30 Computer Application in Qualitative Data Analysis

Subhasis Bandyopadhyay

1. Introduction

All research in the final analysis is qualitative research. Because, every research project is aiming at some form of value addition to the knowledge domain, and at the same time, contribute to the enhanced understanding of certain aspect of nature. The terms quantitative and qualitative are only indicative of the principal ways by which a particular body of research is carried out, i.e. whether the method is following numerical representation of empirical study or sensitized analysis of the associated concepts, constructs, and theories. An overwhelming majority of the social science research today is mixed method research where both the qualitative and quantitative approaches complement each other during the entire research process.

Insights from qualitative research are having a transformative effect on how we understand and manage our world. A qualitative way of knowing the world can change how we hear and see others; and how we reflect on our own participation in the world around ourselves. The skill sets and knowledge base demanded by qualitative research including technology prowess, artistic aplomb, and methodology acumen challenge us to grow continuously in order to meet the changing demands of academia and the marketplace1.

Learning Outcome

This module would introduce the learner to the uses of computer application in qualitative data analysis. This would make her/him aware of the scope and limitations of computer applications in qualitative data analysis. The particular process of handling qualitative data through machine interface would also pave the way for development of more sophisticated procedure in the form of regeneration of new software with enhanced analytical sense and ease of use.

Use of Computer Software for Analysis of Qualitative Data

An important development in recent time is the arrival of computer software for analysis of qualitative data. Lee and Fielding (1991) first coined the term The Computer-assisted qualitative data analysis software or CAQDAS that perform such analysis. In the 1990s, Non-numerical Unstructured Data Indexing, Searching and Theorizing (NUD*IST) became very popular. However, relatively new entrants in the field are known as NVivo, Atlas/ti or QDA Miner. Most of the best known programmes allow researchers to code text in the computer and retrieve the coded data. In other words, after some simple operation by the researcher, the computer takes over the physical task of writing marginal codes, marking photocopies of transcripts or field notes, cutting out all chunks of text relating to a code, and pasting them altogether (Bryman 2008: 565). Interestingly, these programmes largely differ from the use of quantitative data analysis software in terms of the environment within which they operate.

Despite these developments, there is serious concern about the use of computer software for qualitative data analysis (Bryman 2008: 566-67). Thus, Hesse-Biber (1995) has argued that use of computers for analysis of qualitative data will result in qualitative research being colonised by the reliability and validity criteria of quantitative research. Weaver and Atkinson (1994) feel that the narrative flow of interview transcripts and events recorded in field notes may be lost if fragmentation of textual material is done in the coding and retrieving process. Buston (1997) and Fielding and Lee (1998) also feel that such fragmentation de-contextualise data. The awareness of the context of any research is crucial in any qualitative research. Catterall and Maclaran (1997) have shown that CAQDAS is not very suitable for Focus Group data. This is because, the code and retrieve function results in loss of the communication process that is vital in any FGD. Coffey et al. (1994) have suggested that the new trend of use of computer for qualitative data analysis has introduced a new kind of ‘orthodoxy’ which is inconsistent with the growing need for a variety of representational modes in qualitative research.

Notwithstanding these critics, several writers have preferred to use the available packages on a variety of grounds. As such, computer application (CA) in qualitative research tries to fulfil the twin objectives of scientific research: one, to enhance objectivity in the formulation of research tools; two, to increase the analytical girth of the qualitative researcher without much botheration for developing a standardized platform. CAQDAS can make the coding and retrieval process faster and more effective, and hence, to some it is a “new opportunity” as well. Bryman (op.cit. 567), for instance, took the assistance of NVivo as a tool in the process of qualitative data analysis in his study of visitors to Disney theme parks. To some, these techniques may be helpful in the development of explanations (Mangabeira 1995). It is also suggested that they enhance the transparency of the process of conducting qualitative data analysis as they may force researchers to be more explicit and reflective about the process of analysis.

Nonetheless, it remains only an associative process in the research to help the integrative and personal expertise of the researcher concerned. Qualitative research techniques are definitely more dependent on the subjective subtlety of the strategy and sensitivity developed by the social scientists. The following table lists the advantages of qualitative data in the form of document (source: http://www.esourceresearch.org).

