This form of data helps in making real-life decisions based on mathematical derivations. Quantitative data is used to answer questions like how many? How often? How much? This data can be validated and verified. The following are examples of quantitative data.
Create a free account. Qualitative data is important in determining the particular frequency of traits or characteristics. It allows the statistician or the researchers to form parameters through which larger data sets can be observed. Qualitative data provides the means by which observers can quantify the world around them. For a market researcher, collecting qualitative data helps in answering questions like, who their customers are, what issues or problems they are facing, and where do they need to focus their attention, so problems or issues are resolved.
Qualitative data is about the emotions or perceptions of people , what they feel. In quantitative data, these perceptions and emotions are documented. It helps the market researchers understand the language their consumers speak and deal with the problem effectively and efficiently.
Qualitative data collection is exploratory; it involves in-depth analysis and research. Qualitative data collection methods are mainly focused on gaining insights, reasoning, and motivations; hence they go deeper in terms of research.
Since the qualitative data cannot be measured, researchers prefer methods or data collection tools that are structured to a limited extent. Here are the qualitative data collection methods :.
One-to-One Interviews: It is one of the most commonly used data collection instruments for qualitative research, mainly because of its personal approach. The interviewer or the researcher collects data directly from the interviewee on a one-to-one basis. The interview may be informal and unstructured — conversational. Mostly the open-ended questions are asked spontaneously , with the interviewer letting the flow of the interview dictate the questions to be asked.
Focus groups: This is done in a group discussion setting. The group is limited to people, and a moderator is assigned to moderate the ongoing discussion. Depending on the data which is sorted, the members of a group may have something in common. For example, a researcher conducting a study on track runners will choose athletes who are track runners or were track runners and have sufficient knowledge of the subject matter.
Record keeping: This method makes use of the already existing reliable documents and similar sources of information as the data source. This data can be used in the new research. It is similar to going to a library. There, one can go over books and other reference material to collect relevant data that can be used in the research. This is known as the process of observation. Besides taking notes, other documentation methods, such as video and audio recording, photography, and similar methods, can be used.
Longitudinal studies : This data collection method is performed on the same data source repeatedly over an extended period. It is an observational research method that goes on for a few years and, in some cases, can go on for even decades. This data collection method aims to find correlations through an empirical study of subjects with common traits.
Case studies: In this method, data is gathered by an in-depth analysis of case studies. The versatility of this method is demonstrated in how this method can be used to analyze both simple and complex subjects. This approach not only analyses conversation, but also takes into account the social context in which the conversation occurs , including previous conversations, power relationships and the concept of individual identity.
It may also include analysis of written sources, such as emails or letters, and body language to give a rich source of data surrounding the actual words used. This looks at the way in which stories are told within an organisation or society to try to understand more about the way in which people think and are organised within groups.
This is largely used in ethnographic research. It assumes that conversations are all governed by rules and patterns which remain the same whoever is talking. It also assumes that what is said can only be understood by looking at what went before and after.
Conversation analysis requires a detailed examination of the data, including exactly which words are used, in what order, whether speakers overlap their speech, and where the emphasis is placed. There are therefore detailed conventions used in transcribing for conversation analysis. Like content and grounded analysis, discourse, narrative and conversation analysis can be considered as on a spectrum of systems for analysing forms of language. Which you use will depend on what you want to achieve from the analysis.
There are many computer packages designed to support and assist with the analysis of qualitative language-based data , these include NVivo , Atlas. Their use is beyond the scope of this page, but they are widely used to analyse large quantities of data, reducing the pressure on a researcher to read and code everything him- or herself.
If you think that your research might need to use a package of this type, you are probably best discussing it with your supervisor or a colleague who has experience of using the package and can advise you about its use. Qualitative data refers to non-numeric information such as interview transcripts, notes, video and audio recordings, images and text documents.
Qualitative data analysis can be divided into the following five categories:. Content analysis. This refers to the process of categorizing verbal or behavioural data to classify, summarize and tabulate the data. Narrative analysis.
This method involves the reformulation of stories presented by respondents taking into account context of each case and different experiences of each respondent. In other words, narrative analysis is the revision of primary qualitative data by researcher. Discourse analysis. A method of analysis of naturally occurring talk and all types of written text. Framework analysis. This is more advanced method that consists of several stages such as familiarization, identifying a thematic framework, coding, charting, mapping and interpretation.
Grounded theory. This method of qualitative data analysis starts with an analysis of a single case to formulate a theory. Then, additional cases are examined to see if they contribute to the theory. Qualitative data analysis can be conducted through the following three steps:.
Step 1: Developing and Applying Codes.
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