International Interdisciplinary Journal of Education – January 2013, Volume 2, Issue 2
USING NVIVO FOR DATA ANALYSIS IN QUALITATIVE
RESEARCH
AlYahmady Hamed Hilal Saleh Said Alabri
Ministry of Education, Sultanate of Oman
ABSTRACT
_
Qualitative data is characterized by its subjectivity,
richness, and comprehensive text-based information. Analyzing
qualitative data is often a muddled, vague and time-consuming
process. Qualitative data analysis is, the pursuing of the
relationship between categories and themes of data seeking to
increase the understanding of the phenomenon. Traditionally,
researchers utilized colored pens to sort and then cut and
categorized these data. The innovations in software technology
designed for qualitative data analysis significantly diminish
complexity and simplify the difficult task, and consequently make the
procedure relatively bearable. NVivo, the qualitative data analysis
software developed to manage the ‘coding’ procedures is considered
the best in this regards. This article is devoted to demonstrate the
methods in which NVivo can be employed in qualitative data
analysis.
Keywords: Coding, data analysis, Nvivo, qualitative research,
survey.
Qualitative Research in Education
Qualitative research has become widely accepted across a
wide range of education science, including administrative,
curriculum, and psychology research. This wide acceptance
of qualitative research in education is attributed to large
extent to the advantage of this type of research. Unlike the
quantitative approach, qualitative inquiry is a method of
research that describes phenomena based on the point of view
of the informants, discovers multiple realities and develops
holistic understanding of the phenomena within a particular
context [1]. It has been acknowledged that properly
employing the qualitative data gleaned from face to face
interviews, field observation and document analysis can lead
the researcher to gain a deeper understanding of the problem
than merely analyzing data on a large scale [2].
By this approach, the researcher can figure out the
knowledge, skills and attitudes pertaining to the studied
phenomenon.
That is because the qualitative research
"provides information about the “human” side of an issue,
―that is, the often contradictory behaviours, beliefs,
opinions, emotions and relationships of individuals" [3]. One
more advantage of the qualitative research is that this
approach has its magnitude in the developing countries taking
into account their situation. [4] contend that:
In predominantly oral cultures the advantages of
personal fieldwork, in-depth interviews and observation are
most significant. [Yet,] there remains a tendency in many
developing countries for research and policy planning to be
based on a system perspective that still neglects the realities
of schooling in an everyday context. [4]
There are several justifications for adapting the
qualitative approach within the educational field, yet many
researchers still avoid using this type of research due to its
analyzing difficulty. There is, however, a growing body of
literature devoted to simplify analyzing qualitative research in
education. The main goal of this article is to pave the way in
using the software in analyzing qualitative data.
Data Analysis
Qualitative data analysis is a “process of bringing order,
structure and meaning to the mass of collected data” [5]. Such
process is not an easy task. It is disordered, hard, and time
consuming, even though it is an innovative and captivating
method. Qualitative data analysis is, in fact, pursuing the
relationship between categories and themes of data seeking to
increase the understanding of the phenomenon. Thus, rather
than being strict and procedures-based, the researcher is
required to be alert, flexible and positively interact with data
collected [6].
Since the qualitative data are text-based, the corner stone
of analyzing these data is the coding process. Codes
according to [7] are “tags or labels for assigning units of
meaning to the descriptive or inferential information
compiled during a study”. Codes often adhere to chunks of
words, phrases, sentences or the entire paragraph. Coding
involves pursuing related words or phrases mentioned by the
interviewees or in the documents. These words or phrases are
then combined together in order to realize the connection
between them.
Conventionally, coding was done by hand, utilizing
colored pens to sort and then cut and categorize these data. In
some cases researcher would photocopy each transcript on
different colored paper (i.e. interviewee 1 on red, interviewee
2 on blue etc.) and then pertinent phrases are cut from the
script using a pair of scissors and arrange them into heaps.
Alternatively, the researcher could use the highlighting
function in the word processor to highlight the text he or she
is interested in, once more a different color for each
interviewee and then bring them together in an electronic file
[5], [7]. This task in most of the cases actually is muddled,
vague and time-consuming process.
Using NVivo in data analyzing
Given the innovations in software technology, electronic
techniques of data coding are gradually being more employed
to obtain rigor in dealing with such data. Moreover, using a
International Interdisciplinary Journal of Education – January 2013, Volume 2, Issue 2
computer basically “ensures that the user is working more
methodically, more thoroughly, more attentively” [8]. Thus,
qualitative researchers are encouraged to pursue employing
this tool as much as possible in their works.
NVivo, a Qualitative Data Analysis (QDA) computer
software package produced by QSR International, has many
advantages and may significantly improve the quality of
research. Analysis of qualitative data has become easier and
yields more professional results. The software indeed reduces
a great number of manual tasks and gives the researcher more
time to discover tendencies, recognize themes and derive
conclusions [9]. In addition, NVivo is considered as an ideal
technique for researchers working in a team since it facilitates
combining the work of individuals to come up with one
project together.
Bazeley [8] mentions five important tasks in which
NVivo ease analysis of qualitative data. These tasks include:
- Manage data: by organizing a number of muddled data
documents. That includes interview transcripts, surveys,
notes of observations and published documents.
