Levels of of measurement and, Qualitative data analysis

1. Quantitative andQuantitative and
Qualitative Data AnalysisQualitative Data Analysis
Dr. Jagannath.K.DangeDr. Jagannath.K.Dange
Department of EducationDepartment of Education
Kuvempu UniversityKuvempu University
Jnanasahyadri, ShankaraghattaJnanasahyadri, Shankaraghatta
Shivamogga-577451Shivamogga-577451
KarnatakaKarnataka

2. Quantitative ResearchQuantitative Research
Quantity is the unit of analysisQuantity is the unit of analysis
– AmountsAmounts
– FrequenciesFrequencies
– DegreesDegrees
– ValuesValues
– IntensityIntensity
Uses statistics for greater precision andUses statistics for greater precision and
objectivityobjectivity
Based on the deductive modelBased on the deductive model

3. Model for ConceptualizingModel for Conceptualizing
Quantitative ResearchQuantitative Research
Overall purpose orOverall purpose or
objectiveobjective
Research literatureResearch literature
Research questionsResearch questions
and hypothesesand hypotheses
Selecting appropriateSelecting appropriate
methodsmethods
Validity and reliabilityValidity and reliability
of the dataof the data

4. 44
Creating the FoundationCreating the Foundation
for Quantitative Researchfor Quantitative Research
ConceptConcept
– Abstract thinking to distinguish it from other elementsAbstract thinking to distinguish it from other elements
ConstructConstruct
– Theoretical definition of a concept; must beTheoretical definition of a concept; must be
observable or measurable; linked to other conceptsobservable or measurable; linked to other concepts
VariableVariable
– Presented in research questions and hypothesesPresented in research questions and hypotheses
OperationalizationOperationalization
– Specifically how the variable is observed or measuredSpecifically how the variable is observed or measured

5. 1. Exploratory -- It is a good starting point to get
familiarized with some insights and ideas (e.g. identify
the dependent and independent variables)
1. Descriptive – “The mapping out of a circumstance,
situation, or set of events”
1. Causal—experimenting (statistically speaking) to asses
cause and effect. For example, whether or not a Radio
program is achieving its objectives. Experiments in the
social science take place “naturally” (e.g. The
effectiveness of SES on the academic achievement)
Types of Quantitative ResearchTypes of Quantitative Research

6. 66

7. Measuring Variables
To establish relationships between
variables, researchers must observe the
variables and record their observations.
This requires that the variables be
measured.
The process of measuring a variable
requires a set of categories called a scale
of measurement and a process that
classifies each individual into one
category.
77

8. The Levels of MeasurementThe Levels of Measurement
NominalNominal
OrdinalOrdinal
IntervalInterval
RatioRatio

9. Four Basic Types Of
Measurement:
• Categorizing
-Nominal
• Ranking
– Ordinal
• Determination of the size interval
– Interval
• Determination of the size of ratios
– Ratio

10. Scales ofScales of Measurement
Nominal
Ordinal
Interval
Ratio
}} Qualitative
}} Quantitative

11. Nominal ScaleNominal Scale (discrete)(discrete)
 Simplest scale of measurement
 Variables which have no numerical value
 Variables which have categories
 Count number in each category, calculate
percentage
 A simple categorical variableA simple categorical variable is binary oris binary or
dichotomousdichotomous (1/0 or yes/no).(1/0 or yes/no).
 Useful for quantifying qualitative data
 Examples:
– Gender
– Race
– Marital status

12. 1212

13. Ordinal ScaleOrdinal Scale
 Variables are in categories, but but we can say thatbut we can say that
some categories are higher than others.some categories are higher than others.
Used to arrange data into series
 Rank-order categories from highest to lowest
 Intervals may not be equal- Distances between attributesDistances between attributes
do not have any meaning,do not have any meaning,
 Count number in each category, calculate
percentage
 Examples: 1st
2nd
3rd
4th
5th
– Likert scale 1.2.3.4.5
the distance from 0 to 1 is not same as 3 to 4the distance from 0 to 1 is not same as 3 to 4

14. 1414

15. IntervalInterval Scale
 Quantitative data
 Can add & subtract values
 Cannot multiply & divide values
– No true zero point
 Example:
– Temperature on a Celsius scale
• 00
indicates point when water will freeze, not an absence of
warmth

