Increasing the rigor and efficiency of research through the use of qualitative data analysis software

Paper presented at the 2nd Asia-Pacific conference
on Computer-Aided Qualitative Research
24 & 25 February 2011, Macau SAR

Increasing the rigor and efficiency of research through the use of
qualitative data analysis software
Dr Suria Baba and Prof Marohaini Yusoff, University of Malaya, Malaysia.

Abstract
The purpose of this study is to examine the creativity of instructional leaders (IL) in
generating innovative environment during teaching and learning process. Methods for data
collection reflected a qualitative case study approach and included audio-recorded
observations of project work, focus group interviews, and students' journals. Data showed
that students' preparatory activities inside and outside the classroom included negotiating task
definition and teacher expectations, sharing experiences, collaborative dialogue in preparing
presentation materials, and rehearsing and peer-coaching and thus resulting the innovative
environment. Analysis shed useful light on students' contextualization of and orientation to
the task and the interdependence of doing task. To support the humoungous data gathered,
analysis were done using Nvivo. The use of this software has proven to benefit and enrich the
findings of the study and a pathway to increase rigor and efficient in research. Data has been
managed, analyse and code from various sources and has features that are well suited to aid
in analyzing throughout field work. In sum, procedural instruction and friendly user features
had reduce the fear of the researchers in using software as it leads to wider spectrum because
in using the software the researchers are assured of a full assistance. To relate this to the
methodology used, transcricpt from observation, interviews, picture, audio video are archived
systematically and thus developed patterns through the themes provided.

Key Words:
Computer Based Data Analysis, NVivo 8 & 9, Constructivist Instructional Leadership ,
Creative and innovative classroom

Introduction
This article describes how a qualitative data analysis package, NVivo, was used in a study of
creative instuctional leadership (IL) enhancing an innovative and constructivist classroom.
The paper starts with a summary of CAQDA (Computer Aided Qualitative Data Analysis )
that starts the era in assisting qualitative data analysis and followed by the research study in
which NVivo was used to analyze the data and overviews the methodology that was adopted
in this study. It, then, describes how NVivo was used in the analysis of observational (video)

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data, interviews and field notes and finally how it meet to the criteria rigor and efficience
usage.
Weitzman & Miles (1995) noted that CAQDAS has been used in social research, since the
early 1980s. The findings was supported by Creswell (2007) saying that the development of
qualitative data analysis software are well-conceived and is an assistance to expedite and
enhance the process of qualitative research as a whole. The inquirer identifies a text
segments, assign code label, and then searches through the database for all text segments that
have the same code label. In this process the researcher, nor the computer program, does the
coding and categorizing.

There is a substantial literature on the advantages of CAQDAS. In particular, it can facilitate:
data reduction; systematic coding; effective searching; the analysis of large data sets; the
testing of hypotheses; and the identification of negative cases Creswell (2007) claim that
using a computer for the more mechanical aspects of the process allows the researcher to
devote more energy to analytic and interpretive work.

Data analysis as a core business

As we know Miles and Huberman (1994) divide data analysis into three stages: data
reduction, data display and verification but coding may be part of the analysis process, it
should not be thought of as a substitute for analysis as quoted by Atkinson, Coffey and
Delamonts (2003). Coding links data fragments to concepts, but the important analytic work
lies in establishing and thinking about such linkages. However, Tech (1990) claimed that the
coding process does not merely consist of a random division into smaller units, but requires
skilled perception and artful transformation. Richards (2002) regards that coding is a
theorizing process. Different analysts may use different coding systems for the same data,
and the same analyst may apply different coding systems at different stages; there is no one
ideal coding structure. Furthermore to gain thick and rich description of data, analysis must
be done rigorous so the report will be sufficient and in depth not as suferficial as quoted
“student engage in learning activity and they relate their experience” (very thin and
superficial explaination).