The Advantages of Documentary Data

Advantage	Rationale
Richness	Close analysis of documents reveals presentational subtleties and skills.
Relevance and Effect	Documents influence how we see the world and the people in it and how we act —
	think of advertisements and CVs!
Naturally-occurring	Documents are instances of what participants are actually doing in the world –
	without being dependent on being asked by researchers.
Availability	Texts are readily accessible and not dependent on access or ethical constraints as
	they may be quickly gathered and encourage us to begin early data analysis.

It may additionally be noted that if a researcher has very small data set, it is probably not worth the time and energy to use the new software. Again, if someone does not have easy access to such software, it is likely to be too expensive for any personal use (Bryman 2008: 567). Yet learning the use of new software does enhance the skill base of any researcher for use in future occasion.

Data Processing in Qualitative Analysis

The analytical process in qualitative setting is largely dependent upon the ways researcher would like to communicate her/his findings. However, there are three distinct pathways in which the qualitative researcher can portray the findings (Kumar 2014: 317):

Developing a narrative to describe a situation, episode, event or instance,
Identifying the main themes that emerge from the field-notes or transcriptions of observation and interviews,
Taking effort to quantify the frequency of occurrence of a setting or incidence to denote their prevalence.

Self-Check Exercise 1

Q 1. What is the major function of computer software in qualitative research?

Most of the best known programmes allow researchers to code text in the computer and retrieve the coded data.

Q 2. What are the objectives of a computer tool for qualitative research?

Computer application in qualitative research tries to fulfil the twin objectives of scientific research: one, to enhance objectivity in the formulation of research tools; two, to increase the analytical girth of the qualitative researcher without much botheration for developing a standardized platform.

Grounded Theory

When analyses are generated on the basis of interpretation of recorded data, which are categorized, classified and coded following an inductive process, it is called a grounded theoretical approach. This is a constant comparative method of analysis grounded in an observed empirical database. From open coding through selective and axial coding to content analysis and application of hermeneutics, the qualitative aspiration of the research design gradually paves the way for computer simulation, ordering and systematic reflection within the ambit of value oriented research. Studies in organizational behaviour, pattern of social interactions, and social artifacts are a few domain of research where grounded theory based coding and qualitative computer applications can be potentially used.

Sociologists Glaser and Strauss (1967) in their effort to combine two major theoretical lineages in social theory, positivism and interactionism, arrived at the point of grounded theory that primarily attempts to develop theoretical interpretation on the basis of analysis of patterns within observed data-structure.

Semiotics

Commonly defined as “science of signs,” Semiotics tries to understand how meaning is generated from socially shared symbols. Often employed for analysis of texts, semiotic understanding of the text can be verbal, non-verbal, or both. Semiotics is divided into three broad categories:

Semantics: the relationship of signs to what they stand for;
Syntactic or syntax: the structural or formal relations between signs; and
Pragmatics: the relation of signs to interpreters1.

Systematically theorized for the first time by Swiss linguist Ferdinand de Saussure (1857-1913), semiotic is originally conceived to study the role of sign in social life (see figure 2 for an example of semiotic analysis of colour). In the late 1960s, especially with contribution from Roland Barthes, it began to turn into a powerful approach in cultural studies. The importance of semiotics as a methodological tool in social research is now reflected through codification and categorization of meaningful symbols. It opens up the opportunity for computerized analysis of qualitative data without affecting the interpretive dynamics of the research process in a social setting.

Other forms of textual analysis like rhetorical analysis, discourse analysis, and content analysis have now become important research techniques in social sciences. Particularly the last one, content analysis, besides semiotics, is playing a pivotal role to synthesize computer application within the ambit of qualitative social research.

Finding patterns within the societal context through grounded content or symbolic expressions help machine simulation for categorization and development of searchable database. Hence, both grounded theory and semiotics liberate the opportunity for computerized analysis of economic, social, political and cultural reality.