16. IntervalInterval
Variables of this type areVariables of this type are called scalar orcalled scalar or
index variablesindex variables in the sense they provide ain the sense they provide a
scale or indexscale or index that allows us to measurethat allows us to measure
between levels.between levels.
We can notWe can not only measure which is higher oronly measure which is higher or
lowerlower, but, but how much sohow much so..
– Distance is measured between points on a scale withDistance is measured between points on a scale with
even units.even units.
– Good example is temperature based on Fahrenheit orGood example is temperature based on Fahrenheit or
Celsius.Celsius.
WhenWhen distancedistance between attributes has meaning, forbetween attributes has meaning, for
example, temperature (in Fahrenheit) --example, temperature (in Fahrenheit) -- distancedistance
from 30-40 is same as distance from 70-80from 30-40 is same as distance from 70-80

17. 1717

18. 1818

19. Ratio ScaleRatio Scale ((continuous))
Quantitative data with true zero
– Can add, subtract, multiply & divide
Examples:
– Age
– Body weight
– Blood pressure
– Length of University stay

20. RatioRatio
RatioRatio:: Similar to interval levelSimilar to interval level
variables in that it canvariables in that it can measure themeasure the
distance between two pointsdistance between two points, but, but
can do so in absolute terms.can do so in absolute terms.
– For example, one can say thatFor example, one can say that
someone is twice as rich assomeone is twice as rich as
someone else based on thesomeone else based on the
value of their assetsvalue of their assets since tosince to
havehave no money is based on ano money is based on a
starting point of zero.starting point of zero.

22. 2222

23. Scales of Measurement
• Nominal: classification
• Ordinal: ranking
• Interval: equal intervals
• Ratio: absolute zero
Scale Classification Order Equal Intervals Zero
Nominal Yes No No No
Ordinal Yes Yes No No
Interval Yes Yes Yes No
Ratio Yes Yes Yes Yes

24. Measurement HierarchyMeasurement Hierarchy
NOMINAL
ORDINAL
INTERVAL
RATIO
WEAKEST
STRONGEST

25. 2525

26. The Hierarchy ofThe Hierarchy of
LevelsLevels
NominalNominalNominalNominal
IntervalIntervalIntervalInterval
RatioRatioRatioRatio
Attributes are only named; weakest
Attributes can be ordered
Distance is meaningful
Absolute zero
OrdinalOrdinalOrdinalOrdinal

27. Scales ofScales of Measurement
Nominal
Ordinal
Interval
Ratio
}} Lead to nonparametric
statistics
}} Lead to parametric statistics

28. Two Branches of StatisticsTwo Branches of Statistics
Descriptive
– Frequencies & percents
– Measures of the middle
– Measures of variation
Inferential
– Nonparametric statistics
– Parametric statistics

29. Qualitative ResearchQualitative Research
““AA form of social inquiryform of social inquiry thatthat focuses on thefocuses on the
way people interpret and make sense ofway people interpret and make sense of
their experiencestheir experiences and the world in whichand the world in which
they live.”they live.”

30. Qualitative ResearchQualitative Research
““: Qualitative data analysis is the array of processes and
procedures whereby a researcher provides explanations,
understanding and interpretations of the phenomenon under
study on the basis of meaningful and symbolic content of
qualitative data.
It provides ways of discrimnating, examining, comparing and
contrasting and interpreting meaningful patterns and themes. It
is based on the interpretative philosophy.
Qualitative data are subjective, soft, rich and in-depth
descriptions usually presented in the form of words. The most
common forms of obtaining qualitative data include semi-
structured and unstructured interviews, observations, life
histories and documents. The process of analysing is difficult
rigorous. .”.”

31. The Nature of Qualitative
Research
• The term qualitative research refers to
studies that investigate the quality of
relationships, activities, or situations.
• Qualitative data are collected in the form
of words or pictures and seldom involve
numbers.

32. What is Qualitative Data Analysis?
•Qualitative Data Analysis (QDA) is the
range of processes and procedures
whereby we move from the
qualitative data that have been
collected into some form of
explanation, understanding or
interpretation of the people and
situations we are investigating.

