Planning Your Research

Summary

This video module, led by Paul Bern from Syracuse University Libraries, provides a comprehensive guide to planning research effectively. It covers key areas such as research, data analysis, collection, and documentation planning. Bern emphasizes the importance of careful preparation, likening it to having a map for a journey. He advises researchers to plan backwards, from desired conclusions to necessary data collection methods. The module also touches on project management, software tools, variable tracking, and the importance of thorough documentation for data sharing and future analysis.

Takeaways

• 😀 Planning your research is essential to avoid complications and delays. It's like using a map for a road trip to ensure efficiency and clarity.
• 😀 Research planning should be done backward: Start with the desired conclusion and work backward to figure out what data and analyses you need.
• 😀 Data analysis planning is crucial. Know what analyses are necessary, the software required, and ensure you understand the assumptions and interpretations involved.
• 😀 Always create a detailed list of variables you'll need for your research, including variable names, descriptions, measurement levels, values, and sources.
• 😀 Understand how your data collection method affects both the quantity and quality of your data. Different methods will give you different results.
• 😀 Keep track of time when planning data collection, and include time for training, testing, and potential delays. Consider conducting pilot tests to identify potential issues.
• 😀 Secondary data may have usage restrictions. Be sure to know the terms of use, and have a clear plan for accessing and sharing the data if necessary.
• 😀 Good documentation is key to keeping your project on track. Document everything as you go, so you don’t forget important details or make mistakes later on.
• 😀 Keep a clear record of how you collected and analyzed your data, including any changes or methods used to extract or subset data from larger datasets.
• 😀 If you’re sharing your data, be aware of the documentation requirements from funding agencies, journals, or data repositories. Follow these guidelines from the start.
• 😀 While research planning is time-consuming, it will ultimately save you time and reduce the chances of unexpected setbacks during your project.

Q & A

Why is planning your research important?

Planning helps avoid future headaches by providing clear directions for data collection, analysis, and documentation, ensuring that the research runs smoothly and efficiently.

What does it mean to 'do it backwards' when planning research?

It means to start with your desired conclusion and work backward to determine the necessary data, analysis, and methodology needed to achieve that outcome.

What should you consider when selecting a data collection method?

You should evaluate how the method will impact the quality and quantity of data, as well as its feasibility and the resources needed, including tools, personnel, and permissions.

What role does project management play in research planning?

If the project involves multiple people or data from various sources, understanding project management principles is essential for ensuring smooth coordination, resource allocation, and meeting deadlines.

Why is it important to know your data before analyzing it?

Knowing your data helps identify potential issues like inadequate sample sizes or misrepresented variables, ensuring accurate and meaningful analysis.

What steps should you take when planning data analysis?

Identify the type of analysis needed, ensure you understand the software and analysis methods, and prepare the necessary tables, charts, and descriptive statistics to present your findings.

How should you document your research process?

Documenting from the start helps track decisions, methods, and reasons behind them. This is crucial for revising the research or sharing it later, particularly when meeting requirements for data repositories or funding agencies.

What are computed variables, and why are they important in research planning?

Computed variables are created by combining other variables in your dataset. It’s crucial to collect the raw data for these variables, as they can be derived later, ensuring data integrity and flexibility.

What should you do if you're using secondary data in your research?

Ensure you can access the data, understand its restrictions, and document how you subset the data. If necessary, match it with your primary data using consistent ID variables.

How can pilot tests help in research planning?

Pilot tests help identify potential problems in your research design, data collection methods, or tools before you fully commit to the study, making it easier to refine the approach.