Data And Its Analysis English Language Essay

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Data and its analysis are an integral part of any form of research (Blumberg and Schindler 2011:280). Likewise, when data is organised, analysed and interpreted, it becomes useful information for a researcher (Hussey and Hussey1997:149). In the same light, the Organisation for Economic Co-orporation and Development (OECD:2001) defined Data analysis as ' the process of transforming raw data into usable information, often presented in the form of a published analytical article which to adds value to the statistical output ' The aim of this coursework is to critically explore the qualitative method of data analysis from the social science perspective.

To begin with, there would be an identification of the different methods of data collection and their modes of analysis, under which special attention will be given to the qualitative form of data analysis. Subsequently, this report shall point out some advantages and disadvantages of these forms.

Many business research authors have given their criteria for choosing a qualitative approach to data. Hair et al (2000:3) posit that research in an unfamiliar topic with little or no theoretical basis should tilt towards qualitative approach of data collection and analysis; Collis and Hussey (2009:143) in a more recent publication claim that, the context in which a research is carried out and its influences whether demographic, economic or religious -contextual framework- should be the basis for a qualitative approach to data analysis. However, Collis and Hussey's (2009) reason for using qualitative data analysis is more valid, as it was recently published and gives a fresh tone to contextualisation in research.


Sekaran (2003:223) insists that data collection method is a basic part of any research; he went ahead to suggest different methods of data collection, which include; Interviews, Questionnaires, Observation, Motivational techniques. However, Hair et al (2007:192) advice that the technique of data collection should be dependent on the essence and aim of the research 'if the study is exploratory the researcher collects narrative data through the use of focus groups, personal interviews or by observing behaviour or events' (Hair et al, 2007:192-193; Hussey and Hussey, 1997:55) gave a new twist to data collection by mentioning that there are positivistic and phenomenological approaches to data collection, however they went further to attribute the former to quantitative and the latter to qualitative data collection.

Ticehurst and Veal (2000:20-21) comment that quantitative approach lays more emphasis on large numerical and statistical forms of data analysis, while qualitative concerns itself with accumulation of relevant information about a precise group in order to make inferences about them. Smith, Thorpe and Lowe (2002:117) have commented on qualitative research saying that "it requires both a clear explanation of how the analysis was done and conclusions reached, and a demonstration of how the raw data was transformed into meaningful conclusions"

As a result of the scope within which this report is given this paper would concentrate on the qualitative type of data analysis


Maanem (1983:9) as cited in Smith et al (2002:85) suggests that Qualitative analysis is the arrangement of obtained information, in order to understand its implications to the social research topic. The defects in the quantitative method of data analysis forcefully induced the need for qualitative methods as suggested by Alvesson and Deetz (2000:49), they also went further to explain that while British and Swedish social researchers were tilting towards Qualitative data analysis, countries like the United States, still depend on quantitative method of data analysis. For such reasons, this report is very important because many authors explain that the qualitative method of data analysis usually fits into a social science or anthropological study, which in turn fits into the aims of this course work; more like fixing round pegs in round holes

From the stand point of Robson (1993), as cited both in Hussey and Hussey (1997:55; Collis and Hussey 2009:163), the major challenge faced by qualitative data analysis, is that 'there is no clear and accepted conventions for analysis corresponding to those observed with quantitative data' the concurrence to the statement made by Robson (1993) by set of authors at different times can be understood, as one author is repeated twice in the both publications. Hair et al (2007:193) states that there are two major methods of qualitative data collection which are interviews and observations. These collection methods are repeated in the methods given by (Sekaran 2003:223) above.

According to Brennan (2005) cited in an online publication, the major analytical considerations to make while undertaking a qualitative data analysis include "words, context (tones and inflections), internal consistency, frequency and intensity of comments (counting, content analysis), specificity, trends/themes, and iterations"

With reference to the purpose of this report, in this section would enumerate and critically examine the different types of qualitative data analysis, whilst also taking into account the merit and demerits of each method as it concerns social research.

Bernard and Ryan (1998) cited in[online], explains that a qualitative research is usually characterised by 3 types of data, which are Audio, Text and Video, the most frequently used would be the 'Text' therefore according to Bernard and Ryan (1998), the methods of qualitative data analysis include classical content analysis, Schema Analysis, Analytic induction, ethnographic decision models etc. However, Leech and Onwuegbuzie (2007) identified seven tools for qualitative data analysis, these include:

Constant Comparison Analysis

Key Words in Context (KWIC)

Word Count

Classical Content Analysis

Domain Analysis

Taxonomic Analysis and,

Componential Analysis

This report will pay exceptional attention to four tools of the seven given above, and another one from a different source.