33. Qualitative/QuantitativeQualitative/Quantitative
DifferencesDifferences
The aim is a detailedThe aim is a detailed
description.description.
Researcher may only knowResearcher may only know
roughly in advance what he/sheroughly in advance what he/she
is looking for.is looking for.
The design emerges as theThe design emerges as the
study unfolds.study unfolds.
Researcher is the dataResearcher is the data
gathering instrument.gathering instrument.
Data is in the form of words,Data is in the form of words,
pictures or objects.pictures or objects.
SubjectiveSubjective - individuals’- individuals’
interpretation of events isinterpretation of events is
importantimportant
Qualitative data is more 'rich',Qualitative data is more 'rich',
time consuming, andtime consuming, and notnot
generalizable.generalizable.
Researcher tends to becomeResearcher tends to become
subjectively immersed in thesubjectively immersed in the
subject matter.subject matter.
The aim is to classify features,The aim is to classify features,
count them, and constructcount them, and construct
statistical models in an attemptstatistical models in an attempt
to explain what is observed.to explain what is observed.
Researcher knows clearly inResearcher knows clearly in
advance what he/she is lookingadvance what he/she is looking
for.for.
All aspects of the study areAll aspects of the study are
carefully designed before data iscarefully designed before data is
collected.collected.
Researcher questionnaires orResearcher questionnaires or
equipment to collect numericalequipment to collect numerical
data.data.
Data is numerical in nature.Data is numerical in nature.
ObjectiveObjective – seeks measurement– seeks measurement
& analysis of target concepts.& analysis of target concepts.
Quantitative data is moreQuantitative data is more
efficient, able to test hypotheses.efficient, able to test hypotheses.
Researcher tends to remainResearcher tends to remain
separated from the subjectseparated from the subject
matter.matter.

34. Qualitative and Quantitative ApproachesQualitative and Quantitative Approaches
Qualitative Quantitative
(Usually) Non-probability based
sample
Typically a probability-based
sample
Non-generalizable Generalizable
Answers Why? How? Answers How many? When?
Where?
Formative, earlier phases Tests hypotheses, latter phases
Data are “rich” and time-
consuming to analyze
Data are more efficient, but may
miss contextual detail
Design may emerge as study
unfolds
Design decided in advance
Researcher IS the instrument Various tools, instruments
employed

35. Case StudyCase Study
Attempts to shed light on a phenomena byAttempts to shed light on a phenomena by studying in depth astudying in depth a
single case example of the phenomenasingle case example of the phenomena. The case can be an. The case can be an
individual person, an event, a group, or an institution.individual person, an event, a group, or an institution.
GroundedGrounded
TheoryTheory
TheoryTheory is developed inductively from a corpus of datais developed inductively from a corpus of data acquired by aacquired by a
participant-observer.participant-observer.
PhenomenologPhenomenolog
yy
Describes the structures of experience as they present themselves toDescribes the structures of experience as they present themselves to
consciousness, without recourse to theoryconsciousness, without recourse to theory, deduction, or assumptions, deduction, or assumptions
from other disciplinesfrom other disciplines
EthnographyEthnography
Focuses on the sociology of meaning throughFocuses on the sociology of meaning through close field observationclose field observation
of sociocultural phenomenaof sociocultural phenomena. Typically, the ethnographer focuses on a. Typically, the ethnographer focuses on a
community.community.
HistoricalHistorical
Systematic collection and objective evaluation of data related to pastSystematic collection and objective evaluation of data related to past
occurrences in order to test hypotheses concerning causes, effects,occurrences in order to test hypotheses concerning causes, effects,
or trends of these events that may help to explain present eventsor trends of these events that may help to explain present events
and anticipate future events.and anticipate future events.
Main Types of QualitativeMain Types of Qualitative
ResearchResearch

36. There are three main methods of dataThere are three main methods of data
collection:collection:
1. Interactive1. Interactive
interviewinginterviewing
People asked to verbally described their experiences ofPeople asked to verbally described their experiences of
phenomenon.phenomenon.
2. Written descriptions2. Written descriptions
by participantsby participants
People asked to write descriptions of theirPeople asked to write descriptions of their
experiences of phenomenon.experiences of phenomenon.
3. Observation3. Observation Descriptive observations of verbal and non-verbalDescriptive observations of verbal and non-verbal
behavior.behavior.
Analysis begins when the data is first collected and is used to guide decisionsAnalysis begins when the data is first collected and is used to guide decisions
related to further data collection.related to further data collection.
"In communicating--or generating--the data, the researcher must make the process"In communicating--or generating--the data, the researcher must make the process
of the study accessible and write descriptively so tacit knowledge may best beof the study accessible and write descriptively so tacit knowledge may best be
communicated through the use of rich, thick descriptions" (Myers, 2002).communicated through the use of rich, thick descriptions" (Myers, 2002).

