or
formal qualities of an image relate
to seemingly relevant ideas,
histories, narratives, politics,
cultures, affects, and/or
experiences. In other words, visual
analyses are used to show how
particular visuals create specific
effects and/or affects.
55

56. Visual analyses often involve a
combination of writing styles. This can
include observant, technical, emotive,
critical, reflective, and/or speculative
modes of writing about images. Visual
analyses can vary in length from a few
sentences, to a paragraph, to an entire
essay
56

57. • To create a coherent and clear
• interpretation of an image, it is
recommended that you do
three things:
• describe, analyse, and
interpret.
57

58. • descriptive, and visual language, tell the
reader what the image looks like.
• The subject matter and how it
• has been composed.
• Detail the formal and structural qualities of
the image (such as the tonal, linear, and
textural characteristics of the image). You
may also wish to describe how the image
has been made and exhibited.
• Consider the affective and experiential
qualities of the work;
• Consider step one in relation to the
image’s context and seemingly
associated concepts.
• Relate to specific theoretical framework.
• Provide the reader with a concluding
remark (take the image as their focus)
that clearly articulates the overall
impact – or, perhaps, ‘meaning’ – of
your selected image. Sometimes you
may arrive at multiple, even
conflicting, interpretations of a work.
58
How should you structure a
visual analysis of a an art
work?

60. Faculty of Art, Computing and Creative Industry,
Universiti Pendidikan Sultan Idris,
35900 Tanjong Malim, Perak,
MALAYSIA
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bistarian@mail.com +6011 3350 1941
60