Domain can be described as an area or region that is under the control of a person or group of persons, therefore domain analysis could mean a study of an area, the forces controlling it and the people in it. 'The researcher is moving from observing a social situation to discovering the cultural scene' (Jackson and Verberg, 2007). However, according to Leech and Onwugbuzie (2007), this method gives a more explicit description that 'Domain analysis utilizes semantic relationships to help uncover domains. Domains are created from (a) cover terms (concepts; Y), (b) included terms (referents; X), and (c) a semantic relationship between the cover term (Y) and the included terms (X)' or better still x is a kind of y; x is the result of y; x is a part of y. For example: a research topic titled 'the importance of a Masters' degree to an International student' and a data expert would be:

'Sometimes I wonder why I left my warm country with my lovely friends and family, to come to Britain where the weather is extremely cold, where I barely understand the British accent, tand then I think of the greater good, my bright future; I, a Managing Director with a secretary and a luxury car and a lovely home in the capital city of my country and I smile. That is the reason why I am here'

Using domain qualitative data analysis tool, it can be inferred that:

Possible Cover term: masters' degree Y

Possible included term: Britain, bright future, managing director X

Possible semantic relationship: X gives a better insight into Y

Therefore the semantic relationship for this data would be X is the included term (an example would be 'bright future') and Y is the cover term of a 'Masters' degree'. In this same vein, a research question can be coined as 'why do international students bother to acquire a masters' degree from Britain?'

With the reseacher's understanding of the domain analysis, the following were made about its weaknesses and strengths


Domain analysis pays too much attention on analysing data within the created domain, without considering other factors outside the domain. For instance, in the example above, domain analysis does not take account of the respondent's emotional state of mind.


It helps the researcher to narrow the scope of his findings; it easily brings out the required information.


Taxonomy simply put, is the specification and classification of life, it is mostly done in science concerning animal and plants classification. Meanwhile, taxonomic analysis in social science research prides its' self with a better understanding of the domain analysis suggests (Spradley, 1979). In similarity, it can be said that both domain and taxonomic tools for qualitative data analysis seek to establish or prove a semantic relationship between terms whilst analysing data, in a different view, Spradley (1979) cited in leech and Onwugbuzie, (2007) goes further to identify subsets in a domain, and explains the chain of relationships and how they ultimately impact on the domain itself. According to Spradley (1979) 'taxonomic analysis always has a "substitution frame"'

Therefore using Spradley's insight, a taxonomic analysis from the domain analysis research question generated above, which is" why do international students bother to acquire a masters' degree from Britain?" and the substitution frame would be "explaind"


Britain explains master's degree

Bright future explains master's degree

Managing director explains master's degree

With these subsets, a social science researcher will be able to coin more specific questions about a master's degree program to an international student for instance, "why do most international students choose to acquire a master's degree from Britain?"


It flows from domain analysis; therefore it has the same weakness, which is, paying too much attention to its subsets and little or no attention is given to the factors affecting the subsets created.


It creates a smaller subset, thus: a better understanding of a theme under research.


Word count literally means a mathematical exercise of finding the number of words in a statement or sentence as the case might be. However in a social science research perspective it holds a more deep translation, the propagators of the word count tool for qualitative data analysis such as Pennebaker et al (2003) suggest that there is a hidden meaning or interpretation behind every word that a person says or writes. Therefore if a person tries to convey certain message using words that mean something other than the literal translations of their original context; this means the repetition of these words for emphasis sake should draw the attention of the researcher to a very salient message being conveyed. In fact, this point is more expressed by Bernard (2010 retrieved online) "the researcher evaluates the frequency and co-occurrence of particular words or phrases in a body of textual data in order to identify key words, repeated ideas, or configuration of words with respect to other words in the text"

Furthermore, Leech and Onwuegbuzie (2007) posit that word count can easily be done by identifying words, and counting the number of times it is repeated by a respondent.

Using the statement above, word count analysis was carried, and was discovered that the word "I" was repeated 7 times, while the word "my" is repeated 3 times. Therefore out of 83 words, the word "I" accounts for 5% in comparison with "my" which only accounts for only 2%. Therefore it is reasonable to infer that the major reason why international students come to Britain to obtain a Master's Degree is for everything ranging personal fulfilment, personal benefitting, personal accomplishments and everything personal, as insinuated by the results of the word count analysis exercise.


