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Hiram Ting Huong Yiew
Qualitative Research and Procedures
In-House Training (2)
SEGi College Sarawak, Kuching
November 15, 2014
Organized by Sarawak Research Society
In Collaboration with SEGi College Sarawak
1
Acknowledgement
The training instructor wishes to express his gratitude to
Prof Dr Ernest Cyril de Run and Prof Ramayah Thurasamy
for their guidance on the training and its contents.
In-House Training (2) – Qualitative Research
November 15, 2014
2
About Myself
Profile:
 Hiram Ting
 Married to Rebecca Sii
 Father to Carlyne & Henna
Academia:
 B. Com at Canterbury University, NZ
 Corporate MBA at UNIMAS
 PhD at UNIMAS (viva candidature)
Employment:
 Youth & social worker in a Christian organization
 PT lecturer at Swinburne University Sarawak
 PT Msc lecturer at Open University Malaysia
 PT MBA lecturer at SEGi College Sarawak
 Research tutor under Zamalah contract
 RA/RO of consultancy projects
3
Research Experience (2012-2014):
 10 conference proceedings (local and international)
 5 published journal articles (local and international)
 6 manuscripts under review, 5 working papers
 Invited speaker at graduate seminar in 2013
 Assisting 3 consultancy projects
 Reviewing of manuscripts and theses/drafts
Progress Recognition (2012-2014):
 3 best paper awards (IBBC2012, APMMC2013, IBBC2014)
 Silver and Bronze awards in R&D Exposition 2013
 2 intellectual properties awards in 2013
 Full Zamalah scholarship (2011-2013)
 DPI (internal grant) co-investigator (2011-2013)
 FRGS (MOHE grant) co-researcher (2014-2017)
About Myself (cont.)
4
5
6
Contents
 Research Paradigms
 Research Designs
 Research Approaches
 Comparisons between
Qualitative and Quantitative
Approaches
 Mixed-Methods Designs
 Why Qualitative?
 Overview of Qualitative
Research
 Preliminary Decisions
 Enumerators and Coders
 Population and Sample
 Sampling Strategy
 Research Methods
 Data Processing
 Data Analysis
 CAQDAS
 Findings and Discussions
 Potential Errors/Bias
 Introduction to ATLAS.ti
(Afternoon session)
7
“… we shall never understand the complex reality of
organizations if we persist in studying them from a
distance, in large samples with gross, cross-sectional
measures. We learn how birds fly by studying them
one at a time, not by scanning them on radar screens”.
Henry Mintzberg
8
Research Paradigms
 Paradigm framework is made up by philosophy, ontology,
epistemology and methodology. Epistemology is the philosophy
of knowledge or how we come to know (Trochim, 2000).
 Understanding the differences in epistemology among research
paradigms begins primarily as a philosophical exercise to address
the question of whether there is one knowable reality or that
there are multiple realities (Olson,1995)
 The underlying belief system of the researcher (ontological
assumptions) largely defines the choice of method
(methodology) (Dobson, 2002)
9
Research Paradigms (cont.)
 In the positivist paradigm, the object of study is independent of
researchers; knowledge is discovered and verified through direct
observations or measurements of phenomena; facts are
established by taking apart a phenomenon to examine its
component parts.
 An alternative view, the naturalist or constructivist view, is that
knowledge is established through the meanings attached to the
phenomena studied; researchers interact with the subjects of
study to obtain data; inquiry changes both researcher and
subject; and knowledge is context and time dependent (Coll &
Chapman, 2000; Cousins, 2002).
10
Research Paradigms (cont.)
 Researchers generally align with one of three research paradigms
(Teddlie & Tashakkori, 2009): (a) a positivist paradigm
(quantitative researchers), (b) a constructivist paradigm
(qualitative researchers), or (c) a pragmatist paradigm (mixed-
methods researchers).
 Pragmatism “rejects the either/or choices associated with the
paradigm wars, advocates for the use of mixed methods in
research, and acknowledges that the values of the researcher play
a large role in interpretation of results” (Tashakkori & Teddlie,
2003, p. 713). Researchers mix methods in ways that make the
most sense given their research questions, integrating qualitative
and quantitative approaches and data analysis procedures and
attempting “to open up inquiry to all possibilities while tying that
search to practical ends” (Maxcy, 2003, p. 86).
11
Research Designs
What is Research Design:
 Procedures for collecting, analyzing, interpreting and reporting
data in research studies.
 They are useful because they help guide the methods decisions
that researchers make.
 Set the logic by which they make interpretations at the end of
their studies.
 Research designs are generally categorized into exploratory,
descriptive and causal research design.
12
Research Approaches
Qualitative vs. Quantitative
 A qualitative study is defined as an inquiry process of
understanding a social or human problem, based on building a
complex, holistic picture, formed with words, reporting detailed
views of informants, and conducted in a natural setting.
 Alternatively a quantitative study is an inquiry into a social or
human problem, based on testing a theory composed of
variables, measured with numbers, and analyzed with statistical
procedures, in order to determine whether the predictive
generalizations of the theory hold true (Creswell, 1994)
13
Research Approaches (cont.)
Inductive vs. Deductive
 When using inductive approach, Yin (2003) suggests that, where
you have made use of existing theory to formulate your research
question and objectives, you may also use the theoretical
propositions that helped you do this as a means to devise a
framework to help you to organize and direct your data analysis.
 The alternative to the deductive approach is to start to collect
data and then explore them to see which themes or issues to
follow up and concentrate on (e.g. Glaser and Strauss, 1967;
Strauss and Corbin 2008). This is also referred to as a grounded
approach because of the nature of the theory or explanation that
emerges as a result of the research process.
14
Comparison between Approaches
15
Qualitative Research: Quantitative Research:
Action research
Case study research
Ethnography
Grounded theory
Semiotics
Discourse analysis
Hermeneutics
Narrative and metaphor
Surveys
Laboratory experiments
Simulation
Mathematical modeling
Structured equation modeling
Statistical analysis
Econometrics
Mixed-Methods Designs
 Mixed-methods research is a combination of “elements of
qualitative and quantitative research approaches (e.g., use of
qualitative and quantitative view points, data collection, analysis,
inference techniques) for the purposes of breadth and depth of
understanding and corroboration” (p. 123).
 Mixed-methods research has several purposes and can address
many types of research questions. Mixed methods can illustrate
and explain quantitative findings, describe both process and
product, check reasons for unexpected effects, develop the basis
for instruments, show the extent of generality, validate and
triangulate other data, and fulfill social or political purposes
(Krathwohl, 2009).
16
Mixed-Methods Designs (cont.)
 Creswell and Plano Clark (2011) described six possible designs.
 Convergent Design: Quantitative and qualitative data are
collected concurrently, analyzed separately, and then merged.