Coffey and Atkinson (1996) viewed that the process of analysis and coding is iterative,
because the identification of relevant concepts and codes depends on analysis, but in analysis,

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codes are used as tools to “think with”. Eisenhardt (1989) detail out that it is analysis is an
iterative process started with the development and presentation of an initial set of theoretical
propositions based on evidence from the first phase of data collection during fieldwork and
the theoretical assumptions associated with the theoretical framework. The initial
propositions then became a vehicle for generalizing to the phenomenon studied. As a second
step, the emergent propositions from the first phase were systematically compared with
evidence from the second phase. The theoretical propositions were either supported by the
evidence, revised, or not supported for lack of sufficient evidence. As a final step, the process
was repeated when refined theoretical propositions were systematically compared with
evidence from the previous phase. The central idea was to iterate toward a theory that fits the
data, where projects which supported the emergent theory enhance confidence in its validity,
while projects which did not support the theory often provide an opportunity to refine and
extend the theoretical model.

The method of generalization adopted here is "analytic generalization," in which previously
developed theory is used as a template with which to compare the empirical results of the
case study. Under such logic, when two or more cases are shown to support the same theory,
replication may be claimed (Yin, 1994).

Early steps in data analysis

One of the main and striking feature in the collection of data is the frequent overlap of data
analysis in the process to build theory from case studies (Eisenhardt, 1989). The analytical
techniques adopted in the first stage of data analysis in our own research are listed below.
Note that these techniques were used to help us identify themes, develop categories, and
explore similarities and differences in the data, and relationships among them. The process
are as follows;

First, field notes were an important means of accomplishing this overlap in our study. As
described by Van Maanen (1988), field notes are an ongoing stream-of-consciousness
commentary about what is happening in the research. By reviewing, refining with reflexive
approach our field notes frequently, important issues or conflicting answers provided by
different individuals were identified immediately. Then follow up with several visitations and
interview with the selected key informants to clear up any questions and to provide any

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additional information that was missing and also to bridge the gap. The field notes also were
useful in revising the interview guides and protocol as the study progress . Second, once an
interview was transcribed, reflective remarks were directly entered into the transcripts within
brackets.( As in NVivo it is done with Memos file). These statements were ways of getting
ideas down and of use as a way to facilitate reflection and analytic insight. They were a way
to convert the researcher's perceptions and thoughts into a visible form that allows reflection
(Miles & Huberman, 1994; Strauss & Corbin, 1990). In short, reflective remarks helped us
start thinking, making deep thinking and more general sense of what was happening, and
explaining things in a conceptually coherent way. Finally, a document summary form was
created for each document collected and then filled out in the database. This form put the
document in context, explained its significance, and gave a brief content summary (Miles &
Huberman, 1994).

In sum, overlapping data analysis with data collection not only gives the researcher a head
start in analysis but, more importantly, allows researchers to take advantage of flexible data
collection. Indeed, a key feature of theory-building case research is the freedom to make
adjustments during the data collection process. In our study, adjustments included adding
questions to interview protocol, reviewing more data sources, observe activities in the and out
the classroom whichever viable to the study especially when the opportunity arose to do so,
and interviewing previously unknown individuals who were identified during the study as
important actors in the study.
Thus, this paper specifically addresses how NVivo was used in this research study to analyze
the qualitative data.
Using NVivo in Qualitative Data Analysis: Literature Review
In the mid- 1980’s, a reform in using computer to aid analyzing of qualitative data where
CAQDAS was developed. After this development, qualitative data analysis became quite
different. Richards (2002) claimed that this development had meet to this criteria, (1)
Computing has enabled new and assist in making things easier, previously unavailable
qualitative techniques, (2) there are no support in computerization before until this reform
occurs as an eye opener, at least until recently, and (3) this innovation of computerization
encouraged some biases in qualitative techniques, NVivo does everything, it has character-
based coding, rich text capabilities, edit-while-you-code, multimedia data, and splitting up
the information load that nodes were being asked to carry. Lyn Richards, (2009) regards

