Research methods and data

Research methods

• Research methods are qualitative and quantitative methods of data collection and analysis mainly in empirical research.
• The choice of research methods depends on your research questions and research data, as well as the theoretical background of your research, not your discipline as such.
• The difference between everyday information and scientific information was discussed earlier on page Information resources for different needs. Here, you should be aware of the difference between research data  (empirical data) and research literature (theoretical background).
• The choice of the source for research methods is also dependent on your information need. See more at the front page of SAGE Research Methods > "I want to..."
• Note that the name of the research methods database or site does not tell everything of its contents.

Resources for research methods 

Research data

Typically, research data are

• questionnaire surveys, interviews, focus group discussions, written material, recordings, official documents, archival material, websites, or register or media data, as well as 
• different artifacts, specimens and samples, laboratory test results, genetic sequences, code, algorithms, simulations and other things depending on the scientific field. 

Data management planning is an important part of research planning. It deals with all that you should be aware of before you start collecting research data eg.: Are you handling personal data? Do you need an ethical review?

Data management planning is much easier to do if you use archived research data.

Archived research data

Collecting your own data

If you are collecting your own data instead of using archived data:

• Create a data management plan (DMP), which will help you consider important questions like 
• Is there personal data, e.g. in interview data or survey data? If yes, you must follow GDPR legislation. What is personal data?
• Is there copyrighted material?
• Especially if you are part of a research group, it's important to formulate an agreement on who has the right to access and share the data.
• How to document your data. 
• FAIR principles are part of taking care of good scientific practice. FAIR principles as such are difficult to implement perfectly, but don't let that confuse you. In your thesis project, the important thing is to take care of documentation of your research process and be systematic. FAIR principles can be summed up like this:
• Findable: data and information of the data (metadata) should be findable, if possible.
• Accessible: how to access the data, e.g. is authentication required.
• Interoperable: researchers should be able to use the data in different applications.
• Reusable: data and metadata are documented and described well and can be reused or replicated.
• To learn more, see go-fair.org 

Sources
FSD Data Management Guidelines 
North Carolina State University Library