 Explanatory Design: Quantitative data are collected and
analyzed first; results are used to inform follow-up qualitative
data collection.
 Exploratory Design: Qualitative data are collected and
analyzed first, results are used to inform follow-up quantitative
data collection.
 Embedded Design: Qualitative and quantitative data can be
collected sequentially, concurrently, or both. One form of
data is embedded within another.
17
Mixed-Methods Designs (cont.)
 Creswell and Plano Clark (2011) described six possible designs.
 Transformative Design: The researcher frames the study
within a transformative theoretical perspective. Qualitative and
quantitative data can be collected concurrently, sequentially,
or both.
 Multiphase Design: Both sequential and concurrent strands
are included in a study over a period of time (e.g., in a large-
scale evaluation).
18
Mixed-Methods Designs (cont.)
Priority Decision
Quantitative Qualitative
Qualitative Preliminary
Qual QUANT
Quantitative Preliminary
Quant QUAL
Qualitative follow-up
QUANT Qual
Quantitative follow-up
QUAL Quant
Comp
Prelim
Comp
Follow-up
Sequence
Decision
19
Why Qualitative
It is useful when:
 It is difficult to develop specific and actionable decision,
statements or research objectives.
 The research objective is to develop a detailed and in-depth
understanding of some phenomena.
 The research objective is to learn how a phenomenon occurs in
its natural setting or to learn how to express some concept in
colloquial terms.
 The behavior the researcher is studying is particularly context-
dependent.
 A fresh approach to studying the problem is needed.
20
Why Qualitative (cont.)
 Qualitative research methods are designed to help researchers
understand people and what they say and do.
 They allow a researcher to see and understand the context within
which actions and decisions take place.
 It is the context that helps to “explain” why someone said
something or acted the way they did.
 Also by talking to people, or reading what they have written, we
can find out what they are or were thinking.
 Types of questions: What is happening here?Why is it
happening? How has it come to happen this way?When did it
happen?
21
Overview of Qualitative Research
22
Written Record
(Thesis, book, report, article ...)
Data Analysis Approach
(Hermeneutics, semiotics,
narrative analysis . ..)
Data Collection Technique
(interviews, participant
observation, documents)
Research Method
(action research, case study,
ethnography, grounded theory ...)
Philosophical Assumptions
(positivist, interpretive, critical)
Overview of Qualitative Research
23
Overview of Qualitative Research (cont.)
1. Identify and diagnose the
problem
2. Generate a programmatic
solution to solve problem
3. Design and test intervention to
solve the problem
4. Ensure results are used
5. Disseminate results
24
Preliminary Decision
 Descriptive of rules and systematic procedures are necessary for
the validation of qualitative research (Kolbe & Burnett, 1991).
1. Problem and Propositions
2. Sampling decisions
3. Research method and instrument design
4. Pilot study/pre-test (Dicicco-Bloom & Crabtree, 2006)
5. Enumerators, transcribers, coders and translators are
carefully selected and/or trained (Hemsley, 2000).
6. Data processing and ethic considerations
7. Dealing with potential errors/bias (Childers & Skinner, 1985;
Steele, Schwendig & Kilpatrick, 1992)
25
Enumerators/Coders
 Inexperienced enumerators all started at about the same level
and thus could be trained together or separately (Dijkstra, 1983)
but they can commit mistakes. Experienced enumerators would
perform well but cost more, and would be much more diverse.
 Moreover, young adults are more flexible in terms of time and
pliable in intellectual capabilities. Females in their young
adulthood could easily stabilize possible confounders of gender,
age and language differences (Moerman, 2010).
 Consistency must be preserved across the enumerators while
generating productive discussion during interviewing process.
26
Enumerators/Coders (cont.)
 Enumerators who interview are encouraged to transcribe the
recorded interviews due to their familiarity with the topics
discussed during interview sessions.
 Translators and back-translators should not be the same persons.
 Enumerators/transcribers are also encouraged to be
coders/judges due to their familiarity with the topics.
 Training and stringent procedures are mandatory to ensure the
transcribing and coding behaviours are consistent and
appropriate.
 Do you have/need enumerators/coders? Who should be your
enumerators/coders? Can they do it?
27
Population and Sample
 Stake (1994) elucidates that generalization is not the criteria for
case selection in qualitative study; rather the replication of each
case that predicts similar results or patterns of findings is what
matters.
 Eisenhardt (1989), Lincoln and Guba (1985) advocate that cases
and respondents should be added until data saturation or
replication is obtained or the point of redundancy is arrived at.
 As far as the sample size is concerned, there are no clear guides
as to how many cases or respondents should be included in
qualitative studies (Patton, 1990; Perry, 1998).
28
Population and Sample (cont.)
 Eisenhardt (1989) suggests four to ten cases to achieve credible
purpose.
 Hedges (1985) advocates four to six cases for a serious project
while Miles and Huberman (1994) suggest two to four cases as
the minimum and fifteen as the maximum.
 Ritchie, Lewis and Elam (2003) state that qualitative samples
often lie under 50 respondents while Bertaux (1981) asserts that
fifteen is the smallest acceptable sample for all qualitative
research.
 Perry (1998) recommends 30-50 interviews for any qualitative
studies at PhD level.
29
Sampling Strategy
 Non-probability sampling technique was adopted instead of
probability sampling technique as there is no way to estimate the
probability of any one person being included in the sample in a
large population, and no assurance is given that each person has
a chance of being included (Maxfield & Babbie, 2011).
 It is also inappropriate to assume that human behaviours such as
beliefs, attitudes and intentions are normally distributed within
the population (Jackson, 1970).
 Sampling strategies include purposive, quota, theoretical and
convenience sampling techniques.
30
Research Methods
31
Research Methods (cont.)
32
Research Methods (cont.)
Interview
 Interview is one of the most useful tools of collecting data in
qualitative research (DeVillis, 1991; Zikmund et al., 2010) and
the most common type of research instrument employed in
mixed-method marketing studies (Hanson & Grimmer, 2007).
 It is known to be able to offer great insight into consumer
behaviour (Kahan, 1990; Roller, 1987).
 It is the recommendable qualitative means for studies related to
generation (Fountain & Lamb, 2011; Pennington-Gray et al.,
2010).
 The purpose is to allow probes and obtain unrestricted
responses so as to identify important subjects and common
themes for subsequent analysis (Burns & Bush, 2005).
33
Research Methods (cont.)
Interview
 Rubin and Rubin (2005, p. 13) elaborate that, “the depth,
details, and richness sought in interviews, what Clifford Geertz
(1973) called „thick description‟, are rooted in the interviewers‟
first-hand experiences and form the material that researchers
gather and synthesize. To get to this level of detail, depth, and
focus, researchers work out main questions, probes, and follow-
ups.”