Coding Process

In qualitative analysis, coding plays an important role to convert textual narratives and descriptive elements into computational categories. In this process, Open coding is the logical starting point where the social scientist read and re-read some text to identify the key concepts contained therein. After the identification, a particular piece of information is broken into several constituent concepts and each of these concepts is given a code to ascertain its relevance to the subject under study. Axial coding comes next in the line. It aims to identify the core concepts (like justice and power, for example) involved in a process by regrouping the open coded data. The last one in this sequence is Selective coding. It seeks to identify the central code in the study: the one that the other codes all related to (Babbie 2013: 398)

Figure 3. Structure of Coding in Social Setting

Source: openi.nlm.nih.gov Obtained from Google Image

Concept Mapping

After taking note of the data coding, generally the social researcher prepares memos to identify the code labels and their meaning. Once this task is accomplished, the researcher embarks upon the task of concept mapping. Concept mapping is the graphic display of relevant concepts in the study and their interrelations that help in the formulation of a projected theory.

The use of visual portrayals like this also contributes meaningfully in data collection as well as organization of data analysis through computation.

Qualitative Data Analysis and Computation

Data in social science research can be collected from computer among other sources. Effective resources are available in the online databases or ethnographic portals. For the purpose ordering and categorization, however, social scientist requires the intermediate step of entering all this data into the computer, before the analysis step can begin. In the recent past, there have been technological advances in text recognition (OCR, or Optical Character Recognition) which is likely to upgrade scanning procedures to take out some of the drudgery of this work. Latest development in information technology has also made way for voice recognition and audio transcription, both online and offline.

Miles and Weitzmann (1995) have divided qualitative software into six essential categories: text retrieval; text-base managers; code-and-retrieve programs; code-based theory-builders; and conceptual network-builders.

Grounded theory-builders go beyond simple code-and-retrieve programs to allow researchers to conceptually organize and categorize their codes; annotate their data with memos; examine conceptual relations; extend their coding schemes; and even test hypotheses about their data (read Module RMS 26). These programmes have much more power than simple code-and-retrieve, but their organizational and conceptual schemes for theory-building also may impose more pre-existent limitations. The last category of software, conceptual network-builders, almost falls into the realm of data presentation, except for the fact that these programs create provisional network output for the researcher to “play with” themselves and manipulate, rather than prepare for display for others. Conceptual network-builders allow people to build and test theory through semantic networks of nodes and links2. The best of these programmes allow the researcher to truly work with concepts qualitatively – moving around elements in the network to see how the rest of the network changes, representing semantic difference through spatial separation, allowing links of variable strength and directionality (Carley and Palmquist 1992).

Because of the movement from static tables to dynamic visualization (fractals, bifurcation trees, virtual reality, etc.) in the sciences, they point out, researchers are beginning to gain a greater appreciation of phenomena as being complex, hierarchical, indeterminate, “holographic,” mutually causal, perspective-dependent, and continuously evolving3.

A good scientific visualization at a minimum allows the viewer to find their own visual patterns in phenomena – rather than requiring the original researcher to point them out – thus changing the collaborative nature of research. Beyond that, it might even allow the viewer to alter the parameters of the representation itself, and see what happens4. Visualizations tend to bring the context and relationships of data to the foreground. It’s not hard to see how this might also help produce a “new paradigm” in the social sciences, and qualitative research in sociology, as well.

Some qualitative researchers feel that the computer biases research toward positivism, quantification, and Western ethnocentrism. After all, the computer processes everything through precise, logical, binary/digital operations – when it is well known that human thoughts and decisions are often based on open ended, quasi-standardized factors like “fuzzy logic,” analogue thinking, and association. It seems too “left brain” and abstract in its operations, ignoring the “right brain” contextual holism that seems to be an important part of cultural and local knowledge. These objections are based on the way computers operate today. Parallel processing, neural networks, analogue circuits, optical memory storage, perceptual-learning systems, and “biochips” (electronic logic gates involving living tissue) could produce computers that not only simulate human thinking, which is simultaneously quantitative and qualitative, but even physically emulate it. The word “computer” is insufficient to consider the evolving nature of these devices. It prevents us from seeing how they can be used qualitatively5.

Software Platforms

Numerous standard software programmes are now available for qualitative social scientific research. Atlas.ti.5; NVivo and QDA Miner are only few of them. These programmes can efficiently search, sort, distribute and process large volume of textual data as defined by the users. The only prerequisite in such cases is development of an adequate and effective coding scheme based on initial manual examination of representative textual source and validation of the coding procedure. Software programmes in qualitative analysis are only effective to that extent where the subjective standardization is meticulously pursued by the social scientists. This brings in the issue of research ethics. Moral conditioning of the social researcher about the right and wrong has become an influential factor in determining whether the rigorous process of elimination of bias in the coding and application has been effectively implemented or not.

