Qualitative data analysis is a process by which meanings and conclusions are drawn from unstructured and heterogeneous data that are not squeezed numerically or quantifiably. We will explain what qualitative data analysis is and how it is carried out, the different qualitative research techniques and the software tools available.
Qualitative data analysis: what is it and what is it for?
Qualitative data analysis is a dynamic and creative process that allows us to extract knowledge from a mass of heterogeneous data in textual or narrative form. It can come from multiple sources and can be in text, audio, image, or video formats.
The importance of qualitative data lies in the fact that it allows us to gain in-depth knowledge about certain subjective realities, such as, for example, the feelings and motivations of our clients.
In today's context dominated by Big Data, data represents the fundamental asset of companies. The multiplication of data sources, of different formats and provenance makes it very common to have to analyze qualitative data together with quantitative data. However, qualitative data need special techniques and tools for its treatment, which is very different from quantitative data.
Qualitative data collection techniques
In the case of qualitative data, the information is usually obtained through different techniques, the main ones being:
- Observations through video or transcribed notes.
- Open-ended question surveys, which allow us to obtain in-depth details of the respondents.
- Delphi technique, which consists of gathering contrasting opinions from a panel of experts, looking for a point of consensus.
- Focus group, in which it seeks to collect the opinions of a group in a relaxed and informal atmosphere.
- Online communities, in which research is prolonged over time and carried out asynchronously.
- Differences between qualitative and quantitative data analysis.
Qualitative and quantitative data analysis processes differ substantially, because the types of data on which they are based are different:
- Consists mainly of words, while quantitative data consists of numbers.
- Tends to take up more size.
- It's a much longer and more complicated process with respect to quantitative data analysis.
- Answers the questions Why? How? While the quantitative looks for the How many? Where? And when?
- In the case of qualitative data, it is not always possible to carry out a statistical analysis.
Quantitative data analysis usually pursues objective results, while qualitative data obtains subjective and relative conclusions.
Qualitative data analysis phases
This process consists of different phases, although it can be an iterative procedure or successive approximation. In this regard, the main phases include:
- The discovery and collection of data.
- The preparation, review and transcription of the data, if any, to text.
- Also it includes the organization of the data according to criteria, which can be chronological, thematic, etc.
- The categorization, labeling and coding of the data, which prepares it for analysis.
- The analysis of the data and generation of hypotheses, theories, conclusions, etc.
- Software for qualitative data analysis.
Traditional qualitative data analysis in linguistics and sociology was done entirely by hand. Fortunately, we now have at our disposal different software tools that can facilitate the work when analyzing massive data and heterogeneous sources, both textual and images, videos and audio.
These tools allow us to easily extract patterns from the data that answer the questions of our analyses.
In this article we have seen what qualitative data analysis is and its importance when it comes to obtaining conclusions about subjective realities, which a quantitative analysis cannot always discover.
What procedure is followed to analyze qualitative data?
Typically, qualitative data analytics require more time to process and sort the data than for its collection. Likewise, qualitative analysis is traditionally a slow process, especially compared to quantitative data analysis.
Qualitative data analysis techniques, therefore, approach them as an object of analysis. We find different techniques to analyze words or phrases, a task that actually begins with the collection, because for this techniques such as free lists, paired comparisons or, for example, classification in piles are used.
These collected data are analyzed from different analysis techniques, either through exponential analysis, mind maps or, say, taxonomies and, in each of them, in turn, we find different methods.
It is a complex activity, whose ultimate goal is to give the data meaning. For this, very diverse varied procedures are used, which are rarely statistical. Not surprisingly, qualitative data analytics is considered more of an art than a technique.
Your choice will depend on our goal, as well as the most suitable tasks and operations. The overall process of this type of analysis begins with a selective collection of the data, followed by a reduction for identification, classification, synthesis and grouping.
The advantages of the software
These general procedural steps are carried out either manually or using software programs that offer us different very advantageous tools, although in both cases the principles of the analytical process are the same.
In the case of the most advanced solutions, suitable for the management of big data, the methodologies of qualitative data analysis will vary in one way or the other. We can have functionalities related to the search, organization, categorization and qualitative analysis in structured and unstructured data.
From texts or images, for example, to objects, videos or audio, with which we work to find patterns that offer answers to the questions raised. A task entrusted, as is well known, to experts in qualitative and quantitative data analysis in order to make better business decisions.
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