 The emphasis is to draw as much information as possible from
the respondents, listen patiently, and encourage them to speak
(Dicicco-Bloom & Crabtree, 2006).
34
Research Methods (cont.)
Types of Interview
 Structured interviews
 The use of pre-formulated questions, strictly regulated with
regard to the order of the questions, and sometimes regulated
with regard to the time available.
 Semi-structured interviews
 The use of some pre-formulated questions, but no strict
adherence to them. New questions might emerge during the
conversation.
 Unstructured interviews
 Few if any pre-formulated questions. In effect the interviewee
has free rein to say what they want. Often no set time limit.
35
Research Methods (cont.)
Focus Group
 Most qualitative interviews are one-on-one, but group interviews
can be used as well.
 The purpose of a focus group interview is to get collective views
on a certain defined topic of interest from a group of people
(normally 6-10) who are known to have had certain experiences.
 Focus groups enable a researcher to elicit opinions, attitudes and
beliefs held by members of a group.
 Moderator is key to focus group discussions.
36
Research Methods (cont.)
Case Study
 Research cases can be used:
 In exploratory research – to discover
 In explanatory research – to test, to explain, or to compare
 The purpose of case study research in business and management
is to use empirical evidence from real people in real
organizations to make an original contribution to knowledge.
 Yin defines a case study as an empirical inquiry that investigates a
contemporary phenomenon within its real-life context, especially
when the boundaries between phenomenon and context are not
clearly evident" (Yin, 1994, p. 13).
37
Research Methods (cont.)
Ethnographic Research
 The main purpose of ethnography is to obtain a deep
understanding of people and their culture.
 One distinguishing feature is fieldwork.
 Ethnographers immerse themselves in the life of people they
study (Lewis, 1985) and seek to place the phenomena studied in
their social and cultural context.
 In ethnographic research, the context is what defines the
situation and makes it what it is.
38
Research Methods (cont.)
Ethnographic research Case study research
Time commitment – significant
length of time required in the field
(months/ year)
Time commitment – less time
required in the field (weeks / few
months at most)
Orientation of researcher – learns
from people
Orientation of researcher – studies
people
Type of data collected – interviews,
documents, notes from fieldwork
and participant observation
Type of data collected – mostly
interviews and documents
39
Research Methods (cont.)
Observation
 Observation is when you are watching other people from the
outside as an observer.
 Participant observation is when you not only observe people
doing things, but you participate to some extent in these activities
as well.
 The main idea of participant observation is that you are talking
with people and interacting with them in an attempt to gain an
understanding of their beliefs and activities from the inside.
40
Data Processing
Transcription
 Good audio recording is crucial for the quality of the transcript,
and likewise the quality of the transcript itself is crucial for the
reliability of the analysis (Moerman, 2010; Potter & Hepburn,
2005).
 Interviews are transcribed verbatim (Burns & Bush, 2005). It is
known to be time-consuming. Data sampling can be used where
only relevant sections are transcribed (Sauders et al., 2009)
 In the process, an independent assistant can do a thorough
review of each completed transcript, matching it against the
recorded interview (Kurasaki, 2000).
41
Data Processing (cont.)
Translation
 Translation of questions into mother languages is encouraged in
order to draw reliable and valid information from respondents
who preferred using these languages (Malhorta & Birks, 2003).
 Past studies emphasize the importance of establishing
appropriate translation procedure (Brislin, 1970; Rustogi,
Hensel & Burgers, 1996; Werner & Campbell, 1970).
 Competent translators who are familiar with the content involved
in the source language materials are essential.
42
Data Processing (cont.)
Back-translation
 Back-translation is known to be one of the most widely used
translation techniques adopted in cross-cultural research
(Cateora, 1990; Usunier, 2000).
 When translating the transcripts from English back to the source
language, different translators must be called upon to back-
translate the transcripts (Werner & Campbell, 1970).
 Reliability or usability of English transcripts is determined by
looking at the agreement between the script with source language
and back-translated script.
43
Data Analysis
44
 Narrative analysis
 Semiotics
 Content analysis
 Conversation analysis
 Discourse analysis
 Grounded theory
 Hermeneutics
 Phenomenology
 Literary criticism
 Deconstruction
Coding
Series of events
Critical incidents
Decision-modelling
Cognitive maps
Analytic induction
Within case analysis
Cross case analysis
Pattern matching
Explanation building
Time series analysis
Data Analysis (cont.)
 Responses to open-ended and probing questions were found in
free-flowing texts. One solution to annotating the main points for
coding purpose was to identify “idea units” based on where an
idea started and ended (Carey, Morgan, & Oxtoby, 1996).
 Coding is the process by which themes are attached to segments
of data that depict what each segment is about (Charmaz, 2006).
 “Codes are tags or labels for assigning units of meaning to the
descriptive or inferential information compiled during a study.
Codes are attached to “chunks” of varying size – words, phrases,
sentences, or whole paragraphs, connected or unconnected to a
specific setting” (Miles and Huberman, 1994).
45
Data Analysis (cont.)
Analytic Induction
 Analytic induction is a way to develop causal explanations of a
phenomenon from one or more cases.
 First, define a phenomenon that requires explanation and
propose an explanation. Next, examine a case to see if the
explanation fits. If it does, then examine another case. An
explanation is accepted until a new case falsifies it.
 When a case is found that doesn‟t fit, change the explanation (so
that you can include the new case) or redefine the phenomenon
(so that you can exclude the nuisance case).
 Ideally the process continues until a universal explanation for all
known cases of a phenomenon is attained (Ryan & Bernard,
2000, p. 787).
46
Data Analysis (cont.)
Grounded Theory
 In the grounded theory of Strauss and Corbin (2008) the
disaggregation of data into units is called open coding, the
process of recognizing relationships between categories is
referred to as axial coding, and the integration of categories to
produce a theory is labeled selective coding.
 The use of a grounded theory strategy will be time consuming,
intensive and reflective. Before you commit yourself to this
strategy, you will need to consider the time that you have to
conduct your research, the level of competence you will need,
your access to data, and the logistical implications of immersing
yourself in such an intensive approach to research.
 Watch Grounded Theory by Graham R. Gibbs on Youtube.
47
Data Analysis (cont.)
Hermeneutics
 Hermeneutics suggests a way of understanding textual data.
 Hermeneutics is primarily concerned with the meaning of a text
or text-analogue (an example of a text-analogue is an
organization, which the researcher comes to understand through
text or pictures).
 The basic question in hermeneutics is: what is the meaning of
this text? (Radnitzky, 1970, p. 20)
 If hermeneutic analysis were to be used in business and
management, the object of the interpretive effort could become
one of attempting to make sense of a company as a text-
analogue.
