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Qualitative Data Interpretation
It is the process of analysing and understanding non-numeric data. This type of data is unstructured and often
comes from interviews, surveys, observations, or textual content. Qualitative data tells us about the emotions
and feelings of people. Qualitative data interpretation is focused on insights and motivations of people.
Data Collection Methods in Qualitative Data Interpretation
Data collection methods in qualitative data interpretation involve techniques such as interviews and observations
to gather rich, descriptive data for detailed analysis, fostering a deeper understanding of complex human
experiences and behaviours. Some methods are as follows:
• Record keeping: This method utilises documents that are reliable and well curated and other similar sources of
information as the data source that are verified and maintained. It is similar to going to a library.
• Observation: In this method, data is collected by observing the participants, their behaviour and emotions,
carefully.
• Case studies: In this method, data is collected from case studies.
• Focus groups: In this method, data is collected after a group discussion on topics of relevance.
• Longitudinal studies: In this data collection method, data is collected on the same data source repeatedly over
an extended period of time.
• One-to-one interviews: In this method, data is collected using a one-to-one interview.
Open Ended Surveys and Questionnaires
Open ended surveys and questionnaires allow organisations to collect views and opinions from respondents
without meeting in person.
Steps to Qualitative Data Analysis
The five steps involved in qualitative data analysis are:
1. Collect data: Gather qualitative data through various methods to understand people’s experiences, opinions,
or behaviours. This is done through interviews, surveys, observations, or documents. For example, a researcher
interviews patients about their experiences with a new healthcare app, recording their responses for further
analysis.
2. Organise and connect the qualitative data: Prepare and arrange the collected data in a systematic way
to make it easier to work with. For example, the researcher transcribes the recorded interviews into text
documents and organises them by participant or interview date.
3. Set a code to the data collected: Assign labels or codes to different parts of the data to identify
themes, patterns, or categories. For example, the researcher reads through the interview transcripts and
highlights sections discussing "ease of use," "technical issues," and "benefits of the app," tagging them with
corresponding codes.
4. Analyse your data for insights: Examine the coded data to identify deeper patterns, relationships, and
insights. For example, The researcher groups codes related to "ease of use" and "technical issues" into a
broader theme of "user experience" and analyses how these themes impact overall user satisfaction with the
app.
5. Reporting on insights derived from analysis: Present the findings clearly, using quotes and visual aids to
support your conclusions and recommendations. For example, the researcher writes a report highlighting
the main themes along with positive and negative feedback.
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