Qualitative research is defined as “the study of the nature of phenomena”, including “their quality, different manifestations, the context in which they appear or the perspectives from which they can be perceived”, but excluding “their range, frequency and place in an objectively determined chain of cause and effect”. (Philipsen. H,Vernooij-Dassen. M, 2007)
This formal definition can be complemented with a more pragmatic rule of thumb: qualitative research generally includes data in the form of words rather than numbers.
In order to understand why people act the way they do, qualitative research methods concentrate on the participants' ideas, feelings, reasons, motivations, and values.
Why Qualitative Research?
When addressing complicated subjects, qualitative research is fantastic on its own. However, giving background information utilising quantitative facts can give a broader and wider knowledge of a topic. Quantitative research might not be sufficient in these circumstances.Having a richer understanding of the topic and data collected means a better chance at answering the larger question ‘why’. Once again quantitative research might not be enough here. Finding out about a customer's experience through health research studies can point up areas where services can be improved in service sectors where customers are vital, such as private health care.
Information that cannot be quantified, counted, or simply stated using numbers is referred to as qualitative data. Data visualisation technologies like word clouds, idea maps, graph databases, timelines, and infographics are used to communicate the information that is gathered from text, audio, and visual sources.
Two types of unstructured qualitative data are frequently mentioned: interpretative and ethnographic. To better understand how a group provides context to an occurrence, ethnographic data is gathered. To comprehend a person's unique experience and feelings regarding the event, interpreting data is gathered. However, how is all this data collected?
There are around six primary methods used by researchers to collect qualitative data and conduct qualitative research.
A one-on-one interview, also known as a qualitative interview, is a research method used in qualitative studies where a participant must be approached personally and extensive data must be acquired. These interviews are performed in a conversational or discussion format and typically include follow-up questions.
Comparing one on one interviews to generic questionnaires or focus group studies, the latter are more impersonal forms of study. Such formats frequently incorporate follow-up and open-ended questions. There are three types of these interviews; structured, semi structured, and unstructured.
In a focus group discussion, a researcher brings together a group of people to talk about a certain subject with the goal of eliciting the participants' complicated personal experiences, beliefs, perceptions, and attitudes through mediated interaction. The rise of participatory research, particularly the "active experimentation with focus groups" conducted in the academic social sciences in the 1980s, is directly related to the method's popularity.
The method originated as a qualitative data gathering approach and a bridge between local knowledge and scientific study. There are five types of focus group discussions; single, two way, dual moderator, duelling moderator, and respondent moderator. Mini and online are the two recently growing types of focus groups discussions.
Keeping records is like visiting the library: you go through books or other reference materials to gather pertinent information. As a data source for this strategy, already extant, trustworthy documents and related sources are used.
Every piece of reference information a company gathers could serve as the basis for a later investigation. Businesses may give employees instructions to save specific papers in case they need to use them for qualitative research analysis. The information they gather can help businesses grow financially, cut down on waste, and draw in new clients.
Using their five senses—sight, smell, touch, taste, and hearing—researchers gather data through observation. As it is dependent on the researcher's sensory organs, it is a subjective way of information gathering. This method only works with data you can view with your senses; it does not entail quantitative measurement. The researcher can study, engage with, and develop a rich image of participants in their natural context through observation. The methods, culture, or subjects of the study can all be better understood thanks to this form of data collection. Social scientists, sociologists, and psychologists frequently employ observations to get a deeper knowledge of both human and animal behaviour.
Studies that track specific individuals over an extended period of time—often years or decades—use continuous or recurring measures. They are typically observational in nature, collecting data on any combination of exposures and results without the use of outside influences. This research design is very helpful for assessing the causal link between risk factors and illness development as well as the effectiveness of therapies over various time periods. Similar to this, as information is gathered for specific individuals within a predetermined group, proper statistical testing may be used to examine changes over time for the group as a whole or for specific individuals.
For academic scholars interested in qualitative research, the case study technique is the most popular (Baskarada, 2014). Researchers can perform a thorough examination of complex phenomena within a particular environment using the qualitative case study methodology. When choosing a method for their research, research students often choose the case study without considering the wide range of variables that can influence the results.