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NVivo is designed for the researchers who wish to display and develop rich data in dynamic
documents. The rich data she refers to is a wide range of data collected over a period of time.
Needless to say, computer based programs served poorly in the previous years to deal with
this kind of data. NVivo addresses this need with the features like rich text, memos,
DataBites (media files such as video, audio, images, literature review, external data from
various sources.), and new capabilities embedded into document and node browsers. By
using qualitative data analysis software (QDAS) basically helps and assists researchers
during labor-intensive process of qualitative data analysis.
Not only are there many different approaches and debates on qualitative research methods
and techniques, but also computer-assisted analysis of data was discussed widely. For
instance, (Welsh, 2002) expresses his concern that the software may “guide” researchers in a
particular direction. There are also many other comments like “using QDAS may serve to
distance the researcher from the data, encourage quantitative analysis of qualitative data,
and create a homogeneity in methods across the social sciences” (Welsh, 2002).
Some others believe that using computers in the qualitative analysis process may add rigor
and prestige to research study, also to the thrustwortiness and quality of the analysis. This is
true if we think about how NVivo and other similar programs help organize and manage data
files as well as support the representation of coding in a neat manner. However, it is still the
researchers who will make the decisions for their data organization, coding, or analysis.
Nevertheless, computer analysis programs do not add rigor per se, but the way researchers
handle their data using these programs does add rigor.
Study done by Asensio (2000) using phenomenographic towards students’ experiences of
networked learning in higher education in the U.K., describes the process and rationale of
choosing QDAS. As a group of researchers they investigate and contrast the three most well
known software packages, Atlas/ti, QSR NUD*IST, and QSR NUD*IST Vivo (NVivo) and
explain why they have chosen NVivo in their research. This study is also a very interesting in
that it gives an example of phenomenographic analysis which is completely different from
the grounded theory approach. Asensio (2000) thinks that “the outcome of
phenomenographic research is a set of categories of description which describe the variation
in experiences of phenomena in ways that they were allowed to deepen their understanding
on students’ learning”.
The aforementioned study aims at understanding the students’ experiences of participating in
a networked learning course. The basis of the study is phenomenological and draws on
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individual interviews of 60 students plus observations of the face-to-face classes and online
environments. The study is also complemented by a survey of 300 students and mapping
exercise for wide range of teaching staff to show the examples of the use of networked
learning in higher education in the UK.
The existence of large and varied amounts of data and a research team geographically
distributed require using a software program to support the management of the data. After
using NVivo, the research team agreed that the software increased their speed and flexibility
in coding, retrieving, and linking the data. They also discuss that this new version of
NUD*IST is really advanced and flexible as compared to other versions. One of the advanced
features of NVivo is enabling researchers to work collaboratively on the same project from
different geographic regions of the U.K (Asensio, 2000).
Whilst, Di Gregorio (2000) in her interesting paper discusses how to use NVivo for literature
reviews which are often overlooked as a form of qualitative analysis. She acknowledges the
benefits of bibliographical software such as EndNote, Reference Manager, ProCite, and their
unique biographical tools. However these packages do not support the analysis processes of
literature review. “Of all the qualitative analysis software packages, only NVivo has a
particular set of tools that is ideal for analyzing literature”. She uses “proxy documents”
(documents created in NVivo) to summarize the particular authors’ argument or quotation
which may be retrieved later. “Memos” attached to proxy documents can be used to write
reflections on a particular paper and then use these first reflections to build one’s critique.
“Document” and “node links” of NVivo may be used as reference to other works. Di
Gregorio (2000), also suggests the use of “attributes” and “sets” as organizers of the existent
documents since these are also useful for restricted searches to particular documents by
author, date, or discipline. She also presents some other strategies for the researchers to use in
elaborating their literature review.
Video Intervention/Prevention Assessment (VIA) was used in the study done by Rich and
Patashnick (2002) which investigates health conditions from the patient’s perspective. Each
participant use the devices and they create a personal “video diary” of living with their
medical condition. Therefore, VIA “examines the illness experience from the outside
in”.Rich and Patashnick (2002) try to adopt constructivist theory in their study as well by
asking patients to interview their family members or friends to capture the whole picture of
their condition (multiple perspectives and holistic approach). Also, they believe that social
realities can be represented best by using a variety of media such as words, sounds, and
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images through VIA. In the analysis of their data, they found NVivo as the software package
that responds best to the nature of VIA data. According to them, data can be coded easily in
NVivo and the software supports analysis of different types of data. They also think that
NVivo is ideal in “parallel analysis of visual and audio components, objective and subjective
information, or a variety of types of information that can exist simultaneously in video data”
(Rich & Patashnick, 2002).

