Methodological Approaches in Information Science Research on Computer-mediated

Communication

Anis Pervez

Adjunct Faculty, Department of Media and Communication, Independent University Bangladesh (IUB),

Dhaka, Bangladesh;

Email id: anispervez@gmail.com.

Pearl: A Journal of Library and Information Science, Year: 2015, Volume: 9, Issue: 1, pp. 37-43

ABSTRACT

Computer-mediated communication (CMC) research in information science is progressively using

interpretive theories, which is also the case in the methods used in CMC research. Different of methods

are used including content analysis, discourse and hermeneutic analysis. With the fast diffusion of social

media, where people endorse meaning in different modes of information – text, image, sound – it is likely

that the use of different qualitative methods will increase.

KEYWORDS: CMC, Information science research, Qualitative method, Discourse analysis, Hermeneutic

analysis, Interpretivism, Social media

INTRODUCTION

Computer-mediated communication (CMC) artefacts are primarily text – linguistic and semiotic –

through which information is disseminated over a digital platform among the people who have access to

it. Access is both instrumental (having access to the Internet) and cognitive (extracting or constructing

meaning from the artefacts transferred by the net). CMC research in information science involves a

spectrum of theories as detailed by Pervez (2014) and as well as varieties of methods and research

techniques involving both quantitative and qualitative inquiry. They include, but are not limited to,

descriptive (Gibbs, 2008; Kruger et al., 2005; Stefanone and Gay, 2008) and inferential statistics (Meraz,

2007), content analysis of frequency measures of patterns occurrence (Chen et al., 2005), the description

of meaning (Walker, 2006), online interviews (Meho, 2006; Papacharissi, 2007), case study (Maor, 2003),

and textual analysis by hermeneutics (Lee, 1994). Researchers have also used mixed methods (Herring et

al., 2005; Papacharissi, 2007).

METHODOLOGICAL DIVERSITY

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Literature review shows an increasing use of qualitative methods for investigation of meaning, with

researchers employing ethnography (Menon, 2007), rhetorical criticism (Benson, 1996), and discourse

analysis (Curran et al., 2003). Discourse analysis’s significance in CMC research is evident in Herring’s

work (2003, 2004), where she developed techniques for discourse analysis in the specific context of

CMC, labelling it computer-mediated discourse analysis (CMDA). Among many techniques, CMDA uses

descriptive statistics on language complexity and conducts pragmatic analysis of speech acts to unravel

meaning in the text and its embedded ideological and social references.

According to Monberg (2005), ‘Over the past decade computer-mediated communication, as a

distinct intellectual focus has developed an analysis of the cultural histories, technological rhetorics, and

diverse kinds of online landscape emergent from advanced new communication technologies.’ Marra

(2006) discussed several research methods for assessing the content of CMC for online collaborative

learning. Although she found that the most common methods for evaluating the content of online fora

were frequency counts and other similar quantitative measures, she suggested, ‘In order to assess any

meaning that results from these discussions, it is necessary to perform some kind of semantic analysis of

them.’ Semantic analysis focuses on interpretation of the content (Walsham, 1995).

Interpretivist philosophy aims to provide an account of events and phenomena in terms of how

the people involved perceive and understand their own experiences instead of simply quantifying what

happens. Interpretivism is also a method for carrying out empirical studies in the light of interpretive

philosophy and theory. Lee (1991), in his discussion of the interpretive approach to organisational

research, referred to the procedures associated with ethnography, hermeneutics, phenomenology, and case

studies. Interpretivism is often contrasted with positivism. According to Lee (1991, p. 343), ‘the positivist

approach involves the manipulation of theoretical propositions using the rules of formal logic and the

rules of hypothetico-deductive logic, so that the theoretical propositions satisfy the four requirements of

falsifiability, logical consistency, relative explanatory power, and survival’ (italics in original). These

methods of investigation attempt to explain how things operate in the physical world but do not cater to

the views and opinions of the people who have a cognitive and emotional attachment to the world in

which they live. Max Weber, in introducing the concept of Verstehen (understanding), paved the way for

investigating how people interpret their worlds and their own acts. According to Natanson (1963), by

introducing the sociology of interpretation Weber argued that the primary task of the sociologists is to

understand the meaning an act has for the actor himself. Since then many different theories, approaches,

and methods for interpretive investigation have been developed; a similar proliferation is evident in CMC

research.