Any misunderstanding of the study purpose, the methodology's application, or the validity of the findings could have unanticipated negative effects because performing research requires a lot of time and resources.
Framework of Qualitative Analysis
Qualitative data is unstructured and has greater depth as compared to quantitative data, which records structured information. It can provide us with the answers we seek, aid in the creation of theories, and advance knowledge. However, it is challenging to analyse qualitative data. While tools like Excel, Tableau and PowerBI compress and visualise quantitative data with ease, there are no such mainstream tools for qualitative data.
Qualitative data analysis (QDA) is a process of gathering, structuring and interpreting qualitative data to understand what it represents.QDA aims to provide answers to inquiries regarding the activities people perform and the factors that influence those behaviours. Because it entails the analyst; reflecting, gathering and processing this type of data might take time. A qualitative researcher or qualitative analyst is someone who works with qualitative data. Businesses frequently analyse client feedback using qualitative data. The term "qualitative data" in this context typically refers to verbatim text data from sources like reviews, complaints, chat messages, support centre encounters, customer interviews, case notes, or social media comments.
Once the data has been captured, there are a variety of analysis techniques available and the choice is determined by the specific research objectives and the kind of data gathered. These techniques are content analysis, narrative analysis, discourse analysis, thematic analysis, and grounded theory.
Whichever technique is chosen, knowing the basic process of QFD is important. Though before learning the basic process, there are some preparations that one needs to be clear with.
It's crucial to be very clear about the nature and scope of the research topic in order to get the most out of the analysis process. This will make it easier to decide which methods of data gathering will be most helpful in addressing the research issue.
The method for analysing qualitative data will differ depending on whether a company wants to comprehend client sentiment or an academic is polling a school.
There is a sequence to follow when this is made clear. And while there are some distinctions between the methodologies, most of the phases in the process are the same.
The following phases are part of the Framework Method for analysis of qualitative data which has been used since the 1980s. The Framework Method is one of many analytical techniques that are together referred to as thematic analysis or qualitative content analysis. These methods start by highlighting similarities and contrasts in qualitative data before concentrating on the connections between various data points in an effort to derive descriptive and/or explanatory conclusions grouped around themes. For use in extensive policy research, researchers Jane Ritchie and Liz Spencer from the Qualitative Research Unit at the National Centre for Social Research in the UK created the Framework Method in the late 1980s.
Organise the Collected Data The researcher can examine the responses to each topic and particular question separately after organising and showing the data, which makes it simpler to identify concepts and themes.For organisation of data, the most basic thing to go for is to record the data. Once data is recorded, transcription and translation of the data along with writing it in a formal manner is the next step. Labelling of the content should be done parallel to formally writing the data.
Otherwise, returning to the questionnaire is the best approach to organise the data. Determine which questions and subjects must be addressed and which were only put in the questionnaire as significant but not necessary. The organisation of the data should make it simple to look at and for the researcher to go through each topic and identify topics and themes. Making a chart out of all the information from the transcript is one approach to accomplish this. The next step for the researcher is to identify the framework after the data have been organised.
Identification of Framework The structure that can hold or support a research study's theory is known as the theoretical framework. The research subject under study's underlying theory is introduced and described in the theoretical framework.
In order for the entire research to make sense and the research problem to be valid in their relevant field, intense reading to understand the data is required. As the data is read, the researcher needs to identify the structure that supports the theory on which the study of the research is based. This provides a solid ground for the researcher to work on and defend the existence of their research problem. The explanation of the research question or the investigation of the data may serve as the foundation for the framework identification.
Sorting the Data Into the Framework Once we have our organised and consolidated data in one place and our framework identified and drafted, sorting the collected data into the framework is the natural next step.
Sorting the data into the framework basically refers to moulding and phrasing of the data in a systematic manner so that it fits into the structure that forms the supportive base of the research study.
