Research Methodology II
Lecture summary + notes
Corpus analysis definition (Also called content analysis)
Content analysis is a research technique for the objective, systematic, and quantitative
description of the manifest content of communication.
Objective validity, reliability, clear unit of analysis
Systematic all relevant aspects
Quantitative counting instances
Manifest content content that is tangible and observable
Content analysis in 8 steps by Treadwell and Davis, 2020
● Formulate a research question/hypothesis
● Determine what content you will analyze
● Create your corpus
● Decide what you are going to code
● Establish how you are going to code the data
● Annotate the corpus
● Count occurrences
● Report results
Example of application of corpus analysis
1. Formulate a research question/hypothesis
Topic/literature research
- Metaphor in some advertisements
- Difference between products and services
A hypothesis is the use of metaphor
2. Determine what content you will analyze
I will only take in account
3. Create your corpus
I will select all advertisements that were published in 5 most-read magazines in the
Netherlands
4. Decide what you are going to code
I will code for presence of metaphor in the image displayed in the ad
- Not interested in type of metaphor, not interested in language use
I will code for product/service
5. Establish how you are going to code the data
Presence of metaphor: yes/no
1
, Type
6. Annotate the corpus
Presence of metaphor: yes/no
Type: product/service/other
7. Count occurrences
Count where the metaphor was present and absent
Count which kind of products there occurred
8. Report results
Report results in percentages and note the differences
How would you investigate the questions?
- How are minorities depicted in newspapers
- What kind of information is given in television commercials
- How often is irony used
- Do collectivistic appeals appear more in Japanese than in Canadian radio commercials
- How was the BP oil leak discussed in American and European newspapers
Which questions to ask yourself when setting up research
1. What content will you analyze?
2. What will be your corpus?
3. What are you going to code and how will you code it?
Guest Lecture: Research Data Management (RDM)
Research data is all information, digital and non-digital, generated as part of the scientific
process, on which scientific conclusions are based.
In some disciplines, data is a pretty straightforward concept, such as survey data, interview
transcripts and statistical data. For the other types of data this is less clear.
- Data as part of a publication: extensive, structured reference lists may be valuable
databases on their own, and could be archived in a data repository.
- Primary data: audio/video/text data that you collected/recorded yourself, raw data
- Secondary data: derived data, e.g. analysis schemes, scripts and codebooks
- Annotations: may be databases on their own, and could be achieved in a data
repository (check copyright if the original text is included)
Why research data management?
- Formal need to comply: RDM certificate is part of PhD thesis
- Saves time and increases efficiency
- Keeps your data safe and secure
2
, - Avoids data loss
- Prevents unauthorized access
- Facilitates the documentation and reuse of data
- Links publications and datasets
- Stimulates transparency
FAIR data principles
Findable Data and metadata have a unique identifier
Accessible Data and metadata are accessible through a standard protocol
Interoperable Data and metadata are in standard formats and terminologies
Reusable Rich documentation and licensing information
Open data is data that is freely available online for reuse and republish for everyone,
provided that the data source is attributed.
Open data means literally “as open as possible, as closed as necessary”.
Also closed access data can be stored FAIR provided that the access terms are transparent
and public.
Sharing is not giving away, to work in an open environment benefits all, especially in the data
sharer.
- Reach as many people as possible
- Be cited more often
- Build cooperation
F + A directive means that research data used for scientific publications must be Findable (F)
and Accessible (A).
- (F): datasets should have a persistent identifier (DOI) and rich metadata
- (A): it is properly arranged and recorded how and under what conditions access to the
data is possible (= not per definition open!)
Data Management Plan (DMP) is a document which outlines how research data will be
managed over the course of a research project.
Benefits of writing a DMP
- Make research more efficient
- Comply with privacy law and institutional policy
- Think and decide timely about Research Data Management issues
- Use it as a dynamic document
- Useful in meetings for monitoring progress of your research
Writing a DMP
- Create a plan in the DMP-tool and choose your format
- Answer all the questions and ask your supervisor for feedback
3
, Why data documentation?
Make your research
- understandable
- verifiable
- reusable (by you or by others)
Data documentation summarized
- Understood now and in the future
- Properly interpreted as relevant context is available
- Files explaining the context of the dataset and how the research was done
- Files that describe the structure of the dataset
- Files that describe the content of the dataset, at the data level
- Document steps to create, process and analyze your data
Data organization
- Use folders and subfolders, but not too many
- Name folders and files appropriately
- Be consistent
- Mention version numbers
- When adding dates, use yyyymmdd
- Separate ongoing and completed work
Archiving data after research
General Radboud University RDM policy states:
- Data are stored at the time of publication of the research.
- The retention period for research data is a minimum of ten years for scientific
integrity purposes.
RIS for students
- You create the dataset in RIS for students. Your supervisor will have to approve it.
- You will see a list of all data management plans and datasets you have previously
created.
- You can also create a new dataset, for example for your thesis. Note that you should
create one dataset for your whole thesis (and not one for the raw data, another one for
the processed data)
- Fill in the metadata and upload all the files of which your dataset consists. You can
then submit the dataset for your supervisor’s approval.
Which data should be archived?