48
Data Analysis (cont.)
Semiotics
 Semiotics is primarily concerned with
the analysis of signs and symbols and
their meaning.
 A sign or symbol is something that can
stand for something else.
 The greatest use of semiotics has been
in marketing, consumer research and
information systems. This is because of
the pervasiveness of signs in marketing
communications (e.g. brand names,
logos, and advertising) and the
importance of “information” in
information systems
49
Data Analysis (cont.)
Content Analysis
 Content analysis has long been used in qualitative marketing
studies (Wright & Barbour, 1975; Resnik & Stern, 1977;
Kassarjian, 1977).
 It is particularly useful when data are collected through personal
or in-depth interview (Kassarjian, 1977), and for evaluating
various communication forms on human behaviours (Yale &
Gilly, 1988).
 It includes frequency counts (Wilkinson, 2000), but at the same
time allows for exploratory analysis of qualitative data (Ryan &
Bernard, 2000).
50
Data Analysis (cont.)
Conversation Analysis
 Conversation analysis looks at at the use of language by people
as a type of action, or as a skilled accomplishment by competent
actors
 A key concept within conversation analysis is the idea of the
speaking turn. The principle of turn-taking in speech is claimed
to be a universal feature of all conversations
 It does not presume the existence of fixed meanings in words
and idioms
 Meanings are shaped in the context of the exchange
51
Data Analysis (cont.)
Discourse Analysis
 Discourse analysis looks at the way texts are constructed and is
concerned with the social contexts within which the text is
embedded.
 The word discourse refers to communication that goes back and
forth, like an argument or debate. All language can be treated as
a social interaction (there is always a speaker/writer and listener/
reader), but discourse analysis focuses mostly on language in use
- the use of naturally occurring language in speech and/or written
text.
 Discourse analysis is concerned with actual instances of language
as used in communication.
52
Data Analysis (cont.)
Narrative Analysis
 Narrative is defined by the Concise Oxford English Dictionary as
a “a spoken or written account of connected events; a story”
(Soanes & Stevenson, 2004)
 Traditionally, a narrative requires a plot, as well as some
coherence. It has a linear structure, with a beginning, middle,
and end.
 Narrative analysis is a qualitative approach to the interpretation
and analysis of qualitative data.
 There are many different kinds of narrative analysis.
53
Data Analysis (cont.)
Choosing a qualitative data analysis approach
 Do you find the approach interesting?
 Is the approach reasonably consistent with your own
philosophical assumptions about knowledge and reality?
 Is the approach reasonably consistent with the research method
you employed?
 Have you gathered the right quantity and quality of data for the
particular qualitative data analysis method?
 Do you have a supervisor or some other faculty member who
can provide advice and guidance on the use of your preferred
approach?
54
Data Analysis (cont.)
Intercoder Agreement
 Researchers have suggested using multiple coders to establish
high inter-coder reliability in content analysis of open-ended
interview data (Bernard, 1995, Kurasaki, 2000).
 It is a measure of agreement between multiple coders about how
they code the themes and apply them to the data (Kurasaki,
2000).
 Such joint agreement is used not only to measure the reliability
of the coders in identifying themes in the transcripts, it also
serves as a proxy for the validity of themes emerged from the
data (Ryan, 1999).
 This prevents the coded themes to be anything but the subjective
imagination or predetermination of the researcher.
55
CAQDAS
Use of QDA Software
 Weitzman and Miles (1995) say that Qualitative Data Analysis (QDA) software
can help the qualitative researcher in:
 Making notes in the field, writing up or transcribing field notes
 Editing: correcting, extending, or revising field notes
 Memoing: writing reflective commentaries on some aspect of the data
 Coding: attaching keywords or tags to segments of text to permit later retrieval
 Storage: keeping text in an organized database
 Search and retrieval: locating relevant segments of texts
 Data "linking": forming categories, clusters, or networks of information
 Content analysis: counting frequencies, sequence, or locations of words and
phrases
 Data display: placing selected or reduced data in a condensed organized format,
 Conclusion-drawing and verification: helping interpretation of data and testing
findings
 Theory-building: developing systematic explanations of findings; testing
hypotheses
 Graphic mapping: creating diagrams that depict findings or theories
 Preparing interim and final reports (Weitzman and Miles, 1995)56
CAQDAS
Should you use QDA software?
 If you are using grounded theory or similar – YES.
 The QDA software might be too mechanistic for some
qualitative data analysis approaches.
 QDA software is only a tool.
 It can be a temptation to become too detailed and not see the
big picture.
 As long as you are able to use the software appropriately, most
qualitative researchers should consider using QDA software.
57
Findings and Discussion
 Findings are the presentation of results after analysis.
 Despite the use of frequency count, the percentage of each count
is not required in findings.
 Quotes of interviews are presented as findings so as to provide
evidence to later discussion.
 Researchers need to discuss the findings with reference to past
literature. Stating what the findings are is not discussion.
 Discussion must be relevant, insightful and rigorous with sound
justification based on the literature.
 Sample qualitative paper:
http://dx.doi.org/10.1080/00220670903382921
58
Potential Errors/Bias
 Error (statistical error) describes the difference between a value
obtained from a data collection process and the 'true' value for
the population. The greater the error, the less representative the
data are of the population.
 Sampling error occurs solely as a result of using a sample from a
population, rather than conducting a census (complete
enumeration) of the population. It refers to the difference
between an estimate for a population based on data from a
sample and the 'true' value for that population which would
result if a census were taken.
 Non-sampling error is caused by factors other than those related
to sample selection. It refers to the presence of any factor,
whether systemic or random, that results in the data values not
accurately reflecting the 'true' value for the population.
59
Potential Errors/Bias (cont.)
 Coverage error: this occurs when a unit in the sample is
incorrectly excluded or included, or is duplicated in the sample
(e.g. a field interviewer fails to interview a selected household or
some people in a household).
 Non-response error: this refers to the failure to obtain a response
from some unit because of absence, non-contact, refusal, or
some other reason. Non-response can be complete non-
response (i.e. no data has been obtained at all from a selected
unit) or partial non-response (i.e. the answers to some questions
have not been provided by a selected unit).
60
Potential Errors/Bias (cont.)
 Response error: this refers to a type of error caused
by respondents intentionally or accidentally providing inaccurate
responses. This occurs when concepts, questions or instructions
are not clearly understood by the respondent; when there are
high levels of respondent burden and memory recall required;
and because some questions can result in a tendency to answer
in a socially desirable way (giving a response which they feel is
more acceptable rather than being an accurate response).
 Interviewer error: this occurs when interviewers incorrectly
record information; are not neutral or objective; influence the
respondent to answer in a particular way; or assume responses
based on appearance or other characteristics.
