In general terms, academic research falls into two definitional brackets, qualitative and quantitative. The latter denotes systematic empirical investigation, using mathematical, statistical or computational methods. As the term implies, quantitative research concerns quantities, numbers, numerical information which may be submitted to mathematical models and theories, allowing a degree of objective measurement across samples. Qualitative research, on the other hand, relates to qualities, the properties and characteristics of a given phenomenon; it resultantly compasses an interpretive dimension, a way of assigning meaning to those trends which quantitative data can locate and define but not explain. Qualitative research consequently lends itself to subjective and discursive analyses: affording more explanatory depth in critique than its quantity-oriented counterpart. A simple way of understanding this distinction is to consider that quantitative research may tell you what is happening (that, for example, 10% of the population is unemployed), while qualitative research helps explains why it is happening (prohibitive illness, deficit in jobs market, institutional discrimination, and so on).
Qualitative analysis is the use of non-quantifiable data (i.e. text based) and makes a subjective judgement on such to come to a conclusion. Due to this qualitative data is classed as richer and more detailed than quantitative data and thus allows the researcher to explore in-depth information. Quantitative (i.e. numerical) data analysis could be used to determine the most common choice of the consumer, however qualitative can be used to explore deeper and understand the reasoning behind this choice (Ezzy, 2013).
Qualitative Analysis then, implies a subjective dimension that fits with an interpretivist research paradigm – the idea that the material world is best understood with reference to the meaning ascribed to it in various human interactions. A Qualitative Report seeks out the underlying socially-constructed meaning by which material phenomena are understood; in so doing, Qualitative Reports reflect a specific research paradigm, that all knowledge of “reality” is, in effect, a social construction. This paradigm is highly common in contemporary academia and underlies, for instance, the positivist and constructivist viewpoints, as an epistemological fundamental. In discussing epistemology we are referring to the nature of knowledge itself, its origins, limits and implications.
In composing a Qualitative Report it therefore follows one is espousing a certain kind of world view, with its own associated beliefs and values. In particular, qualitative research places an onus on the abstract symbolic level of reality, on subjective experience and the potential for multiple interpretations of that experience: as opposed to seeking out one unitary “objective” reality, per se.
A Qualitative Report speaks to the complexities and ambiguities at work in mediating people's experiences of social reality, accommodating the diversity of popular experience. Qualitative methodology allows for an event, observation or activity to be viewed as encapsulating multiple “realities”. Hence the qualitative research paradigm contrasts with the normative assumptions of quantitative research: which hold that reality does exist in some objective fashion and thus awaits only the appropriate critical tools, to be unearthed.
A Qualitative Analysis instead looks to the way the aggregate of individual experience within a society serve, em masse, to effect consensual beliefs. These shared beliefs are taken to reflect “reality” but in fact are best thought of as commonly shared codes, a mutual symbolic register which is referred to in decoding everyday experience. Qualitative Reports thus possess a scope as vast as human experience is diverse, which is why they are used in a broad range of subjects and for a variety of reasons.
Qualitative data analysis can be used in situations where the researcher has a large amount on unquantifiable data such as transcripts, interview notes audio recordings or pictures (Walliman, 2017). The key outcome for the researcher would be to find similarities or differences between responses which could then form the basis of themes and new theory on the topic. The process of analysing qualitative data is usually concerned with the categorisation of the information in a bid to reduce the overall size and highlight the key points. Thus qualitative analysis will usually involve a level of content or thematic analysis which will include research strategies such as coding (Patten & Newhart, 2017).
For instance take a researcher who wants to understand why more consumers are shopping online. The quantitative data is available publicly from the Office of National Statistics showing that online sales as a % of total sales is increasing though this data alone provides no reasoning. It provides a fact but no reasoning as to why this has happened. This is why qualitative analysis should be used; a method to question the impacted population over their actions and understand why more people are shopping online. These findings are an extension to the quantitative data presented above and become of use to other researchers and businesses alike in making decisions over future research or business strategy.
Because of its use qualitative data forms part of grounded theory. This is a methodological approach which results in the determination of new theory which explains the patterns seen in data and thus can be used to help predict what social scientists may find in similar datasets. This process is well used and understanding patterns in datasets of a % of the population allows researchers to generalise how the overall population may act. For instance this is the process used to determine political polls when a small subset of the population has their voting intentions recorded which is then generalised for the overall population. This means that grounded theory is also achievable with quantitative data however as mentioned it is the qualitative data which adds meaning and understanding. In the example noted above this would come from understanding why the person is choosing to vote the way they are. This requires qualitative data which could then be coded based on specific themes such as economic, healthcare, immigration among others. This takes the dataset from being simply a descriptive poll to being researcher which shows the key factors which have influenced this poll and thus provides valuable information which can be used by political parties.
The main benefit is that using techniques such as coding allows the researcher to breakdown the massive amounts of data into smaller chucks of useful information. For instance a business which has collected 100 interviews on customer service may have key codes which can be searched for in the text such as price, delivery, quality. These can be identified to determine similarities between responses with the businesses studying the wider context to influence business decisions (i.e. finding that the majority of responses mentioned a high price).
