Asking ChatGPT to explain what document analysis is as a research method and how to use it: a note for MBA students

Asking ChatGPT to explain what document analysis is as a research method and how to use it: a note for MBA students

The following is an account of my conversation with the assistant bot (via poe.com) about the nature of the research method of document analysis and how it is used.

Query 1

Joseph Ho (the student): "I am a part-time MBA student, currently doing my final year dissertation project. One of my research objective is to evaluate the staff turnover issue of my company. I notice that there is a research method called document analysis. I would like to conduct a document analysis of all the staff exit interview records to learn more about my company's staff turnover issue. Could you explain to me what document analysis is and suggest to me what steps and techniques  I should use to conduct the document analysis in this case?"

The assistant bot: "Certainly, I'd be happy to provide an overview of document analysis and suggest some steps you can take to conduct this research method for your final year dissertation project.

Document analysis is a qualitative research method that involves the systematic review and evaluation of documents, both printed and electronic. It is particularly useful when studying organizational practices, policies, and historical trends. In the context of your research on staff turnover, document analysis of staff exit interview records can provide valuable insights into the reasons and factors contributing to employee attrition in your company.

Here are the key steps you can follow to conduct a document analysis for your research:

Identify Relevant Documents: Gather all the available staff exit interview records from your company's human resources department. Ensure that you have access to a comprehensive set of documents that cover a relevant timeframe for your analysis.

Develop a Document Review Protocol: Establish a clear plan for how you will review and analyze the exit interview records. This may include creating a standardized coding scheme or a template to systematically capture the key information from each document, such as reasons for leaving, employee demographics, length of service, and any other relevant data points.

Review and Analyze the Documents: Carefully read through each exit interview record, noting patterns, themes, and any other insights that emerge. You can use qualitative data analysis techniques, such as thematic coding, to identify common reasons for employee turnover, underlying factors, or any other noteworthy trends.

Synthesize and Interpret the Findings: Organize and synthesize the information gathered from the document analysis. Look for connections, contradictions, or any other meaningful insights that can help you better understand the staff turnover issue in your company.

Triangulate with Other Data Sources: Consider supplementing your document analysis with other data sources, such as employee surveys, HR metrics, or industry benchmarks, to provide a more comprehensive understanding of the staff turnover issue.

Draw Conclusions and Recommendations: Based on your document analysis and the overall research, develop conclusions and recommendations that address the staff turnover problem in your company. These insights can inform potential interventions, policy changes, or other strategies to improve employee retention.

Remember to maintain confidentiality and privacy when handling sensitive employee information, and ensure that your document analysis aligns with any ethical guidelines or organizational policies."

Query 2

Joseph Ho (the student): "Could you provide some examples of how I can apply the document analysis techniques to my specific staff turnover data?"

The assistant bot: "Certainly, let me provide some examples of how you can apply document analysis techniques to the staff exit interview records for your dissertation project on the staff turnover issue.

Identify Patterns and Themes:

Review the exit interview records and identify the most commonly cited reasons for leaving, such as better opportunities, work-life balance, management issues, or compensation.

Look for any trends or patterns in the reasons for leaving based on factors like employee tenure, job role, department, or demographics.

Determine if there are any notable differences in the reasons cited by different employee groups or across different time periods.

Categorize and Quantify the Data:

Create a coding scheme or a data extraction template to systematically capture the key information from each exit interview record, such as:

Reason for leaving

Length of service

Job role

Department

Age, gender, or other relevant demographic details

Tabulate the frequency and percentages of the different reasons for leaving to identify the most common factors contributing to turnover.

Analyze the distribution of employee tenure to understand if there are any patterns related to when employees tend to leave the organization.

Explore Contextual Factors:

Review any additional notes or comments provided in the exit interview records to gain deeper insights into the underlying causes of turnover.

Look for any references to specific organizational policies, management practices, or work environment factors that may have influenced the employee's decision to leave.

Identify any potential connections between the reasons for leaving and broader organizational or industry trends that may be shaping the staff turnover landscape.

Triangulate with Other Data Sources:

Complement the document analysis with other data sources, such as:

Employee engagement survey results

HR metrics (e.g., voluntary turnover rates, average tenure)

Benchmarking data from industry or regional comparisons

Cross-reference the insights from the document analysis with these additional data points to develop a more comprehensive understanding of the staff turnover issue.

Visualize the Findings:

Create visual aids, such as charts, graphs, or tables, to effectively communicate the key findings from your document analysis.

These visual representations can help to highlight the most significant patterns, trends, and relationships within the staff turnover data.

For example, you could create a bar chart to illustrate the distribution of reasons for leaving or a line graph to show the turnover rates over time.

By applying these document analysis techniques to the staff exit interview records, you can gain a deeper understanding of the factors contributing to employee turnover in your organization. This insight can then inform the development of targeted strategies and recommendations to address the staff turnover issue and improve employee retention within your company.

Let me know if you have any other questions or if you would like me to provide additional examples or guidance on conducting the document analysis for your dissertation project";

Query 3

Joseph Ho (the student): "Is document analysis a primary research or a secondary research?"

The assistant bot: "Document analysis is considered a form of secondary research. Here's a more detailed explanation:

Primary research refers to the collection and analysis of original data, such as conducting surveys, interviews, or experiments to gather new information about a research topic.

On the other hand, secondary research involves the analysis of existing data and information that has been previously collected and published by others. This includes:

Reviewing academic journals, books, reports, and other published literature.

Examining government statistics, industry data, and other archival records.

Analyzing internal organizational documents, such as meeting minutes, policy manuals, or financial statements.

Document analysis falls under the category of secondary research because it involves the systematic examination and interpretation of existing written materials or documents. In the case of your dissertation project on staff turnover, the staff exit interview records you have access to are considered secondary data sources.

The key advantages of using document analysis as a secondary research method include:

Cost-effectiveness: Analyzing existing documents is generally more cost-effective than conducting primary research.

Efficiency: Documents can provide quick access to relevant information without the time and effort required for primary data collection.

Longitudinal perspective: Documents can offer historical context and insights that may be difficult to obtain through primary research alone.

Triangulation: Document analysis can be used to corroborate or supplement findings from other primary research methods.

By combining document analysis with other primary research techniques, such as interviews or surveys, you can develop a more well-rounded and robust understanding of the staff turnover issue within your organization. This mixed-methods approach can strengthen the overall quality and credibility of your dissertation research.

Let me know if you have any other questions!"

Some overall evaluation comments

The information provided by the assistant bot is very useful and concrete for students considering to use document analysis in their MBA dissertation projects. The students should also do some reading on the document analysis topic to gain more knowledge of this commonly used research method in dissertation projects.

Reference: a collection of blog notes on using ChatGPT for research purpose.