37. Qualitative Data AnalysisQualitative Data Analysis
The following areThe following are the componentsthe components of qualitativeof qualitative
Data analysis:Data analysis:
A.A.Data Reduction :Data Reduction : "Data reduction refers to the"Data reduction refers to the
process of selecting, focusing, simplifying,process of selecting, focusing, simplifying,
abstracting, and transforming the dataabstracting, and transforming the data that appearthat appear
in written up field notes or transcriptions."in written up field notes or transcriptions."
The data collected should beThe data collected should be reduced in terms ofreduced in terms of
meaningful termsmeaningful terms. All the information collected. All the information collected
should not be presentedshould not be presented

38. Qualitative Data AnalysisQualitative Data Analysis
B.B. Data Display :Data Display : Data display provides "Data display provides "an organized,an organized,
compressed assembly of information that permits conclusioncompressed assembly of information that permits conclusion
drawingdrawing..." A display can be an extended piece of text or a..." A display can be an extended piece of text or a
diagram, chart or matrix that provides a newdiagram, chart or matrix that provides a new way of arrangingway of arranging
and thinking about the more textually embedded data.and thinking about the more textually embedded data.
Data display can be extremely helpful in identifying whether aData display can be extremely helpful in identifying whether a
system is working effectively and how to change it.system is working effectively and how to change it.
Data could beData could be displayed using a series of flow charts that mapdisplayed using a series of flow charts that map
out any critical paths, decision pointsout any critical paths, decision points, and supporting evidence, and supporting evidence
that emerge from establishing the data for each site. Thethat emerge from establishing the data for each site. The
researcher may (1) use the data from subsequent sites toresearcher may (1) use the data from subsequent sites to
modify the original flow chart of the first site, (2) prepare anmodify the original flow chart of the first site, (2) prepare an
independent flow chart for each site; and/or (3) prepare a singleindependent flow chart for each site; and/or (3) prepare a single
flow chart for some events (if most sites adopted a genericflow chart for some events (if most sites adopted a generic
approach) and multiple flow charts for others.approach) and multiple flow charts for others.

39. Qualitative Data AnalysisQualitative Data Analysis
C.C. Conclusion Drawing and Verification :Conclusion Drawing and Verification : ConclusionConclusion
drawing requires a researcher to begin to decide whatdrawing requires a researcher to begin to decide what
things mean. He does this by noting regularities, patternsthings mean. He does this by noting regularities, patterns
((differences/similaritiesdifferences/similarities),), explanations, possibleexplanations, possible
configurations, causal flows, and propositionsconfigurations, causal flows, and propositions. This. This
process involves stepping back to consider what theprocess involves stepping back to consider what the
analysed data mean and to assess their implications foranalysed data mean and to assess their implications for
the questions at hand. Verification, integrally linked tothe questions at hand. Verification, integrally linked to
conclusion drawingconclusion drawing, entails revisiting the data as many, entails revisiting the data as many
times as necessary to cross-check or verify thesetimes as necessary to cross-check or verify these
emergent conclusions. Miles and Huberman assert thatemergent conclusions. Miles and Huberman assert that
"The meanings emerging from the data have to be tested"The meanings emerging from the data have to be tested
for their plausibility, their sturdiness, their ‗confirmability‘ -for their plausibility, their sturdiness, their ‗confirmability‘ -
that is, their validity".that is, their validity".

40. Strategies of Qualitative Data AnalysisStrategies of Qualitative Data Analysis
A. Analytical Induction :A. Analytical Induction :
1.1. Analytic induction is aAnalytic induction is a way of building explanationsway of building explanations inin
qualitative analysis byqualitative analysis by constructing and testing a set ofconstructing and testing a set of
causal links between events, actionscausal links between events, actions etc.etc.
2.2. It is research logic used to collect, develop analysis andIt is research logic used to collect, develop analysis and
organise the presentation of research findings.organise the presentation of research findings.
3.3. It refers to a systematic and exhaustive examination ofIt refers to a systematic and exhaustive examination of
a limited number of cases in order to providea limited number of cases in order to provide
generalisations and identify similarities between variousgeneralisations and identify similarities between various
social phenomena in order to develop contacts orsocial phenomena in order to develop contacts or
ideas.ideas.
4.4. Its formal objective is causal explanation.Its formal objective is causal explanation.