Descriptive Analysis Based on the Framework Descriptive analysis is a sort of data analysis that aids in describing, demonstrating, or summarising data points in a helpful manner so that patterns may develop that satisfy all of the conditions of the data.
During the sorting of the data according to the framework naturally repetitive or similar concepts might emerge from different answers or data collected for the same study. One task of the framework is to provide insight into the range of responses and the recurring themes of the data. This means that the framework helps in identification of all the themes that emerge from the collected data and the recurring ones. Identification of recurring data helps in further consolidation and compression of data so that the crucial points discovered are rightfully highlighted and not glossed over. This will result in the study being more to the point and systematic in nature. In a special case of exploratory research, simply identifying this pattern will mark the end of the research.
Second Order Analysis
Remember how we talked about how identification of recurring data helps in compressing and consolidating the data? The last step of QDA extensively deals with this activity. Formally explained, when one is unable to obtain a complete description and explanation of the observed event directly from the informants, second order analysis is used.
Using this analysis method, the events from the data can be organised in a sequence that can help in obtaining an answer to the research question. This answer will help in developing a hypothesis that could be tested which would help in taking the research further.
The data gathered is analysed using second order analysis because this method might help in identifying useful signs that could point to an analytical explanation. This is one of the basic frameworks to be followed when using qualitative data analysis for maximum efficiency and satisfactory results.
Future of Qualitative Research and QDA Most qualitative data analysis is still done by hand. Manual qualitative data analysis is changing due to two new trends. First, improvements have been made in natural language processing (NLP), which aims to comprehend human language. Second, user-friendly software created for both researchers and enterprises is proliferating. Both aid in automating the study of qualitative data.
Traditionally, but not always, qualitative data is loaded into CAQDAS software for coding when using the manual analysis approach. Coding is the process of labelling and arranging your data so that you can later recognise themes and the connections among these topics. Coding is the process of identifying significant words or phrases and categorising them according to their significance. Early in the new millennium, developers like ATLAS.ti, NVivo, and MAXQDA popularised CAQDAS software, which researchers quickly embraced to help with the coding and organisation of data. This is however still a manual process since the data needs to be still typed manually.
We still need to come up with proper codes and frequently manually assign codes to pieces of feedback even in systems that speed up the manual coding process. However, there are also methods that automate both the finding and the use of codes. Machine learning innovations now allow for the automatic reading, coding, and structuring of qualitative data.
The feedback is coded and organised into themes much more quickly and easily thanks to automation. The software uses natural language processing (NLP) to detect meaningful statements by scanning through sentences and phrases.Some automated systems identify patterns that repeat themselves and assign codes to them, while others require you to train the AI by giving it examples. You could claim that the AI figures out the feedback's meaning on its own.
These automated methods become effective for obtaining high-quality business or research insights when combined with sentiment analysis and advanced text analytics.
Fully automated analysis of qualitative data is becoming more and more popular among businesses because it is less expensive, quicker, and equally accurate.
According to the most recent GRIT research, the use of online qualitative techniques like in-depth interviews and focus groups will significantly grow in 2020, rising by 12% and 20%, respectively. 20% of research budgets have historically gone toward qualitative methodologies. We observe that researchers who might have anticipated conducting 20–30% of their research through online interviews this year are now anticipating conducting 70–80% of their research online due to the adoption of digital qualitative research, increased consumer access, the capacity for scaled research, and the cost savings over in-person, work. The pandemic will eventually pass, but the digital revolution will continue.
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The Article "Qualitative research methods and data analysis" has been contributed by Harshita Poddar and Peer Reviewed by Sanika Sharma.
Harshita Poddar is a third-year student pursuing BA Honours in Applied Psychology at Amity University Kolkata. She received her High School Diploma in Humanities from Mahadevi Birla World Academy.
Sanika Sharma is a Psychology Graduate. She is an avid reader and a curious learner. She is inclined towards a holistic understanding of concepts and thus inclines towards interdisciplinary research.
They are a part of the Global Internship Research Program (GIRP). GIRP is an IJNGP initiative to encourage young adults across our globe to showcase their research skills in psychology and to present them in creative content expression.