The above few research using NVivo gives us some eye opening on the advantages and
authenticity in using computer aided software data analysis. Basically the initial purpose of
this software is to assist the researchers but it had turn into an important package when the
researcher plan out their qualitative research.

Purpose of the Study
In general, there’s a lot of “nuts and bolts” when writing qualitative data analysis, it needs a
lot of effort and courage. It is supported by Dickie (2003) that suggests a different approach
in qualitative research reporting and calls for less jargon and more detailed description within
the data analysis process.
As seen in the previous section, there are only a few studies which exemplify how a
qualitative software package can be used in the analysis of qualitative classroom data. In
order to address a more open approach to reporting and to help researchers better understand
how NVivo is used in an actual classroom study, we will share our experienced on how it was
done in this study. In the following sections, the research study will be give background for
this article and then how and why NVivo was chosen for the data analysis will be explained.
Finally, the researcher will share her experiences with the readers on the advantages of
NVivo software to place rigor and their efficience during data analysis
In recent years constructivism and its implications for instruction have been researched
widely since it was seen as one of the best ways of renewing and restructuring learning
environments.. Authentic learning is, for instance, one good way to ensure constructivism in
the learning environments .Therefore, it is described, defended, and advocated in the
literature. After reviewing the literature in authentic learning, one of the major implications is
that authentic learning has to be used more often during the instruction. What is missing,
however, is knowledge about what successful instructional leadership, especially authentic

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learning, do in the classroom and how students behave in such contexts in propelling studens
ability to create innovative environment

Thus this study is to discuss the findings from the research objectives; (1) to investigate the
creative of IL in enhancing innovative classroom to compliment research question which is
“How does creative IL enhance innovative classroom”. This is based from the statement of
problem of this study on the process of innovative classroom develop by IL. And how do we
use it for our data analysis in the research project. So in this paper basically is to explain how
analysis of data was done in a rigorous and efficient manner. This is due to the statement that
underlying problem in analyzing data that is “ hearing what the data have to say rather than
splicing them into an arbitrary units before searching for the themes, categories or meanings”
..
Methodology and Data Types

The study used qualitative, case study design. One way to summarize the research
methodology is to describe it as an effort to develop a rich, thick description of how creative
IL enhancing innovative classroom, with data drawn from different sources. Qualitative
method is considered to be the best for this study, because it meets the descriptive nature of
the research problems and gives the best picture of the learning environment studied. Eisner
(1998) states that “qualitative experience depends on qualitative forms of inquiry. We learn
to see, hear, and feel.”

Because this study is about the qualities of learning environments being studied, qualitative
inquiry best fits in this framework. Data was gathered in several ways including classroom
observations, informal and formal interviews of students and teachers, field notes, work
completed by students including projects, student self assessments, reflective journal logs,
teacher’s comments and notes.

Multisource in the collection of data
Interviews and Observations: Information about students’ responses was gathered through
interviews and observations. Focus groups interview among student interviews were
conducted at the conclusion of school visitations and also instructional leadership (IL)
opinions’ about the constructivist-learning environment were gathered through in-depth
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interviewing. IL and student were interviewed during the school visitations especially after
classroom observation. The interview focused specifically on the use of authentic materials
and activities that lead by the constructivist and creative instructional leadership that able to
propel innovative environment in classroom. Finally, in addition to these formal interviews,
informal questions were asked of IL and students during observations in the classrooms as
well as videotaping them working on the learning tasks.