41. Strategies of Qualitative Data AnalysisStrategies of Qualitative Data Analysis
B. Constant ComparisonB. Constant Comparison It includesIt includes approaching yourapproaching your
data with an open mind to identify note worthy patternsdata with an open mind to identify note worthy patterns
. This requires that every time you select a passage of text. This requires that every time you select a passage of text
(or its equivalent in video etc.) and code it, you should(or its equivalent in video etc.) and code it, you should
compare it with all those passages you have alreadycompare it with all those passages you have already
codedcoded that way, perhaps in other cases.that way, perhaps in other cases.
Newly gathered data are continually compared withNewly gathered data are continually compared with
previously collected data and their coding in order topreviously collected data and their coding in order to
refine the development of theoretical categories.refine the development of theoretical categories.
The purpose is to test emerging ideas that might take theThe purpose is to test emerging ideas that might take the
research in new and fruitful directions.research in new and fruitful directions.

42. Strategies of Qualitative Data AnalysisStrategies of Qualitative Data Analysis
these include:these include:
a.a. Word repetitionsWord repetitions : Look for commonly used words and words: Look for commonly used words and words
whose closewhose close repetition may indicated emotionsrepetition may indicated emotions
b.b. Indigenous categoriesIndigenous categories : It refers to: It refers to terms used byterms used by
respondents with a particular meaningrespondents with a particular meaning and significance in theirand significance in their
setting.setting.
c.c. Key-words-in-contextKey-words-in-context : Look for the range: Look for the range of uses of keyof uses of key
termsterms in the phrases and sentences in which they occur.in the phrases and sentences in which they occur.
d.d. Compare and contrastCompare and contrast : It is essentially the: It is essentially the grounded theorygrounded theory
idea of constant comparisonidea of constant comparison. Ask, ‗what is this about?‘ and. Ask, ‗what is this about?‘ and
‗how does it differ from the preceding or following‗how does it differ from the preceding or following
statements?‘statements?‘
e.e. Social science queriesSocial science queries : Introduce: Introduce social sciencesocial science
explanations and theoriesexplanations and theories, for example, to explain the, for example, to explain the
conditions, actions, interaction and consequences ofconditions, actions, interaction and consequences of
phenomena.phenomena.

43. Strategies of Qualitative Data AnalysisStrategies of Qualitative Data Analysis
ff .. Searching for missing informationSearching for missing information : It is essential to: It is essential to try totry to
get an idea of what is not being done or talked outget an idea of what is not being done or talked out, but, but whichwhich
you would have expectedyou would have expected to find.to find.
g.g. Metaphors and analogiesMetaphors and analogies : People: People often use metaphor tooften use metaphor to
indicate somethingindicate something about their key, central beliefs about thingsabout their key, central beliefs about things
andand these may indicate the way they feel about thingsthese may indicate the way they feel about things too.too.
h.h. TransitionsTransitions : One of the discursive elements in speech which: One of the discursive elements in speech which
includes turn-taking in conversation as well as theincludes turn-taking in conversation as well as the more poeticmore poetic
and narrative use of story structuresand narrative use of story structures..
i.i. ConnectorsConnectors : It refers to: It refers to connections between termsconnections between terms such assuch as
causalcausal (‗since‘, ‗because‘, ‗as‘ etc) or logical (‗implies‘,(‗since‘, ‗because‘, ‗as‘ etc) or logical (‗implies‘,
‗means‘, ‗is one of‘ etc.)‗means‘, ‗is one of‘ etc.)

44. Strategies of Qualitative Data AnalysisStrategies of Qualitative Data Analysis
j.j. Unmarked textUnmarked text :: Examine the text that has not beenExamine the text that has not been
codedcoded at a theme or even not at all.at a theme or even not at all.
k.k. Pawing (i.e. handling)Pawing (i.e. handling) : It refers to: It refers to marking the text andmarking the text and
eyeballing or scanning the text. Circle words, underline,eyeballing or scanning the text. Circle words, underline,
use coloured highlighters, run coloureduse coloured highlighters, run coloured lines down thelines down the
margins to indicate different meanings and coding. Thenmargins to indicate different meanings and coding. Then
look for patterns and significances.look for patterns and significances.
l.l. Cutting and sortingCutting and sorting : It refers to the traditional technique: It refers to the traditional technique
of cutting up transcripts andof cutting up transcripts and collecting all those codedcollecting all those coded
the same way into piles,the same way into piles, envelopes or folders or pastingenvelopes or folders or pasting
them onto cards. Laying out all these scraps and re-them onto cards. Laying out all these scraps and re-
reading them, together, is an essential part of thereading them, together, is an essential part of the
process of analysis.process of analysis.