There is also the benefit that qualitative analysis can be easier to implement than quantitative given there is no need to determine a model or experiment as may be the case with quantitative (Saunders et al, 2009).
The key issue with qualitative data is that it is extremely time consuming for any researcher to undertake. Many researchers employ strategies such as surveys, questionnaires and interviews to collect primary data for their study. Given that surveys are mostly closed-questions the answers are quantifiable making it simpler for the researcher to use Excel, SPSS for the analysis and create graphs and visuals. The use of closed questions also makes it easier for these surveys to be self-administered and thus the researcher can send more out for completion and collect a higher number of responses. With qualitative responses the researcher may need to employ either questionnaires or interviews with open-ended questions which allow for longer and detailed answers. However with this the analysis becomes harder. Excel and other statistical tools are less effective and thus the researcher may need to do a lot by hand, finding key words and picking out key themes from the text (Bernard & Bernard, 2013). Furthermore undertaking detailed interviews over specific topics may mean the researcher must administer the interviews themselves which becomes a time-consuming exercise and takes up a significant portion of the limited time available (Bryman, 2016). So while a researcher could distribute and collect 100 surveys the same would not be possible with interviews in the same time period and so there is an opportunity cost between quantity and quality of detailed response.
Bias can also be present both from the participants but also the researcher. A benefit of an interview process is that the participant is free to elaborate on the ‘WHY’ as opposed to within a closed-question survey where answers are restricted to those provided. However the researcher must then take into account that the participant could be lying, or exaggerating their experience/ reasoning. Having more interviews with others will help overcome this as it will allow the researcher to spot anomalies (Saunders et al, 2009). But there is also the risk that the bias of the researcher could influence both the collection method and the results.
The researcher determines the questions used in the data collection method, while also controls the conversation during the interview. Thus any bias or misunderstanding on their part could lead to inaccurate data collected (Ezzy, 2013). The is also the risk of subjective judgement being applied to the responses by the researcher. This isn’t seen with quantitative data analysis given that two researchers with the same data would come to the same conclusions (i.e. % of retail sales are online). The data is fact. Qualitative responses are subjective and thus two researchers presented with the exact same responses could come to two separate conclusions based on how they perceive the data. Different codes could be used to find similarities and the understanding of the research would also influence what they perceive to be the meaning of the content.
Given this focus on detail and in understanding behaviour or context qualitative data is suited to fields of study within social sciences and humanities.
There are four types of qualitative analysis which can be discussed here; namely content, narrative, discourse and framework. Content analysis involves the categorisation of data to summarise the key points. The content provided to the research can be analysed from either a descriptive or interpretive viewpoint. A descriptive viewpoint would seek to categorise the data to show what the data is. Interpretive goes further and seeks to analyse what the data means (Walliman, 2015). Ultimately this is employed to spot patterns in large bodies of content.
Narrative analysis differs as it focuses more on data which is storied. It seeks to understand the core activity within the stories presented by the individuals and determine similarities or determine how different contexts influenced their differing experiences (Bryman, 2016). The researcher is not just concerned with key words or phases as may be the case with content analysis, but looking to the wider story being told. For this type of analysis to be successful a large amount of rich data is required.
Discourse analysis would be used for analysing the spoken or written language to understand how language is used in real-life and whether the social context in which the language was used impacts this (Saunders et al, 2009). Rather than focusing on small chunks of the text, disclosure analysis is concerned with larger chunks such as the entire conversation to understand how the individual may use language to achieve specific outcomes such as the building of trust, managing conflict or evoking emotions in the audience.
Finally framework analysis is a technique which would provide the researcher with a specific structure to follow when it comes to analysing the data and given such is extremely useful for massive volumes of text (Lacity & Janson, 1994). Five distinct phases can be noted in this analysis as:
1. Familiarisation: Reading the information.
2. Identifying the thematic framework: Developing a coding framework which can be used to breakdown the massive amounts of data into smaller parts of interest to the research.
3. Coding: Being either numerical/ text codes which are used to highlight key parts of the text.
4. Charting: Creation of charts to show the themes.
5. Mapping and Interpretation: Seeking patterns, ideas, associations and explanations within the knowledge provided (Bryman, 2016).
Bernard, H. R., & Bernard, H. R. (2013). Social research methods: Qualitative and quantitative approaches. London: Sage.
Bryman, A. (2016). Social research methods. Oxford: Oxford university press.
Ezzy, D. (2013). Qualitative analysis. London: Routledge.
Lacity, M. C., & Janson, M. A. (1994). Understanding qualitative data: A framework of text analysis methods. Journal of Management Information Systems, 11(2), 137-155.
Patten, M. L., & Newhart, M. (2017). Understanding research methods: An overview of the essentials. London: Taylor & Francis.
Saunders, M., Lewis, P., & Thornhill, A. (2009). Research methods for business students. London: Pearson education.
Walliman, N. (2015). Social research methods: The essentials. London: Sage.
Walliman, N. (2017). Research methods: The basics. London: Routledge.
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