From the perspective of scientific integrity:
- Approval ethical committee
- Informed consent and information sheet
- Raw, processed and analyzed data
4
Lecture summary + notes
Corpus analysis definition (Also called content analysis)
Content analysis is a research technique for the objective, systematic, and quantitative
description of the manifest content of communication.
Objective validity, reliability, clear unit of analysis
Systematic all relevant aspects
Quantitative counting instances
Manifest content content that is tangible and observable
Content analysis in 8 steps by Treadwell and Davis, 2020
● Formulate a research question/hypothesis
● Determine what content you will analyze
● Create your corpus
● Decide what you are going to code
● Establish how you are going to code the data
● Annotate the corpus
● Count occurrences
● Report results
Example of application of corpus analysis
1. Formulate a research question/hypothesis
Topic/literature research
- Metaphor in some advertisements
- Difference between products and services
A hypothesis is the use of metaphor
2. Determine what content you will analyze
I will only take in account
3. Create your corpus
I will select all advertisements that were published in 5 most-read magazines in the
Netherlands
4. Decide what you are going to code
I will code for presence of metaphor in the image displayed in the ad
- Not interested in type of metaphor, not interested in language use
I will code for product/service
5. Establish how you are going to code the data
Presence of metaphor: yes/no
1
, Type
6. Annotate the corpus
Presence of metaphor: yes/no
Type: product/service/other
7. Count occurrences
Count where the metaphor was present and absent
Count which kind of products there occurred
8. Report results
Report results in percentages and note the differences
How would you investigate the questions?
- How are minorities depicted in newspapers
- What kind of information is given in television commercials
- How often is irony used
- Do collectivistic appeals appear more in Japanese than in Canadian radio commercials
- How was the BP oil leak discussed in American and European newspapers
Which questions to ask yourself when setting up research
1. What content will you analyze?
2. What will be your corpus?
3. What are you going to code and how will you code it?
Guest Lecture: Research Data Management (RDM)
Research data is all information, digital and non-digital, generated as part of the scientific
process, on which scientific conclusions are based.
In some disciplines, data is a pretty straightforward concept, such as survey data, interview
transcripts and statistical data. For the other types of data this is less clear.
- Data as part of a publication: extensive, structured reference lists may be valuable
databases on their own, and could be archived in a data repository.
- Primary data: audio/video/text data that you collected/recorded yourself, raw data
- Secondary data: derived data, e.g. analysis schemes, scripts and codebooks
- Annotations: may be databases on their own, and could be achieved in a data
repository (check copyright if the original text is included)
Why research data management?
- Formal need to comply: RDM certificate is part of PhD thesis
- Saves time and increases efficiency
- Keeps your data safe and secure
2
, - Avoids data loss
- Prevents unauthorized access
- Facilitates the documentation and reuse of data
- Links publications and datasets
- Stimulates transparency
FAIR data principles
Findable Data and metadata have a unique identifier
Accessible Data and metadata are accessible through a standard protocol
Interoperable Data and metadata are in standard formats and terminologies
Reusable Rich documentation and licensing information
Open data is data that is freely available online for reuse and republish for everyone,
provided that the data source is attributed.
Open data means literally “as open as possible, as closed as necessary”.
Also closed access data can be stored FAIR provided that the access terms are transparent
and public.
Sharing is not giving away, to work in an open environment benefits all, especially in the data
sharer.
- Reach as many people as possible
- Be cited more often
- Build cooperation
F + A directive means that research data used for scientific publications must be Findable (F)
and Accessible (A).
- (F): datasets should have a persistent identifier (DOI) and rich metadata
- (A): it is properly arranged and recorded how and under what conditions access to the
data is possible (= not per definition open!)
Data Management Plan (DMP) is a document which outlines how research data will be
managed over the course of a research project.
Benefits of writing a DMP
- Make research more efficient
- Comply with privacy law and institutional policy
- Think and decide timely about Research Data Management issues
- Use it as a dynamic document
- Useful in meetings for monitoring progress of your research
Writing a DMP
- Create a plan in the DMP-tool and choose your format
- Answer all the questions and ask your supervisor for feedback
3
, Why data documentation?
Make your research
- understandable
- verifiable
- reusable (by you or by others)
Data documentation summarized
- Understood now and in the future
- Properly interpreted as relevant context is available
- Files explaining the context of the dataset and how the research was done
- Files that describe the structure of the dataset
- Files that describe the content of the dataset, at the data level
- Document steps to create, process and analyze your data
Data organization
- Use folders and subfolders, but not too many
- Name folders and files appropriately
- Be consistent
- Mention version numbers
- When adding dates, use yyyymmdd
- Separate ongoing and completed work
Archiving data after research
General Radboud University RDM policy states:
- Data are stored at the time of publication of the research.
- The retention period for research data is a minimum of ten years for scientific
integrity purposes.
RIS for students
- You create the dataset in RIS for students. Your supervisor will have to approve it.
- You will see a list of all data management plans and datasets you have previously
created.
- You can also create a new dataset, for example for your thesis. Note that you should
create one dataset for your whole thesis (and not one for the raw data, another one for
the processed data)
- Fill in the metadata and upload all the files of which your dataset consists. You can
then submit the dataset for your supervisor’s approval.
Which data should be archived?
From the perspective of scientific integrity:
- Approval ethical committee
- Informed consent and information sheet
- Raw, processed and analyzed data
4