 Processing error: this refers to errors that occur in the process of
data collection, data entry, coding, editing and output.
61
62
THANK YOU
Hiram Ting
Email: hiramparousia@gmail.com
Research Officer at Universiti Malaysia Sarawak
MBA PT Lecturer at SEGi College Sarawak
in collaboration with University of Sunderland, UK
PT Lecturer at Open University Malaysia and
Swinburne University of Technology Sarawak, Malaysia
63

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In house training 151114 qualitative research

  • 1. Hiram Ting Huong Yiew Qualitative Research and Procedures In-House Training (2) SEGi College Sarawak, Kuching November 15, 2014 Organized by Sarawak Research Society In Collaboration with SEGi College Sarawak 1
  • 2. Acknowledgement The training instructor wishes to express his gratitude to Prof Dr Ernest Cyril de Run and Prof Ramayah Thurasamy for their guidance on the training and its contents. In-House Training (2) – Qualitative Research November 15, 2014 2
  • 3. About Myself Profile:  Hiram Ting  Married to Rebecca Sii  Father to Carlyne & Henna Academia:  B. Com at Canterbury University, NZ  Corporate MBA at UNIMAS  PhD at UNIMAS (viva candidature) Employment:  Youth & social worker in a Christian organization  PT lecturer at Swinburne University Sarawak  PT Msc lecturer at Open University Malaysia  PT MBA lecturer at SEGi College Sarawak  Research tutor under Zamalah contract  RA/RO of consultancy projects 3
  • 4. Research Experience (2012-2014):  10 conference proceedings (local and international)  5 published journal articles (local and international)  6 manuscripts under review, 5 working papers  Invited speaker at graduate seminar in 2013  Assisting 3 consultancy projects  Reviewing of manuscripts and theses/drafts Progress Recognition (2012-2014):  3 best paper awards (IBBC2012, APMMC2013, IBBC2014)  Silver and Bronze awards in R&D Exposition 2013  2 intellectual properties awards in 2013  Full Zamalah scholarship (2011-2013)  DPI (internal grant) co-investigator (2011-2013)  FRGS (MOHE grant) co-researcher (2014-2017) About Myself (cont.) 4
  • 5. 5
  • 6. 6
  • 7. Contents  Research Paradigms  Research Designs  Research Approaches  Comparisons between Qualitative and Quantitative Approaches  Mixed-Methods Designs  Why Qualitative?  Overview of Qualitative Research  Preliminary Decisions  Enumerators and Coders  Population and Sample  Sampling Strategy  Research Methods  Data Processing  Data Analysis  CAQDAS  Findings and Discussions  Potential Errors/Bias  Introduction to ATLAS.ti (Afternoon session) 7
  • 8. “… we shall never understand the complex reality of organizations if we persist in studying them from a distance, in large samples with gross, cross-sectional measures. We learn how birds fly by studying them one at a time, not by scanning them on radar screens”. Henry Mintzberg 8
  • 9. Research Paradigms  Paradigm framework is made up by philosophy, ontology, epistemology and methodology. Epistemology is the philosophy of knowledge or how we come to know (Trochim, 2000).  Understanding the differences in epistemology among research paradigms begins primarily as a philosophical exercise to address the question of whether there is one knowable reality or that there are multiple realities (Olson,1995)  The underlying belief system of the researcher (ontological assumptions) largely defines the choice of method (methodology) (Dobson, 2002) 9
  • 10. Research Paradigms (cont.)  In the positivist paradigm, the object of study is independent of researchers; knowledge is discovered and verified through direct observations or measurements of phenomena; facts are established by taking apart a phenomenon to examine its component parts.  An alternative view, the naturalist or constructivist view, is that knowledge is established through the meanings attached to the phenomena studied; researchers interact with the subjects of study to obtain data; inquiry changes both researcher and subject; and knowledge is context and time dependent (Coll & Chapman, 2000; Cousins, 2002). 10
  • 11. Research Paradigms (cont.)  Researchers generally align with one of three research paradigms (Teddlie & Tashakkori, 2009): (a) a positivist paradigm (quantitative researchers), (b) a constructivist paradigm (qualitative researchers), or (c) a pragmatist paradigm (mixed- methods researchers).  Pragmatism “rejects the either/or choices associated with the paradigm wars, advocates for the use of mixed methods in research, and acknowledges that the values of the researcher play a large role in interpretation of results” (Tashakkori & Teddlie, 2003, p. 713). Researchers mix methods in ways that make the most sense given their research questions, integrating qualitative and quantitative approaches and data analysis procedures and attempting “to open up inquiry to all possibilities while tying that search to practical ends” (Maxcy, 2003, p. 86). 11
  • 12. Research Designs What is Research Design:  Procedures for collecting, analyzing, interpreting and reporting data in research studies.  They are useful because they help guide the methods decisions that researchers make.  Set the logic by which they make interpretations at the end of their studies.  Research designs are generally categorized into exploratory, descriptive and causal research design. 12
  • 13. Research Approaches Qualitative vs. Quantitative  A qualitative study is defined as an inquiry process of understanding a social or human problem, based on building a complex, holistic picture, formed with words, reporting detailed views of informants, and conducted in a natural setting.  Alternatively a quantitative study is an inquiry into a social or human problem, based on testing a theory composed of variables, measured with numbers, and analyzed with statistical procedures, in order to determine whether the predictive generalizations of the theory hold true (Creswell, 1994) 13
  • 14. Research Approaches (cont.) Inductive vs. Deductive  When using inductive approach, Yin (2003) suggests that, where you have made use of existing theory to formulate your research question and objectives, you may also use the theoretical propositions that helped you do this as a means to devise a framework to help you to organize and direct your data analysis.  The alternative to the deductive approach is to start to collect data and then explore them to see which themes or issues to follow up and concentrate on (e.g. Glaser and Strauss, 1967; Strauss and Corbin 2008). This is also referred to as a grounded approach because of the nature of the theory or explanation that emerges as a result of the research process. 14
  • 15. Comparison between Approaches 15 Qualitative Research: Quantitative Research: Action research Case study research Ethnography Grounded theory Semiotics Discourse analysis Hermeneutics Narrative and metaphor Surveys Laboratory experiments Simulation Mathematical modeling Structured equation modeling Statistical analysis Econometrics
  • 16. Mixed-Methods Designs  Mixed-methods research is a combination of “elements of qualitative and quantitative research approaches (e.g., use of qualitative and quantitative view points, data collection, analysis, inference techniques) for the purposes of breadth and depth of understanding and corroboration” (p. 123).  Mixed-methods research has several purposes and can address many types of research questions. Mixed methods can illustrate and explain quantitative findings, describe both process and product, check reasons for unexpected effects, develop the basis for instruments, show the extent of generality, validate and triangulate other data, and fulfill social or political purposes (Krathwohl, 2009). 16