45. Strategies of Qualitative Data AnalysisStrategies of Qualitative Data Analysis
C. Triangulation :C. Triangulation : According to Berg and Berg, triangulation is aAccording to Berg and Berg, triangulation is a
term originally associated with surveying activities, map making,term originally associated with surveying activities, map making,
navigation and military practices.navigation and military practices.
The word triangulation was first used in the social sciences asThe word triangulation was first used in the social sciences as
metaphor describing a form of multiple operationalisationmetaphor describing a form of multiple operationalisation oror
convergent validation.convergent validation.
Campbell and Fiske were the first to apply the navigational termCampbell and Fiske were the first to apply the navigational term
triangulation to research. They used the term triangulation totriangulation to research. They used the term triangulation to
describe multiple data collection strategies for measuring adescribe multiple data collection strategies for measuring a
single conceptsingle concept. This is known as data triangulation. According to. This is known as data triangulation. According to
them,them, triangulation is a powerful way of demonstratingtriangulation is a powerful way of demonstrating
concurrent validityconcurrent validity, particularly in qualitative research., particularly in qualitative research.
Triangulation is an approach to research thatTriangulation is an approach to research that uses a combination ofuses a combination of
more than one research strategy in a single investigationmore than one research strategy in a single investigation..

46. Coding
• A coding system tells how to distinguish the content from the
medium.
• Sections of text transcripts may be marked by the researcher
in various ways (underlining in a colored pen, given a
numerical reference, or bracketed with a textual code).
• This section contains data which the researcher is interested
in exploring and analysing further.
• In the early stages of analysis, most if not all sections of the
text will be marked and given different ‘codes’ depending on
their content.
• As the analysis progresses these codes will be refined or
combined to form themes or categories of issues.

47. Develop coding categories
• A major step in analyzing qualitative data is
coding speech into meaningful categories,
enabling you to organize large amounts of text
and discover patterns that would be difficult to
detect by just reading observer commentary.
• Always keep the original copy of observer
commentary.

48. Develop coding categories
(Conti…)
• Next, conduct initial coding by generating
numerous category codes as you read
commentary, labeling data that are related
without worrying about the variety of
categories.
• Write notes to yourself, listing ideas or
diagramming relationships you notice. Because
codes are not always mutually exclusive, a
phrase or section might be assigned several
codes.

49. Develop coding categories
(Conti…)
• Last, use focused coding to eliminate, combine,
or subdivide coding categories and look for
repeating ideas and larger themes that connect
codes.
• Repeating ideas are the same idea expressed by
different respondents, while a theme is a larger
topic that organizes or connects a group of
repeating ideas.

50. Organizing Data for analysis

51. Developing your codes
• Coding is a process for categorizing your
data. Develop a set of codes using both
codes that you predefine and ones that
emerge from the data.
• Predefined codes are categories and
themes that you expect to see based on
your prior knowledge.

52. Coding your data
• Closely review and code your data. If possible,
have more than one person code the data to
allow for different perspectives on the data.
• As you proceed you may find that your initial
codes are too broad. Create subcategories of
your codes as needed. Or you may find that you
have created codes that are too detailed and
that attempt to capture every possible idea. In
that case consider how you can pull categories
together into a broader idea.

53. Coding your data (Conti…)
• Coding is a process of reducing the data into
smaller groupings so they are more manageable.
• The process also helps you to begin to see
relationships between these categories and
patterns of interaction.

54. Finding themes, patterns, and
relationships
•Step back from the detailed work of
coding your data and look for the
themes, patterns, and relationships
that are emerging across your data.
• Look for similarities and differences
in different sets of data and see what
different groups are saying.

55. Summarizing your data
• After you have coded a set of data, such as
transcripts of interviews with faculty or
questionnaire responses, write a summary of what
you are learning.
• Similarly, summarize the key themes that emerge
across a set of interview transcripts. When available,
include quotations that illustrate the themes.
• With your data coded and summarized you are
ready to look across the various summaries and
synthesize your findings across multiple data
sources.

56. The Coding Process
Initially read
through text
data Divide the text Label the segments
into segments of information Reduce overlap
of information with codes and redundancy Collapse codes
into themes

57. Themes
• A theme is generated when similar issues and
ideas expressed by participants within
qualitative data are brought together by the
researcher into a single category or cluster.
• This ‘theme’ may be labelled by a word or
expression taken directly from the data or by
one created by the researcher because it seems
to best characterise the essence of what is being
said.

58. Thank YouThank You
Dr. Jagannath K. Dange
Department of Education
Kuvempu University
Shankaraghatta
Dist: Shimoga
Karnataka
jkdange@gmail.com
http://jkdange.blogspot.com