Qualitative Data Analysis

Which of the following best describes qualitative data analysis?

• It focuses on numerical data and statistical inference

• It focuses on understanding meanings, patterns, and themes in textual or visual data

• It uses quantitative models to predict future outcomes

Which philosophical assumption is fundamental to qualitative research?

• Findings must be universally generalized

• Reality is viewed as objective and measurable

• Findings are context-based and transferable rather than generalizable

What does the Emic perspective in qualitative data analysis emphasize?

• Statistical validation of participant responses

• Understanding participants’ experiences and perspectives

• The researcher’s interpretation of events

Which principle of qualitative analysis involves using multiple data sources to validate findings?

• Rigor and Transparency

• Triangulation

• Contextualization

What is the main goal of Thematic Analysis?

• To calculate mean and variance of qualitative variables

• To identify and code recurrent patterns or themes in data

• To test predefined hypotheses using existing theories

Grounded Theory is primarily used to:

• Develop a theoretical explanation of a phenomenon through iterative data analysis

• Measure correlation between quantitative variables

• Summarize large datasets into numerical indicators

Which of the following best defines Text Mining?

• The process of manually reading all text to extract themes

• The semi-automated process of extracting useful information from unstructured text data

• The process of visualizing numeric data only

What is the main purpose of Sentiment Analysis in text mining?

• To determine the grammatical structure of sentences

• To identify whether text expresses positive, negative, or neutral emotions

• To convert qualitative text into numeric values for regression

Which data cleaning technique reduces inflected words to their base or root form while understanding context?

• Stemming

• Lemmatization

• Tokenization

What is one major challenge in sentiment analysis?

• Lack of enough numeric data

• Overfitting of machine learning models

• Irony and sarcasm that can reverse the intended meaning of text