  • 17. Mixed-Methods Designs (cont.)  Creswell and Plano Clark (2011) described six possible designs.  Convergent Design: Quantitative and qualitative data are collected concurrently, analyzed separately, and then merged.  Explanatory Design: Quantitative data are collected and analyzed first; results are used to inform follow-up qualitative data collection.  Exploratory Design: Qualitative data are collected and analyzed first, results are used to inform follow-up quantitative data collection.  Embedded Design: Qualitative and quantitative data can be collected sequentially, concurrently, or both. One form of data is embedded within another. 17
  • 18. Mixed-Methods Designs (cont.)  Creswell and Plano Clark (2011) described six possible designs.  Transformative Design: The researcher frames the study within a transformative theoretical perspective. Qualitative and quantitative data can be collected concurrently, sequentially, or both.  Multiphase Design: Both sequential and concurrent strands are included in a study over a period of time (e.g., in a large- scale evaluation). 18
  • 19. Mixed-Methods Designs (cont.) Priority Decision Quantitative Qualitative Qualitative Preliminary Qual QUANT Quantitative Preliminary Quant QUAL Qualitative follow-up QUANT Qual Quantitative follow-up QUAL Quant Comp Prelim Comp Follow-up Sequence Decision 19
  • 20. Why Qualitative It is useful when:  It is difficult to develop specific and actionable decision, statements or research objectives.  The research objective is to develop a detailed and in-depth understanding of some phenomena.  The research objective is to learn how a phenomenon occurs in its natural setting or to learn how to express some concept in colloquial terms.  The behavior the researcher is studying is particularly context- dependent.  A fresh approach to studying the problem is needed. 20
  • 21. Why Qualitative (cont.)  Qualitative research methods are designed to help researchers understand people and what they say and do.  They allow a researcher to see and understand the context within which actions and decisions take place.  It is the context that helps to “explain” why someone said something or acted the way they did.  Also by talking to people, or reading what they have written, we can find out what they are or were thinking.  Types of questions: What is happening here?Why is it happening? How has it come to happen this way?When did it happen? 21
  • 22. Overview of Qualitative Research 22 Written Record (Thesis, book, report, article ...) Data Analysis Approach (Hermeneutics, semiotics, narrative analysis . ..) Data Collection Technique (interviews, participant observation, documents) Research Method (action research, case study, ethnography, grounded theory ...) Philosophical Assumptions (positivist, interpretive, critical)
  • 23. Overview of Qualitative Research 23
  • 24. Overview of Qualitative Research (cont.) 1. Identify and diagnose the problem 2. Generate a programmatic solution to solve problem 3. Design and test intervention to solve the problem 4. Ensure results are used 5. Disseminate results 24
  • 25. Preliminary Decision  Descriptive of rules and systematic procedures are necessary for the validation of qualitative research (Kolbe & Burnett, 1991). 1. Problem and Propositions 2. Sampling decisions 3. Research method and instrument design 4. Pilot study/pre-test (Dicicco-Bloom & Crabtree, 2006) 5. Enumerators, transcribers, coders and translators are carefully selected and/or trained (Hemsley, 2000). 6. Data processing and ethic considerations 7. Dealing with potential errors/bias (Childers & Skinner, 1985; Steele, Schwendig & Kilpatrick, 1992) 25
  • 26. Enumerators/Coders  Inexperienced enumerators all started at about the same level and thus could be trained together or separately (Dijkstra, 1983) but they can commit mistakes. Experienced enumerators would perform well but cost more, and would be much more diverse.  Moreover, young adults are more flexible in terms of time and pliable in intellectual capabilities. Females in their young adulthood could easily stabilize possible confounders of gender, age and language differences (Moerman, 2010).  Consistency must be preserved across the enumerators while generating productive discussion during interviewing process. 26
  • 27. Enumerators/Coders (cont.)  Enumerators who interview are encouraged to transcribe the recorded interviews due to their familiarity with the topics discussed during interview sessions.  Translators and back-translators should not be the same persons.  Enumerators/transcribers are also encouraged to be coders/judges due to their familiarity with the topics.  Training and stringent procedures are mandatory to ensure the transcribing and coding behaviours are consistent and appropriate.  Do you have/need enumerators/coders? Who should be your enumerators/coders? Can they do it? 27
  • 28. Population and Sample  Stake (1994) elucidates that generalization is not the criteria for case selection in qualitative study; rather the replication of each case that predicts similar results or patterns of findings is what matters.  Eisenhardt (1989), Lincoln and Guba (1985) advocate that cases and respondents should be added until data saturation or replication is obtained or the point of redundancy is arrived at.  As far as the sample size is concerned, there are no clear guides as to how many cases or respondents should be included in qualitative studies (Patton, 1990; Perry, 1998). 28
  • 29. Population and Sample (cont.)  Eisenhardt (1989) suggests four to ten cases to achieve credible purpose.  Hedges (1985) advocates four to six cases for a serious project while Miles and Huberman (1994) suggest two to four cases as the minimum and fifteen as the maximum.  Ritchie, Lewis and Elam (2003) state that qualitative samples often lie under 50 respondents while Bertaux (1981) asserts that fifteen is the smallest acceptable sample for all qualitative research.  Perry (1998) recommends 30-50 interviews for any qualitative studies at PhD level. 29
  • 30. Sampling Strategy  Non-probability sampling technique was adopted instead of probability sampling technique as there is no way to estimate the probability of any one person being included in the sample in a large population, and no assurance is given that each person has a chance of being included (Maxfield & Babbie, 2011).  It is also inappropriate to assume that human behaviours such as beliefs, attitudes and intentions are normally distributed within the population (Jackson, 1970).  Sampling strategies include purposive, quota, theoretical and convenience sampling techniques. 30
  • 33. Research Methods (cont.) Interview  Interview is one of the most useful tools of collecting data in qualitative research (DeVillis, 1991; Zikmund et al., 2010) and the most common type of research instrument employed in mixed-method marketing studies (Hanson & Grimmer, 2007).  It is known to be able to offer great insight into consumer behaviour (Kahan, 1990; Roller, 1987).  It is the recommendable qualitative means for studies related to generation (Fountain & Lamb, 2011; Pennington-Gray et al., 2010).  The purpose is to allow probes and obtain unrestricted responses so as to identify important subjects and common themes for subsequent analysis (Burns & Bush, 2005). 33
  • 34. Research Methods (cont.) Interview  Rubin and Rubin (2005, p. 13) elaborate that, “the depth, details, and richness sought in interviews, what Clifford Geertz (1973) called „thick description‟, are rooted in the interviewers‟ first-hand experiences and form the material that researchers gather and synthesize. To get to this level of detail, depth, and focus, researchers work out main questions, probes, and follow- ups.”  The emphasis is to draw as much information as possible from the respondents, listen patiently, and encourage them to speak (Dicicco-Bloom & Crabtree, 2006). 34
  • 35. Research Methods (cont.) Types of Interview  Structured interviews  The use of pre-formulated questions, strictly regulated with regard to the order of the questions, and sometimes regulated with regard to the time available.  Semi-structured interviews  The use of some pre-formulated questions, but no strict adherence to them. New questions might emerge during the conversation.  Unstructured interviews  Few if any pre-formulated questions. In effect the interviewee has free rein to say what they want. Often no set time limit. 35