The word count analysis in its concentration of number of times words occur, does not take into account the concept of tautology and how to address it.


It brings quantitative measures to the analysis of qualitative research, which may distort the results of the research.


Content analysis as the name implies, is making inferences based on what is said by a respondent. Collis and Russey (2003:255) comment that "content analysis represents a formal approach to qualitative data analysis" this implies that it is a scientific method to qualitative data analysis.

Also, according to Sommer and Sommer (1991) as cited in Tharenou et al (2007:252) have defined content analysis as a" technique for systematically describing the form and content of written or spoken material" this suggests that content analysis can be used to analyse data in form of a video, audio, or text interview, this point is reiterated by Easterby et al (2002:121) which points out that there are two methods; one method is interested in interpreting communications and conversations between two or more people, thus "Conversation Analysis" and Discourse Analysis which according to them, "uses texts such as newspaper articles, computer conferences or advertisements as the basis for analysis"

According to Silverman (1993) in Collis and Hussey (2003:255), the first stage in content analysis is 'Sampling' sampling is an act of selecting a small size out of an aggregate population for research, in order to make general inference about the aggregate population.; the second step is to determine a 'Coding Unit' Taylor and Gibbs (2005) have defined coding as "combing the data for themes, ideas and categories..."

The last step is the formation of 'Coding frame' which according to Silverman (1993) is the vertical enumeration of the coding units, so as to allow the horizontal addition of information and frequency.

Strengths of content Analysis

According to (kabanoff and Holt, 1996; Kabanoff et al ) in Tharenou et al (2007:262), the strengths of using content analysis includes that:

'It describes organisational values unobtrusively', which implies that it is simple and easy to understand they also comment that 'it allows a systematic and quantitative approach to dealing with qualitative data' this includes the use of frequency and the numeric calculations in content analysis, in my opinion from I think many researchers would prefer the content analysis method because it does not involve any face-to-face contact with the respondents as it involves the analysis of secondary data.

According to the Psychology Press (2004) "the greatest strengths of content analysis is that it provides a way of extracting information from a wealth of real-world settings."

Weaknesses of content analysis

According to writing @csu the disadvantages of content analysis are that 'it is time consuming' in the same vein, Easterby-Smith et al (2002:119) shared the same view on the time consuming nature of content analysis. Writing @CSU has also identified other weaknesses of content analysis, which include that:

tends too often to simply consist of word counts.

Often disregards the context that produced the text, as well as the state of things after the text is produced.

Semiotic Analysis

"'Semiosis' is the process of extracting messages and significance from signs, and thereby generating meaning" (Hackley, 2003:162) It was introduced by the Swiss linguist Ferdinand de Saussure, who commented basically that there is more meaning to what is not said (signs) than what is said (words) the first things that comes to peoples' minds when they see a sign for example the 'Caution sign' implies that one ought to be careful around that environment. Manning and Collum-Swan(1998) comment that for a sign to make an impact, it must be said in a context, this would make it easy to understand, for example, if a musician flashes the "A-OK sign" it could mean be interpreted as an illuminate sign, but if a commercial advert carries the same sign, it could then be interpreted to mean that the product is perfect for consumers, this is better explained by Hackley (2003:162) "the meaning of signs is arbitrary" which indicates that except a context of culture, demography or religion or interpretation is given to a particular sign, it could be misinterpreted.

Semiotic analysis is usually used by advertising agencies or online marketers to measure the impact a sign has on a particular socio-logical group and how to manipulate it for their business purposes, this is further emphasised by (Bell, 2010) who comments that "Semiotic Research sets out to understand the social and cultural influences on behaviour"

Semiotics is connected to qualitative data analysis in that, qualitative research basically involves interview-based data, video and audio data that contains texts in different languages and signs, which serve as a bedrock for semiotic analysis. based on my understanding of semiotics, the following strengths and weaknesses were inferred:


Semiotic analysis helps a researcher to expose intrinsic details about a respondent's non-verbal answer through signs which actually says more. As it is popularly said 'Actions speak louder than words'


The different cultural connotations and interpretations of signs can lead to alterations of research results, for instance a researcher who misinterprets an Indian respondent's nodding to mean 'yes' would have altered the results of his research