Simple word-processing programmes can be put to use for preliminary data analysis in qualitative studies. The “find” or “search” command can easily be a gateway to the passages containing keywords. Additionally, one can type code words alongside passages in the research notes for later retrieval. Database and spreadsheet programmes are often used for qualitative data processing. These packages are often bundled with pre-installed operating system (OS). Figure 5: Example of a spreadsheet in Excel on Evaluation of Satisfaction

Self-Check Exercise 2

Q 1. Mention some areas where qualitative computer applications can be potentially used?

Studies in organizational behaviour, pattern of social interactions, and social artifacts are a few domain of research where grounded theory based coding and qualitative computer applications can be potentially used.

Q 2. What are the different categories of qualitative software?

Computer Assisted/Aided Qualitative Data Analysis Software (CAQDAS)

CAQDAS offers tools that assist with qualitative research such as transcription analysis, coding and text interpretation, recursive abstraction, content analysis, discourse analysis, grounded theory methodology etc.

A CAQDAS programme should have:

Content searching tools
Coding tools
Linking tools
Mapping or networking tools
Query tools
Writing and annotation tools

CAQDAS makes many if not most of the clerical tasks associated with the manual coding and retrieving of data easier and faster. However, it does not and cannot help with decision about how to code qualitative data or how to interpret findings.

There is, however, lack of universal agreement about the utility of CAQDAS. It has been argued that this software reinforces and even exaggerates the tendency for the code-and-retrieve process and result in fragmentation of the textual materials. They also carry the danger of the researcher becoming detached from the findings, and missing some of the less immediately obvious themes that come out of interview. Yet, there are some who found these software extremely useful (read contrasting experiences of students in Bryman 2008: 583).

NVivo

Researchers in social sciences use qualitative data to evaluate, interpret, and explain social phenomena. The computer application known as NVivo can aid social scientists in analyzing data generated by interviews, surveys, field notes, web pages, audio visual material and journal articles. NVivo Analytics is a multi-method tool that does not favour any specific research technique over others. It’s designed to facilitate diverse methods of data collection.

NVivo can help you to manage, explore and find patterns in your data but it cannot replace your analytical expertise6. It should be considered a useful tool, helpful for:

Discovering and Analyzing Constellations of Themes
Demographic Research

Creating a new project (eminently taken from UPenn library resources)

At the first opening of NVivo, the researcher will see a welcome screen that lists all of the most recent projects that have been opened. Notice the ribbon tabs along the top. Also, notice the three buttons along the bottom: New project, Open project and Help. To create a new project, click on the New Project button. A window will appear that allows you to name your project, give it a description and browse to save it in a particular folder.

The ribbon contains all of the commands of NVivo. The commands are grouped into tabs in the ribbon. If you are working with different types of data or specific queries, sometimes additional tabs will open along the top giving you more options. For example, the Analyze tab under ribbon contains the following commands: Coding—code a selection or source at new or existing nodes; autocode; spread coding Uncoding— uncode a selection or source at existing nodes or intersecting content Links—create memo links, see also links, and hyperlinks Annotations—create or delete an annotation Framework Matrices— add and delete summary links or automatically generate summaries from coding.

Coding one’s data is one of the key phases in the whole process of qualitative data analysis and in NVivi it is accomplished through nodes. Nodes are the route by which coding is undertakes. In other words, a node is a “collection of references about a specific theme, places, persons or other are of interest”. Once established, nodes can be changed or deleted. Nodes can take forms like tree node or free node. In a tree node, nodes are held in a treelike structure, implying connections between them. The free node, on the other hand, is independent of any tree and not connected to each other (read, for details including use of NVivo step by step, Bryman 2008: 568-582).

There are several ways of joining the coding process in NVivo. The simple way is to highlight a passage of text in your document, click the highlighted area and drag the selected content to the node in the list. When the drop cursor appears, release the mouse to drop the selection into the node7. Once data are coded, NVivo allows one to very quickly search all these documents including specific text. It should also be noted that Memos (one feature of grounded theory in which ideas and illustrations are stored) can be easily created in NVivo. At the end, one has to save the NVivo project for future use.