Field Notes: During each classroom observation short notes were taken and expanden into
long and reflexive notes were written to clarify what was observed in the classrooms ( Kirk
& Miller) Researchers’ observations in the classrooms might be considered an example of
nonparticipatory observation. However, while trying to be as unobtrusive and unbiased as
possible, the researcher did participate in some activities with teachers and students.

Student Products: A sample of student products (reflective journals, concrete products such
as computer print-outs, pictures, and self or peer evaluation rubrics) were collected and used
in the data analysis.
As for the data analysis approach, the interpretivist research paradigm was used as to guide
the data analysis. This holistic approach of data analysis and a strategy that could be termed
“reflective-interpretive” fits well with the use of NVivo. The software package does not force
the use of certain data analysis strategies, but provides various tools for the researchers which
they can choose based on their research goals and ways of approaching their data.

NVivo as a Tool

The data were analyzed using a qualitative data analysis program, QSR NUD*IST
(Nonnumerical Unstructured Data Indexing Searching and Theorizing), also called NVivo
which was launched in May 1999 (the screenshots used in this paper are from version 8 and 9
Now, NVivo has an updated to version 9). NVivo was chosen as best fit for the study as well
for the researcher’s ease of use of the program. More specifically, these reasons are:

The structural design of the software. One who sees NVivo’s main menu for the first time
may assume that this is a very smart program to deal with. However, as the first impression
fades away, some of the terms used in the NVivo, help to uplift and creates a learning curve.
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In fact, this is the case in most of the other qualitative data analysis software reviewed. It
takes some time to understand some basic concepts like links, nodes, memos, and attributes,
sets, classification, queries to get acquainted with the terminology, and learn how to use some
important functions like coding, searching, uncode or developing a model using graphic
features of the software. However, once the basic features are understood, the process of
analyzing large amounts of qualitative data becomes much easier and more powerful than
manual approaches.

The nature of the research study. NVivo is a powerful way to do sophisticated data coding
and it supports several ways to build theories, either local or more general. These capabilities
fit well with this study’s research goals and the approach to data analysis. NVivo also enabled
the researcher to look at coded segments of the data in context so that it was possible to
explore coded passages without separating them from the material before and after. NVivo
was also very helpful in easily organizing different data types and sources used in the study.

NVivo Basics
NVivo has three main menus: Navigation menu,detail and list view menu supported by
ribbon where the icon laid.
Snapshot 1. Navigation menu is the place where one can create, edit, view, manage,archieve
and explore project documents. Using NVivo it is possible to create and work with different
kinds of documents as much as its needed, either in internal or external source. For example,
documents can be created or imported from a computer hard disk into NVivo (internal
documents) and (external documents). Before this, documents is to convert them into rich
text or plain text format in order to work with them in NVivo.But its being simplify in NVivo
8 and 9.
Another type of the document is “memos” which is extended notes about the data. All kind of
documents can be coded in NVivo including memos. Writing memos, however, is not merely
a support to the memory of the researcher. It is important because it forces the researcher to
reflect, to make explicit all the ideas, perceptions and decisions that have arisen during
observation and analysis. Writing down and recording these mental leaps in memos is an
important tool for making analysis cumulative. In the Navigating menu all the documents can

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be viewed in a database with short descriptions of each document, the time it was created or
modified, and how many other documents are linked to each document. (Appendix i)

The second menu in Snapshot 2 is List view where we can add new items, open existing
items and edit item properties. And when we open the an item from List View it is displayed
in Detail View. This three main menu are interconnected as working a platform of the data
being coded (Appendix i).

In other words, a node is coded to a related data to the study (In NVivo there are options to
code data: nodes (coded but not categorized nodes), tree codes (codes in a hierarchical
mode), and case nodes (codes categorized under different cases). Using NVivo, it is also
possible to search the documents or nodes in the project. In fact, NVivo has a very
sophisticated search tool which might be very useful while working with a group of
researchers or while dealing with very large data files. The second step is to utilize the
models feature and draw visuals based on the patterns, or any other relationship researchers
wish to see based on their data.

