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Research methods and data

tekijä: Tuija Irmeli Oksman Viimeisin muutos torstai 08. huhtikuuta 2021, 16.51

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 

SAGE Research Methods (SRM)

SAGE Research Methods is a database which contains

  • Books, including the database's own book series
    • Little Green Books (quantitative methods)
    • Little Blue Books (qualitative methods)
  • Reference books for quick checking
  • Journal articles
  • Cases showing how methods are applied in research
  • Videos that bring research methods to life.

SRM Help   

Mapping Research Methods

A guide to research and research methods for Master’s (M.A.) students at the Faculty of Humanities at University of Jyväskylä.

  • Note that this is an excellent tool for others as well!
Research Methods Guidebook

About quantitative and qualitative research methods by The Finnish Social Science Data Archive (FSD), mainly in Finnish.

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

Finnish Social Science Data Archive (FSD) > Aila Data Service Portal
  • National resource center
  • Wide range of digital research data for learning, teaching and research purposes
  • Free of charge
  • Data Service Portal Aila > Explore our data collections >
Kielipankki, The Language Bank of Finland
  • National resource center
  • Wide variety of text and speech corpora and tools for studying them
  • Free of charge
CLARIN - European Research Infrastructure for Language Resources and Technology
  • Digital language resources from all disciplines, especially in the humanities and social sciences
Digital archives in Finnish Universities, see: Open science.  


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 

FSD Data Management Guidelines 
North Carolina State University Library

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