Qualitative Data Analysis Programmes (QDA)

There are a few commonly used qualitative data analysis programmes (QDA) with dedicated online resources where one can get acquainted with their use-pattern with the help of downloadable demo version (for details see www.cengagebrain.com). Some of these are as follows:

AnSWR

Ethnograph

HyperQual

Qualrus

A non-exhaustive list of free / open source software for qualitative analysis

Aquad (GPL licence, since version 7) (Windows)
Cassandre (Java-based)
Coding Analysis Toolkit (CAT) (web-based)
Compendium (Java-based)
ELAN (Java-based for Windows, Mac OS, Linux)
FreeQDA (GPL2, Java-based)
LibreQDA (AGPL3 license) (web-based)
QDA Miner Lite, proprietary license, free version with reduced functionality (Windows)
RQDA (Windows, Mac OS, Linux)
TAMS Analyzer (Open source, Mac OS)

Operating systems, version and nature of the platform is given in the paranthesis.

Source: https://en.wikipedia.org/…/Computer-assisted_qualitative_data_analysis_s..

Quality of Qualitative Research

Qualitative social research is trickier than quantitative research on many counts. One major issue among them is the quality of Qualitative Research. However, there are certain standards in research methods which are equally applicable to quantitative as well as qualitative research. Validity and Reliability are two of them. Validity involves the question of whether the researcher is in reality measuring that s/he says s/he is measuring. Reliability is also a reasonable criterion of quality in terms of qualitative research. Reliability is a question of whether a measurement or observation technique would yield the same result again and again in different time and place independently. High reliability in qualitative research is organically related with low-inference descriptors (Seale 1997: 148)

For example, motivation is not measurable in the way income or heights are. Notwithstanding these subtle differences, social scientists have observed conditions or orientations that can be placed under the umbrella concept of motivation. In a qualitative research setting, when the social scientist is designing a schedule to measure motivation, it is important to assess the extent to which the questions asked and answer received actually reflect what people can generally agree to mean by the term. In most areas of social research, however, the concept of reliability is more elusive, because a) the nature of social reality is in an ever changing mode, and b) often the concept carries different meaning in different societies and affects people differently.

Conclusion

The main strength of qualitative research is drawn from its ability to study social conditions and phenomena which are comprehensively missed out in a quantitative endeavour to establish statistical relations between variables. The latter procedure explains a whole load causality that involves input and out factors coming from a phenomena but that is feasible only in the generalized context of some prior measures and without referring to the local constitution.

David Silverman (1997) aptly pointed out that while quantitative research is blind when it does not have access to qualitative research on the local construction of social phenomena, qualitative research is immeasurably strengthened when it is combined with quantitative data.

Self-Check Exercise 3

Q 1. What is CAQDAS?

Computer Assisted/Aided Qualitative Data Analysis Software or CAQDAS offers tools that assist in transcription analysis, coding and text interpretation, recursive abstraction, content analysis, discourse analysis, grounded theory methodology of qualitative research. It is a tool for content searching, coding, linking, mapping/networking, query, and writing/annotation.

Q 2. What is NVivo?

The computer application known as NVivo can aid social scientists in analyzing data generated by interviews, surveys, field notes, web pages, audio visual material and journal articles. NVivo Analytics is a multi-method tool that does not favour any specific research technique over others. It’s designed to facilitate diverse methods of data collection.

Notes

www.nova.edu/ssss/QR/TQR2013/woods_etal.pd
Semiotics for beginners by Daniel Chandler in visualmemory.co.uk/daniel/Documents/S4B
These lines are taken from www2.fiu.edu/~mizrachs/comp-in-qual-research.html
Ibid
Ibid.
library.columbia.edu/indiv/dssc/nvivo_guide.html
http://www.library.upenn.edu/wic-multimedia/tutorials/nvivo10.pdf

Web links

www.what-when-how.com/sociology/computer-applications-in-sociology
www.qsrinternational.com
www.atlasto.com
www.qualisresearch.com
ActionResearch.net
http://www.biograf.org
http://www.surrey.ac.uk/sociology/research/researchcentres/caqdas/
http://www.ncrm.ac.uk/

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References

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