- Manage ideas: in order to understand the conceptual and
theoretical issues generated in the course of the study.
- Query data: by posing several questions of the data and
utilizing the software in answering these queries. “Results of
queries are saved to allow further interrogation and so
querying or searching becomes part of an ongoing enquiry
process” [8].
- Modeling visually: by creating graphs to demonstrate the
relationships between the conceptual and theoretical data.
-Reporting: by utilizing the data collected and the result
found to formulate transcript reports about the study
conducted.
Before a qualitative researcher starts using the NVivo
software, he or she has to obtain a thorough knowledge and
skills of applying this software. The researcher may pursue
some workshops that emphasize the application and
techniques of NVivo software. In addition, the researcher can
go through tutorials attached with the NVivo software. Those
tutorials offer step-by-step animated displays and the
researcher can commence employing QSR software straight
away. The procedures figured out by [8] in employing Nvivo
software program are useful in analyzing the qualitative data.
These procedures are illustrated in Figure 1.
Figure 1
Procedure Followed in Applying NVivo Software.
Adopted from [8]
Starting a project
*Creating project
*Creating confidential password
*Saving the NVivo project
Working with Qualitative Data Files
*Preparing documents for Import
*Importing Documents
* Document Browser
*Types of Nodes
*Creating Nodes
Working with Nodes
Coding Qualitative Data
*Using the Coder
Going further
*Starting analysis
*Going further with concepts
*Going further with categories
*Going further with themes
*Going further with narrative and discourse
*Employing numerical data
*Going further into drawing figures and sketches.
International Interdisciplinary Journal of Education – January 2013, Volume 2, Issue 2
- Starting a project: The first step in this stage is to create
a project comprised of all the documents, coding data and
associated information that can assist during the analysis
process. Seeking to restrict access to the data recorded the
researcher may create a confidential password in the project
as appears in Figure (2).
Figure 2 NVivo Screenshot of a Study’s Project
- Working with Qualitative Data Files:
Typically, the researcher records interviews
digitally to capture all the details revealed by the
interviewee. He/she is recommended to store the
electronic sound files under a name of each
interviewee. These interviews have to be
transcribed and stored in a word processing
application in order to make them text
- based.
Figure 3
NVivo Screenshot of the Tree Nodes
International Interdisciplinary Journal of Education – January 2013, Volume 2, Issue 2
The next step is to import the files which the researcher
intends to analyze. This procedure is basically done via
steering to the location where the file has been stored, then
picking the appropriate file extension. NVivo automatically
imports the selected documents into the application. Also
using the Document Browser allows the researcher to
recognize all of the text in the imported document.
- Working with Nodes: The function of nodes is to
store a place in NVivo for references to code text. That
means the two types of nodes, tree and free, contain the
all known information about a particular concept or
category “[8]. Two common nodes available are: Tree
nodes and free nodes. Tree nodes are illustrated in Figure
(3).
Figure 4
NVivo sample of the coding process
- Coding Qualitative Data: To code a chunk of data in
a project document under a particular node, the researcher
can highlight the text via the mouse and pull the highlighted
text to the identified node. When the cursor is located over
the node, the highlighted text changes color and the relevant
node linked with the text shows up on the Coding Stripe to
the right of the browser as appears in Figure (4). That
allows assigning multiple codes to the same chunk of the
text as well by going through the same process.
Going further: In this stage, the researcher can go
forward to analyze the data. [8] suggests that further than
just code and retrieve in the context of those basic
principles of continues analysis, creativity tempered by
rigor and care, through documentation and flexibility along
with disciplinary awareness. At this stage, the researcher
should focus on the techniques for utilizing the available
tools productively and analytically. These techniques
facilitate the development of the concepts, categories and
themes, as well as going further with the narrative and
discourse pertaining to the study. At this stage also, the
researcher can employ several numerical data obtained
during the study conducted. These numerical data can
sometimes be demonstrated as figures and sketches to
facilitate the reading of the findings as appear in Figure (5).
International Interdisciplinary Journal of Education – January 2013, Volume 2, Issue 2
Figure 5:
NVivo sample of an early category catalogue displayed as a sketch
Summary
NVivo has various advantages and may significantly
improve the quality of research. Analysis of qualitative data
has become easier than ever before and yields more
professional results. The software greatly reduces manual
tasks and gives the researcher more time to discover
tendencies, recognize themes and derive conclusions. In
addition, NVivo is considered as an ideal technique for
researchers working in a team since it facilitates combining
the work of individuals to come up with one project. This
software has an advantage in managing data and ideas,
querying data, modeling visually and reporting. All in all,
qualitative researcher is strongly advised to pursue the
procedures of this software in order to ease the muddled,
vague and time-consuming task. Employing qualitative
approach in education is postulated to develop
fundamentally to parallelize with the fast advancement of
Computer Assisted/Aided Qualitative Data Analysis
(CAQDAS).
References
[1] Glickman, D., Gordon, P., & Ross-Gordon, M. (2007).
Supervision and instructional leadership. Boston: Allyn
& Bacon.
[2] Malakolunthu, S. (2007). Teacher learning in
Malaysia: problems and possibilities of reform. Kula
Lumpur: University of Malaya Press.