Relationships development. It was also very useful to look at the data emphasizing the
relationships within it. Using NVivo, it was easy to do cross-case analyses, to re-order the
codes and add memos about potential relationships to files, and to “play” with the data. The
advanced features of NVivo helped to develop concepts and do complex thinking about the
data. The sophisticated search option of NVivo, for example, allowed the researcher to
explore complex ideas and connect it in a quickly and easily mode. Even the data being
coded can be automated into model feature or even can be displayed in many forms like Tag
cloud, clusters, 3-Dimension features one of new innovative features in NVivo, which is
more meaningful)

Time Consumed As we know doing qualitative research need patience, perseverance and
tolerance. It evolve and time consumed especially during the development of pattern of the
phenomenon studied. As it progress, analyzing of data occurs and it is a “to and forth”
process as its also potrayed as nonlinear and recursive activities. In this condition, NVivo
helped to automate and speed up many data management and analysis tasks. To some
researchers, this might be the most important feature of any computer program. Most QDA
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programs provide tools to organize data, help shape the data in ways researchers reflect upon
it, and give opportunities to see data from different angles; and all these happen in seconds.

Rigor and thorough as it progress. Overall, NVivo was very helpful while building a
rigorous database for the data analyzed. It demonstrated very clearly all the data coded and
the way it had been coded. The relationships explored by the researcher among the data
sources could be seen easily in the menus of NVivo. Also, the management of these long data
files was very easy using NVivo. These were the things that helped increase the rigor of the
entire data analysis process. Welsh (2002) emphasizes another important feature of NVivo in
terms of its adding rigor to the qualitative studies; search facility that enables researchers to
interrogate their data. “However, the software now is also a useful tool addressing issues of
validity and reliability in the thematic ideas that emerge during the data analysis process”

Most researchers have no problem with the idea of being rigorous; a rigorous study is
regarded as thorough, as opposed to sloppy, and purposively complete, as opposed to
haphazard. Qualitative researchers, however, commonly avoid the term, because in
qualitative research, overemphasis on rigidity of the study resists its adaption to discovered
meanings. Rather than thorough, “rigorous” may be seen as meaning undiscriminating,
treating all experience as the same. Rather than ensuring completeness, a fixed research
design can impede discovery from the data.

Qualitative Rigor

Qualitative researchers, however, are very alert to the risks of inadequate and unpersuasive
research. They evaluate their work by criteria for qualitative rigor usually expressed in
different terms from those for a survey or experimental study. We localized the analysis
process in such a way we believed it will add rigor and data meet its validity suggested by
Meriam (2009) that to enable the researcher to be “experience near” it has to be done in a
way the data were collected through multisource or multitechnique;
Data were collected through triangulations of non participation observation, face to
face depth interviews and documents that include vignettes, reflection, memo writing
and daily lesson plan

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Peer check on the verbatim of the transcript from the view of participants itself being
conducted as to validate the data continously
Collaboration with the participant being set from the initial of the project till the data
meet the saturation point.
As always being in the researcher frame of work the validity aspects were put front in
every steps of the fieldwork this is done when researcher itself will remain unbiased
on any incidens during collection of data, so the data being stored are specifically
picturing the events in the field.
Much of the work done are archieve, manage and blend into NVivo file and it helps in
breaking up the data into their specific themes under Nvivo navigator’s icon/button

Qualitative techniques for ensuring rigor include the following:

Framework scope. In qualitative research design, principles of rigor require ongoing
assessment in the scope of study (which, unlike a predetermined sample, changes constantly)
and the fabric of the data (the sources, richness, adequacy, persuasiveness, and complexity of
the records studied). Analogically, it is said that analysis is just like a loom that facilitates the
knitting together of the tapestry. As a matter it reduce and limit the weaver’s error.

Assessing completeness. Rigor in such respondents involves are reliable, strict application
of a prior design but persistent,thorough revisiting of a problem or theme with constant
comparison of cases.The study need to look at every angle of the phenomenon studied within
the theoretical framework.