  • 36. Research Methods (cont.) Focus Group  Most qualitative interviews are one-on-one, but group interviews can be used as well.  The purpose of a focus group interview is to get collective views on a certain defined topic of interest from a group of people (normally 6-10) who are known to have had certain experiences.  Focus groups enable a researcher to elicit opinions, attitudes and beliefs held by members of a group.  Moderator is key to focus group discussions. 36
  • 37. Research Methods (cont.) Case Study  Research cases can be used:  In exploratory research – to discover  In explanatory research – to test, to explain, or to compare  The purpose of case study research in business and management is to use empirical evidence from real people in real organizations to make an original contribution to knowledge.  Yin defines a case study as an empirical inquiry that investigates a contemporary phenomenon within its real-life context, especially when the boundaries between phenomenon and context are not clearly evident" (Yin, 1994, p. 13). 37
  • 38. Research Methods (cont.) Ethnographic Research  The main purpose of ethnography is to obtain a deep understanding of people and their culture.  One distinguishing feature is fieldwork.  Ethnographers immerse themselves in the life of people they study (Lewis, 1985) and seek to place the phenomena studied in their social and cultural context.  In ethnographic research, the context is what defines the situation and makes it what it is. 38
  • 39. Research Methods (cont.) Ethnographic research Case study research Time commitment – significant length of time required in the field (months/ year) Time commitment – less time required in the field (weeks / few months at most) Orientation of researcher – learns from people Orientation of researcher – studies people Type of data collected – interviews, documents, notes from fieldwork and participant observation Type of data collected – mostly interviews and documents 39
  • 40. Research Methods (cont.) Observation  Observation is when you are watching other people from the outside as an observer.  Participant observation is when you not only observe people doing things, but you participate to some extent in these activities as well.  The main idea of participant observation is that you are talking with people and interacting with them in an attempt to gain an understanding of their beliefs and activities from the inside. 40
  • 41. Data Processing Transcription  Good audio recording is crucial for the quality of the transcript, and likewise the quality of the transcript itself is crucial for the reliability of the analysis (Moerman, 2010; Potter & Hepburn, 2005).  Interviews are transcribed verbatim (Burns & Bush, 2005). It is known to be time-consuming. Data sampling can be used where only relevant sections are transcribed (Sauders et al., 2009)  In the process, an independent assistant can do a thorough review of each completed transcript, matching it against the recorded interview (Kurasaki, 2000). 41
  • 42. Data Processing (cont.) Translation  Translation of questions into mother languages is encouraged in order to draw reliable and valid information from respondents who preferred using these languages (Malhorta & Birks, 2003).  Past studies emphasize the importance of establishing appropriate translation procedure (Brislin, 1970; Rustogi, Hensel & Burgers, 1996; Werner & Campbell, 1970).  Competent translators who are familiar with the content involved in the source language materials are essential. 42
  • 43. Data Processing (cont.) Back-translation  Back-translation is known to be one of the most widely used translation techniques adopted in cross-cultural research (Cateora, 1990; Usunier, 2000).  When translating the transcripts from English back to the source language, different translators must be called upon to back- translate the transcripts (Werner & Campbell, 1970).  Reliability or usability of English transcripts is determined by looking at the agreement between the script with source language and back-translated script. 43
  • 44. Data Analysis 44  Narrative analysis  Semiotics  Content analysis  Conversation analysis  Discourse analysis  Grounded theory  Hermeneutics  Phenomenology  Literary criticism  Deconstruction Coding Series of events Critical incidents Decision-modelling Cognitive maps Analytic induction Within case analysis Cross case analysis Pattern matching Explanation building Time series analysis
  • 45. Data Analysis (cont.)  Responses to open-ended and probing questions were found in free-flowing texts. One solution to annotating the main points for coding purpose was to identify “idea units” based on where an idea started and ended (Carey, Morgan, & Oxtoby, 1996).  Coding is the process by which themes are attached to segments of data that depict what each segment is about (Charmaz, 2006).  “Codes are tags or labels for assigning units of meaning to the descriptive or inferential information compiled during a study. Codes are attached to “chunks” of varying size – words, phrases, sentences, or whole paragraphs, connected or unconnected to a specific setting” (Miles and Huberman, 1994). 45
  • 46. Data Analysis (cont.) Analytic Induction  Analytic induction is a way to develop causal explanations of a phenomenon from one or more cases.  First, define a phenomenon that requires explanation and propose an explanation. Next, examine a case to see if the explanation fits. If it does, then examine another case. An explanation is accepted until a new case falsifies it.  When a case is found that doesn‟t fit, change the explanation (so that you can include the new case) or redefine the phenomenon (so that you can exclude the nuisance case).  Ideally the process continues until a universal explanation for all known cases of a phenomenon is attained (Ryan & Bernard, 2000, p. 787). 46
  • 47. Data Analysis (cont.) Grounded Theory  In the grounded theory of Strauss and Corbin (2008) the disaggregation of data into units is called open coding, the process of recognizing relationships between categories is referred to as axial coding, and the integration of categories to produce a theory is labeled selective coding.  The use of a grounded theory strategy will be time consuming, intensive and reflective. Before you commit yourself to this strategy, you will need to consider the time that you have to conduct your research, the level of competence you will need, your access to data, and the logistical implications of immersing yourself in such an intensive approach to research.  Watch Grounded Theory by Graham R. Gibbs on Youtube. 47
  • 48. Data Analysis (cont.) Hermeneutics  Hermeneutics suggests a way of understanding textual data.  Hermeneutics is primarily concerned with the meaning of a text or text-analogue (an example of a text-analogue is an organization, which the researcher comes to understand through text or pictures).  The basic question in hermeneutics is: what is the meaning of this text? (Radnitzky, 1970, p. 20)  If hermeneutic analysis were to be used in business and management, the object of the interpretive effort could become one of attempting to make sense of a company as a text- analogue. 48
  • 49. Data Analysis (cont.) Semiotics  Semiotics is primarily concerned with the analysis of signs and symbols and their meaning.  A sign or symbol is something that can stand for something else.  The greatest use of semiotics has been in marketing, consumer research and information systems. This is because of the pervasiveness of signs in marketing communications (e.g. brand names, logos, and advertising) and the importance of “information” in information systems 49