Pervez (2014) discussed various theoretical approaches used to address artefacts found in the

online landscape, specifically in asynchronous CMC. The literature also indicated a range of

methodological approaches undertaken in CMC research, which are presented in the following table.

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Table 1: Methods used in information science CMC research

Mode Author Methods/techniques

Email Meho (2006) Email interview

Lee (1994) Hermeneutic interpretation

Stefanone and Gay

(2008)

Descriptive statistics (frequency measure)

Kruger et al. (2005) Descriptive statistics (quantitative comparison)

Thompson (2008) Content analysis (identifying recurring patterns

and themes)

Meij (2007) Statistical measurement of attitude

Ducheneaut and

Bellotti (2003)

Contextual inquiry by interview

Tao (1996) No method of inquiry

Online collaborative

learning

Chen et al. (2005) Content analysis

Hara et al. (2000) Content analysis (Henri’s model)

Maor (2003) Case study

Chernobilsky et al.

(2005)

Mixed method (traditional and ethnographic

approach to studying activity systems)

Shih and Swan

(2005)

Questionnaire survey

Gibbs (2008) Descriptive statistics

Blog Walker (2006) Content analysis

Herring et al. (2005) Quantitative social network analysis, qualitative

analysis of blog dyads

Papacharissi (2007) Content analysis (quantification) and informal

interview

Scott (2007) Content analysis (quantification)

Meraz (2007) Descriptive and inferential statistics

Rutigliano (2007) Quantification of behaviour trends, and qualitative

analysis

Here are two mostly used methods in CMC research done by information scientists.

Content Analysis

Content analysis is a popular method in communication research that ‘analyses the denotative

order of significance. … It works through identifying and counting chosen units in a communication

system’ (Fiske, 1982). Content analysis is currently used in a number of disciplines, including marketing,

media studies, literature, rhetoric, psychology, sociology, political science, and gender studies. It is also

frequently used in information science (Julien, 2004). According to Allen (1990), ‘the term “content

analysis” denotes a family of research methods that attempts to identify and record the meaning of

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documents and other forms of communication systematically,’ it has two distinct meanings in the

information science literature. One denotes a rigorous and quantitative analysis of artefacts leading to

conclusions about their content. The other meaning of content analysis denotes the discussion of the

content of the text. Therefore, content analysis is known to have the flexibility (White and Marsh, 2006)

to support both quantitative and qualitative inquiries.

Pratt and Pratt (1995) distinguish conceptual analysis from relational analysis. Conceptual

analysis helps to establish the existence and frequency of concepts most often represented by words or

phrases in a text; and relational analysis examines the relationships among concepts in a text.

Issues of reliability and validity are crucial for content analysis. Reliability is reproducibility,

which means consistency among two or more coders working on the same data or one coder repeating his

or her work after a period of time (Tinsley and Weiss, 1975). The validity of a content analysis means

correspondence between the categories and the conclusion (Berelson, 1971). Validity is necessary to

allow the researcher to generalise from the findings.

One may use content analysis in CMC research to examine what patterns are prevalent in the

artefacts and what they might mean. The nature of the research question will determine whether to use

conceptual or relational content analysis, or a combination of the two. By doing content analysis of video

footage of bowling game, Hsieh and Chen (2005) unravelled the semantic relationships between player

information, game-related information, and video content information. This is an example of analysing

the forms of moving images using both conceptual and relational content analysis.

Hara and colleagues (2000) used Henri’s (1992) model for CMC content analysis, which deserves

special attention because ‘Henri’s model defines not only the types of skills and interactions demonstrated

in online postings, but also attempts to qualitatively define the nature and content of online interactions

that evidence cognitive development and meaningful learning. Henri’s model has provided researchers

with a structure for many ensuing qualitative analysis models’ (Marra, 2006).

Discourse Analysis

Discourse analysis also explores meanings behind text or discourse. It came to the attention of

scholars in the humanities and social sciences in the late 1960s and 1970s. Discourse analysis can be

defined as ‘the study of words and signifiers, including the form or structure of these words, the use of

language in context and the meanings or interpretation of discursive practices’ (Putnam and Fairhurst,

2001). Information science scholars began adopting this research method in the first half of the 1990s.