Establishing saturation. Perhaps the most dramatic development of qualitative coding has
been the ability of software to support exploration of context and dimensionalizing of
concepts. These methods enhanced the rigor of code-based analysis, supporting claims that
the themes adequately represent the data and “dimensionalizing” of a concept.
exhaustion of sources: -little information of relevance gained if prolong engagement
Saturation of categories- continuing data collection will only gathereed tiny
increments of new information
emerging of regularities – sufficient consistencies in the data that had been developed
and the phenomena is represented
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Overextension- a new information is far removed from the central core of viable
categories that have emerged and does not representing the phenomena

As (Wolcott,1994,b) claimed that working closely through emic perspective is to ensure the
real picture from the perspectives of the participant view because it will describe feeling that
they experienced thus to produce a rich thick description (Geertz, 1973) and according to
Merriam (2009) rich, thick description provide enough description so that readers will be able
to determine how closely their situations match the research situation and hence findings can
be transferred

Computer Solutions to the Time Challenge
Time framework : Speed and Qualitative Research
Such challenges require not the condensing or dodging of analytical processes, but the
efficient handling of those that qualitative researchers, however, are very alert to the risks of
inadequate and unpersuasive. Qualitative research faces 3 particular challenges of speed:

Data collection. Computer tools cannot remove the time required to conduct a narrative
interview, but they can support rapid assessment of the adequacy of records and automate
processing. An indicator of the relevance of questions asked or the appropriateness of sites
studied can be rapidly obtained as documents can be viewed, reported on, and profiled.
.
Data preparation For the researcher in a hurry, the labor of qualitative data “collection” are
high compares dramatically with data-collection methods that are not face-to-face. Any of the
methods of making qualitative records, focus groups, in depth interviews, field research, take
substantial time, even for small scale research. Even the first generation of qualitative
computer tools remade qualitative coding for many researchers. Coding on paper was boring,
burdensome work, more clerical than creative. All computer software for qualitative research
supports coding, and it is always faster than the same task done manually. Some software can
effectively remove descriptive coding tasks (autocoding by command file or section coding in
NVivo codes all the answers to a question, or everything said by a respondent). Demographic
or other background data can be input by table import from spreadsheet or statistics package.
Interpretive coding is easier, swifter, and more visual.

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Pursuit and validation of conclusions. :Arrival at conclusions in qualitative research is rarely
rapid, and in most studies undue haste risks superficial or incomplete analysis. Significantly,
qualitative software has met resistance from researchers to the sorts of searching is supported.
But the processes of pursuing conclusions and establishing their robustness are helped by
software tools that provide ways of gaining rapid access to data. Text search or keyword
search are mechanical processes that can support interpretative goals by providing all relevant
data for consideration. In NVivo 9, an innovative tools to display the excessive work done
and looking at the themes’ frequency of the data gathered by clicking the button and the
display in either at matrices, three dimension feature, three charts or tag cloud and data
cluster. It means the fear for not finishing aren’t happen in NVivo because the anxiety in
looking at the finishing parts is high. As conclusions are pursued, researchers can command
iterative searches through different areas of the data to hasten assessment of explanations or
create live matrices offering a new sort of assessment of patterns. These and all other profiles
of data can be exported to statistical software or spreadsheet in Excell if this is appropriate.

Computer-aided Reliability
Reliability

Reliability in social research usually refers to the assertion that a measurement procedure
yields consistent scores when the phenomenon being measured is not changing. If reliability
requires exact replication, this will be difficult, arguably impossible, to achieve in a
qualitative study, because all qualitative methods require situated study of changing ideas and
behaviours. Not surprisingly, therefore, qualitative researchers frequently express concern at
the concept.

Positivist notions of reliability assume an underlying universe where inquiry could, quite
logically, be replicated. This assumption of an unchanging social world is in direct contrast to
the qualitative/interpretative assumption that the social world is always changing and the
concept of replication is itself problematic. Such negativism about positivism has branded
qualitative research in some areas as defiantly unreliable. What is reliability in qualitative
research?

