Qualitative Data Analysis

What is qualitative data analysis?

• Qualitative data analysis focuses solely on statistical analysis.

• Qualitative data analysis is the process of converting numerical data into qualitative data.

• Qualitative data analysis involves only looking at data that can be quantified.

• Qualitative data analysis involves examining non-numerical data to understand patterns, themes, and meanings.

Name one common method used in qualitative data analysis.

• Grounded theory

• Content analysis

• Thematic analysis

• Narrative analysis

Explain the process of coding in qualitative data analysis.

• The process of coding in qualitative data analysis is a one-step process

• The process of coding in qualitative data analysis involves categorizing data into themes or patterns through steps like familiarization, generating initial codes, searching for themes, reviewing, defining, and producing the final report.

• Coding involves converting qualitative data into quantitative data

• Coding in qualitative data analysis does not involve categorizing data into themes or patterns

What is thematic analysis?

• Thematic analysis is a statistical method used to analyze data.

• Thematic analysis is a method of analyzing quantitative data by identifying patterns or themes within the data.

• Thematic analysis involves counting the frequency of words in a text.

• Thematic analysis is a method of analyzing qualitative data by identifying patterns or themes within the data.

How is data saturation achieved in qualitative data analysis?

• Data saturation occurs when new information is continuously added to the dataset

• Data saturation is reached when the data is quantitatively analyzed

• Data saturation is achieved in qualitative data analysis when no new information or themes are observed in the data.

• Data saturation is achieved by limiting the amount of data collected

Discuss the importance of reflexivity in qualitative data analysis.

• Researcher biases enhance the quality of qualitative data analysis

• Preconceptions do not impact the validity of qualitative data analysis

• Reflexivity is not necessary in qualitative data analysis

• Reflexivity is important in qualitative data analysis to acknowledge and mitigate researcher biases, assumptions, and preconceptions.

What are some common challenges faced during qualitative data analysis?

• Data interpretation subjectivity, data reliability and validity, time-consuming nature, researcher bias

• Data accuracy, data completeness, data visualization challenges

Explain the concept of triangulation in qualitative data analysis.

• Triangulation is only used in quantitative research

• Triangulation helps ensure the validity and reliability of qualitative research by cross-verifying information from different angles.

• Triangulation refers to the process of eliminating data points in analysis

• Triangulation involves creating geometric shapes in data analysis

What is member checking in qualitative data analysis?

• Sharing the findings with participants to validate the accuracy of the data

• Using only researcher interpretation

• Ignoring participant feedback

• Asking participants to provide new data

How can qualitative data analysis contribute to theory building?

• By identifying patterns, themes, and relationships within the data.

• By conducting quantitative analysis instead of qualitative analysis.

• By excluding any interpretation of the data and presenting it as-is.

• By ignoring data patterns and focusing solely on individual data points.