  • 50. Data Analysis (cont.) Content Analysis  Content analysis has long been used in qualitative marketing studies (Wright & Barbour, 1975; Resnik & Stern, 1977; Kassarjian, 1977).  It is particularly useful when data are collected through personal or in-depth interview (Kassarjian, 1977), and for evaluating various communication forms on human behaviours (Yale & Gilly, 1988).  It includes frequency counts (Wilkinson, 2000), but at the same time allows for exploratory analysis of qualitative data (Ryan & Bernard, 2000). 50
  • 51. Data Analysis (cont.) Conversation Analysis  Conversation analysis looks at at the use of language by people as a type of action, or as a skilled accomplishment by competent actors  A key concept within conversation analysis is the idea of the speaking turn. The principle of turn-taking in speech is claimed to be a universal feature of all conversations  It does not presume the existence of fixed meanings in words and idioms  Meanings are shaped in the context of the exchange 51
  • 52. Data Analysis (cont.) Discourse Analysis  Discourse analysis looks at the way texts are constructed and is concerned with the social contexts within which the text is embedded.  The word discourse refers to communication that goes back and forth, like an argument or debate. All language can be treated as a social interaction (there is always a speaker/writer and listener/ reader), but discourse analysis focuses mostly on language in use - the use of naturally occurring language in speech and/or written text.  Discourse analysis is concerned with actual instances of language as used in communication. 52
  • 53. Data Analysis (cont.) Narrative Analysis  Narrative is defined by the Concise Oxford English Dictionary as a “a spoken or written account of connected events; a story” (Soanes & Stevenson, 2004)  Traditionally, a narrative requires a plot, as well as some coherence. It has a linear structure, with a beginning, middle, and end.  Narrative analysis is a qualitative approach to the interpretation and analysis of qualitative data.  There are many different kinds of narrative analysis. 53
  • 54. Data Analysis (cont.) Choosing a qualitative data analysis approach  Do you find the approach interesting?  Is the approach reasonably consistent with your own philosophical assumptions about knowledge and reality?  Is the approach reasonably consistent with the research method you employed?  Have you gathered the right quantity and quality of data for the particular qualitative data analysis method?  Do you have a supervisor or some other faculty member who can provide advice and guidance on the use of your preferred approach? 54
  • 55. Data Analysis (cont.) Intercoder Agreement  Researchers have suggested using multiple coders to establish high inter-coder reliability in content analysis of open-ended interview data (Bernard, 1995, Kurasaki, 2000).  It is a measure of agreement between multiple coders about how they code the themes and apply them to the data (Kurasaki, 2000).  Such joint agreement is used not only to measure the reliability of the coders in identifying themes in the transcripts, it also serves as a proxy for the validity of themes emerged from the data (Ryan, 1999).  This prevents the coded themes to be anything but the subjective imagination or predetermination of the researcher. 55
  • 56. CAQDAS Use of QDA Software  Weitzman and Miles (1995) say that Qualitative Data Analysis (QDA) software can help the qualitative researcher in:  Making notes in the field, writing up or transcribing field notes  Editing: correcting, extending, or revising field notes  Memoing: writing reflective commentaries on some aspect of the data  Coding: attaching keywords or tags to segments of text to permit later retrieval  Storage: keeping text in an organized database  Search and retrieval: locating relevant segments of texts  Data "linking": forming categories, clusters, or networks of information  Content analysis: counting frequencies, sequence, or locations of words and phrases  Data display: placing selected or reduced data in a condensed organized format,  Conclusion-drawing and verification: helping interpretation of data and testing findings  Theory-building: developing systematic explanations of findings; testing hypotheses  Graphic mapping: creating diagrams that depict findings or theories  Preparing interim and final reports (Weitzman and Miles, 1995)56
  • 57. CAQDAS Should you use QDA software?  If you are using grounded theory or similar – YES.  The QDA software might be too mechanistic for some qualitative data analysis approaches.  QDA software is only a tool.  It can be a temptation to become too detailed and not see the big picture.  As long as you are able to use the software appropriately, most qualitative researchers should consider using QDA software. 57
  • 58. Findings and Discussion  Findings are the presentation of results after analysis.  Despite the use of frequency count, the percentage of each count is not required in findings.  Quotes of interviews are presented as findings so as to provide evidence to later discussion.  Researchers need to discuss the findings with reference to past literature. Stating what the findings are is not discussion.  Discussion must be relevant, insightful and rigorous with sound justification based on the literature.  Sample qualitative paper: http://dx.doi.org/10.1080/00220670903382921 58
  • 59. Potential Errors/Bias  Error (statistical error) describes the difference between a value obtained from a data collection process and the 'true' value for the population. The greater the error, the less representative the data are of the population.  Sampling error occurs solely as a result of using a sample from a population, rather than conducting a census (complete enumeration) of the population. It refers to the difference between an estimate for a population based on data from a sample and the 'true' value for that population which would result if a census were taken.  Non-sampling error is caused by factors other than those related to sample selection. It refers to the presence of any factor, whether systemic or random, that results in the data values not accurately reflecting the 'true' value for the population. 59
  • 60. Potential Errors/Bias (cont.)  Coverage error: this occurs when a unit in the sample is incorrectly excluded or included, or is duplicated in the sample (e.g. a field interviewer fails to interview a selected household or some people in a household).  Non-response error: this refers to the failure to obtain a response from some unit because of absence, non-contact, refusal, or some other reason. Non-response can be complete non- response (i.e. no data has been obtained at all from a selected unit) or partial non-response (i.e. the answers to some questions have not been provided by a selected unit). 60
  • 61. Potential Errors/Bias (cont.)  Response error: this refers to a type of error caused by respondents intentionally or accidentally providing inaccurate responses. This occurs when concepts, questions or instructions are not clearly understood by the respondent; when there are high levels of respondent burden and memory recall required; and because some questions can result in a tendency to answer in a socially desirable way (giving a response which they feel is more acceptable rather than being an accurate response).  Interviewer error: this occurs when interviewers incorrectly record information; are not neutral or objective; influence the respondent to answer in a particular way; or assume responses based on appearance or other characteristics.  Processing error: this refers to errors that occur in the process of data collection, data entry, coding, editing and output. 61
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  • 63. THANK YOU Hiram Ting Email: hiramparousia@gmail.com Research Officer at Universiti Malaysia Sarawak MBA PT Lecturer at SEGi College Sarawak in collaboration with University of Sunderland, UK PT Lecturer at Open University Malaysia and Swinburne University of Technology Sarawak, Malaysia 63