Qualitative Reliability
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Qualitative researchers have clear standards for reliability. Reliable studies have methods of
making and interpreting data that are transparent, properly documented, and clearly adequate
to the question asked and the claims made. However the concept (like “validity”) has been
seen as problematic, and there are few texts in which techniques for establishing reliability
are set out

Techniques for ensuring qualitative reliability are emphasized as mentoned by Merriam (
2009):;

As we looked into the table below some of the flow of the procedures done in the NVivo
file to show the rigorous process and it is supported and determined by a scholarly principle
in data analyzing.

Coding reliability. Ways of establishing the reliability of interpretation as through coding by
many researchers of the same data or one over time Qualitative researchers would rarely
expect identical coding across coders or across time, because the goal is to learn from the
data, but differences, especially gross differences in coding require discussion,
interpretation,and often concept development. Software can assist the researcher with this
task, though it is almost impossible to do manually. (N9 provides for automating viewing of
areas of difference and similarity, within specified tolerance, between 2 researchers’ coding
of the same document or 2 coding processes by the same researcher at different times.)
Comparison of coding patterns provides a firm basis for concept clarification and team
training and is necessary for a claim that coding is reliable.

Triangulation. Ways of showing data from different sources and technique but lead to the
same conclusion. This is a (much misused) term for “sighting” a phenomenon by different
methods. It requires the dovetailing of studies, very difficult or takng times to achieve by
manual methods. Its supports coding, and it is always faster than the same task done
manually. (focus-group transcripts) for thorough comparison or detailed searching is
supported by import and export of tables from any table-based software. The
researcher“tells” NVivo what the statistic spackage “knows” about a case or site and can then
use that information in seeking and verifying patterns in the qualitative data. Export of tables
36

permits the output of qualitative analysis to be “told” to the statistics package for further
pursuit. Merging of 2 or more qualitative projects for comparative analysis or collaborative
work is supported by software that investigates all aspects of the databases being merged and
allows the researcher to construct the best fit of projects. (With Merge for NVivo, this ability
is extended to aligning projects in great detail for thorough comparison of their emerging
analyses.)

Auditing and log trails. Ways of accounting or the steps by steps in analysis, the crucial
processes of theory emergence and theory construction. Most qualitative research bases
claims to reliability on the ability of the researcher to show clearly how a concept was
developed and discovered, its recurrence in the data traced and place in a growing theory and
how its significance was investigated. The researcher using computer software can log
emergence of a category, date memos or other documents, archive images of analysis at each
stage. (In NVivo, the researcher can create and edit a memo telling its history and trailing its
occurrence in other data by hyperlinking documents and coded data.) This can provide full
documentation of how the category grows in significance and is tested through the data.

Therefore, it can be said that the NVivo package provided a tremendous help in the data
analysis process and some facilities of the software helped increase rigor in terms of data
management. The researcher used the term “validity and reliability” appropriate terms for
qualitative research studies as many scholar in qualitative has been using for decade
(Merriam 2009, Creswell 2005). The things that ensured the validity of the conclusions in this
research study such as triangulation of data sources, extended or long term collaboration
experience in the environment, and researcher journaling had nothing to do with NVivo
software but the way this study was conducted by the researcher. The material used are kept
and managed effiently in NVivo.

CONCLUSIONS

Using CAQDA especially NVivo in qualitative data analysis have strong standards and
positive mechanisms of rigor and efficient. However, the difficulty of achieving these
standards and unevenness in research outcomes may come from te user and how they deal
with it. If steps and procedure were used properly and systematically it will lead to a
37

successful work. This paper has identified computer assisted techniques, but it was beyond its
scope to assess and critique them or to discuss the unanticipated consequences of rapid
methodological change. These include dramatic increase in the acceptability of qualitative
research in areas where it is not taught and hitherto has not been widely accepted. The need
for appropriate literature in these areas is urgent. So too is the need for a full and critical
discussion of the impact of these changing techniques and the directions of software
development. Thus, outreaching software user in qualitative research should be done
continuously and in timely it will develop.

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