Frohmann (1994) identified the relevance of discourse analysis for information science while commenting

on the discipline’s user-centred focus:

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The shift of LIS’s theoretical attention from information system to information user is especially

suited to questions of the role of LIS theories in the discursive construction of specific identities for

information, its users, and its uses. A benefit of the shift to users is that it problemetises, rather than

stabilises, the related notions of information users and information needs. When users are forced into the

centre of theoretical vision, questions arise of how their identities, and especially their needs, are

constructed in theoretical discourses. Precisely how are users of information positioned as subjects in

user-centric information theories?

Advocating discourse analysis’s importance for information science research, Frohmann (1994)

added that this method ‘permits analysis of the ways in which information, its uses, and its users are

discursively constructed, especially in theoretical discourses in Information science, such that power over

them can be exercised in specific ways.’ Budd (2006) describes two varieties of discourse analysis: one is

interpersonal – focusing on dyadic or group settings – and requires lexical comprehension as well as

commonality of belief among the communicators; this is a complex kind of discourse. The second variety

is more formal – focusing on speeches, the writing of articles and books, the publishing of results, and

artefacts and activities – and is practised in a wide range of disciplines, including information science.

Budd advocated the relevance of discourse analysis for both informal and formal information exchange

settings. Nahl (2007) employed discourse analysis to analyse text that was produced by people when

discussing their information practices. She attempted to understand people’s micro-information

behaviours. Talja (1999) used discourse analysis in unravelling information contexts.

Communicative events such as writing and conversation, all of which have forms, are examples

of discourse. Discourse analysis works with a huge range of variables, including intonation, gesture,

syntax, style, lexicon, rhetoric, meaning, speech, act, moves, strategies, turn taking, and other aspects of

interactions. It also analyses the relations between text and context, discourse and interaction, and

cognition and memory. In other words, discourse analysis serves an encompassing arena of action,

interaction, and construction of meaning. Herring’s (1994) work on identifying politeness in computer

culture by analysing texts of online chat, i.e. artefacts, is an example of how to explore value, in this case

politeness, in an online conversation. It shows the possibility of identifying forms in a discourse in order

to understand the meaning people endorse through an information environment, such as CMC, that

facilitates information dissemination and construction.

Qualitative content analysis and discourse analysis may look similar because they both tend to

reveal the meaning of text. Nevertheless, they are fundamentally different in that content analysis presents

immediate snapshot descriptions whereas discourse analysis goes beyond that. In Budd’s (2006, p. 75)

words, ‘discourse analysis addresses more than utterance. It is aimed at speech (parole), inasmuch as

speech is historically situated, occurs at a point in time, and is engaged in by numerous individuals.

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Speech, therefore, embodies epistemological, rhetorical, communicative, obfuscatory, political, cultural,

and other intentions.’

CONCLUSION

This review has explored the methods used in information science research on computer-

mediated analysis. Aligned with the findings of Pervez (2014) identifying the most frequently theoretical

perspectives such as media richness theory, hermeneutics, constructivism, activity theory, and elaboration

likelihood theory, most of the methods used in CMC research are of qualitative nature. In their preface to

the Handbook on Research on Computer Mediated Communication, Kelsey and Amant (2008) offered a

broad definition of CMC:

Computer-mediated communication can be described as any form of information humans present

or exchange by means of a computer. This information can be imparted to oneself, to another person or

group of people, or even to an imaginary audience. Likewise, CMC can be a one-to-many or one-to-one

transaction, a synchronous (real time) or asynchronous (time delayed) process and involve modes of

interaction as diverse as typed text, spoken discussions, or visual/video messages. The types of software

affecting CMC are numerous and increasing every day. Email, text messaging, video and audio players,

social networking web sites, wikis, syndicated feeds, bulletin boards, and blogs are just some of the

software enabling people to communicate.

Such a broad definition of CMC reflects and supports the extension of CMC through its adoption

of social media. This has widened CMC’s horizon, even as users find new ways to construct, disseminate,

and use information for various purposes – personal, social, and governmental. It is worth noting that the

significant number of CMC research is devoted to linguistic and discourse analysis, with attention to

semiotics. Semiotics is a proven approach for artefact analysis (Benthall, 1993; Andersen, 1990, 1991).

As the way social media is expanding, and the way people from all walks of life is sharing information of

all kind – text, image and sound – it is perhaps an indication and one can argue as a need, that more and

more investigation will adopt discourse analysis and other qualitative methods. For such methods are able

to track and understand the meaning created and shared over computer-mediated communication.

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