Qualitative data is defined as the data that approximates and characterizes. Qualitative data have to be observed and recorded. This data type is non-numerical also it is collected through methods of observations, one-to-one interviews, conducting focus groups and similar methods. Qualitative data in statistics is also considered as categorical data, that can be arranged categorically based on the attributes and properties of a thing or a phenomenon (D. Silverman 2011, p. 132-137).
Qualitative data is a source of well-grounded, good descriptions and explanations of a human process. With qualitative data, you can preserve chronological flow, see which incidents led to which consequences, and derive resultful explanations. It also leads to serendipitous findings and new integrations; it allows researchers to get beyond initial theories and generate or revise conceptual frameworks. Ultimately, the findings from well-analyzed qualitative research have an overwhelming value. Words, especially organized into events or stories, have a considered, vivid and meaningful flavor that often makes far more convincing to a reader-another researcher, a policymaker, or a practitioner-than pages of summarized numbers (M. Miles 2014, p.214).
Qualitative data is a part of qualitative research. Qualitative research main advantages (C. Grbich 2012, p. 37):
- it provides detailed information and can expand knowledge in a variety of areas.
- it can improve assess to the impact of policies on a population.
- it gives insight into people's individual experiences.
- it helps evaluate service provision.
- it can enable deep exploration of little-known behaviors, attitudes, and values.
Qualitative research supports certain styles of design, selection, and analytic interpretation. The underpinning ideology or belfies system says that (C. Grbich 2012, p. 38):
- subjectivity means that both the views of the participant and those of you the researcher are to be respected, acknowledged and integrated as data, and the interpretation of this analysis will be constructed by both of you
- trustworthiness (validity) us seen as getting to the truth of the matter, reliability is seen as a sound research design and generalisability is local and theoretical only
- a holistic view is important (so the structures affecting on the setting such as policies, culture, situation, and context need to be included
- every study is time-bound and context-bound (replication and generalization are unlikely outcomes)
Examples of Qualitative data
- Gender: Male, Female
- Sentiment: Positive, Negative, Neutral
- Nationality: Indian, American, French
- Brand: Apple, Samsung, Sony
- Employment Status: Employed, Unemployed
- Marital Status: Married, Single, Divorced
- Religion: Hindu, Muslim, Christian
Advantages of Qualitative data
Qualitative data can provide a wealth of information and deeper insights into a particular subject or phenomenon. It can help to get an understanding of opinions, attitudes, motivations and behaviours. The advantages of qualitative data include:
- The ability to explore the underlying reasons and motivations behind the data. Qualitative data can provide in-depth insights that may not be captured by quantitative data.
- It can provide a better understanding of the context in which the data was collected, as well as the circumstances and environment of the people from whom the data was collected.
- It can be used to identify patterns and trends, as well as discover new insights.
- Qualitative data can also be used to uncover new opportunities and ideas that may not have been discovered through quantitative data.
- Qualitative data can be used to validate quantitative data and provide a more complete picture of a research topic.
Limitations of Qualitative data
Qualitative data has some key limitations that must be taken into account when collecting and interpreting data. These include:
- Subjectivity - Qualitative data is heavily reliant on the researcher and the research method used, so it is prone to bias and subjectivity. This can lead to inaccurate results and interpretations.
- Limited scope - Qualitative data is typically limited to a small set of data points, so it may not be representative of the entire population.
- Time consuming - Qualitative data collection and analysis is often time consuming, requiring researchers to spend significant amounts of time conducting interviews, focus groups, and other methods of gathering data.
- Expense - Qualitative data collection can be expensive, as it often requires the use of specialized tools and technologies to collect and analyze the data.
- Difficulty in replicating - Qualitative data can be difficult to replicate, as it relies on the individual researcher and research method used.
- Difficulty measuring - Qualitative data is often difficult to measure and quantify, as it is not numerical. This makes it difficult to compare across different data sets.
One of the approaches related to qualitative data is the use of narrative analysis. Narrative analysis involves the analysis of stories told about events and experiences, as a means of understanding the meanings that individuals attach to those events and experiences.
Other approaches related to qualitative data include:
- Content Analysis: Content analysis is a method of analyzing qualitative data by looking for patterns and themes in the data. It involves examining textual data for topics, themes, and patterns.
- Phenomenology: Phenomenology is a method of qualitative research that attempts to uncover the essential meaning of an experience by focusing on the subjective, lived experiences of individuals.
- Grounded Theory: Grounded theory is a qualitative research method that involves the development of a theory based on the data collected from interviews and other sources.
Overall, qualitative data analysis is a complex and interpretive process that involves the use of various methods and approaches to uncover the meaning and significance of data. The various approaches used in qualitative data analysis provide insight into the complex relationships that exist between individuals, and their experiences and perceptions.
- Bernard H., Ryan G. (2010), Analyzing Qualitative Data: Systematic Approaches, SAGE Publications Ltd, Thousand Oaks
- Gabrich C. (2012), Qualitative Data Analysis: An Introduction, SAGE Publications Ltd, p. 37-38, New York
- Miles M., Huberman M., Saldana J. (2014) Qualitative Data Analysis, SAGE Publications Ltd, p. 214, London
- Sullivan P. (2011), Qualitative Data Analysis Using a Dialogical Approach, SAGE Publications Ltd, London
- Silverman D. (2011), Interpreting Qualitative Data, SAGE Publications Ltd, p. 132-137, London
Author: Szymon Olejniczak