Research Methods Training Assessment Form - South Coast Doctoral Training Partnership

Research Methods Training Assessment

Applicants awarded a +3.5 studentship are expected to have obtained the majority of the core research training skills, as specified within the ESRC Postgraduate Training and Development Guidelines 2022. This form below is designed for you to provide further information on your research methods training and helps us to decide on any funding offer over and above the standard 3.5 years as per the ESRC Postgraduate Training and Development Guidelines 2022.

It is not the case that a failure to have obtained all the core skills will necessarily lead to the requirement to take a Master’s degree with research methods; other more flexible options are available for additional quarter years for funding.

This decision will be taken considering the application as a whole. You should answer the following questions as honestly as, and as clearly as possible.

The form should be completed by the applicant and returned within 4 working days.

In evidencing your conceptual, general and specialist research skills you should highlight the specific module(s) that relate to each element of the guidelines. You should include:

module title
brief summary of what content was included
module level – undergraduate or masters
module credit points
grade achieved

If skills have been gained in the workplace rather than through academic training, please give details.

If you would like to review the questions in advance, you can download the question list here.

Student Detail

Forenames *(Please make sure this is the same as your funding application form input)

Surname *(Please make sure this is the same as your funding application form input)

SCDTP application ID *(Please make sure this is the same as your funding application form)0 / 21

Email Address *(Please make sure this is the same as your funding application form input)

1. In what ways can you demonstrate the following conceptual understanding?

Question overview

1.1 Comprehension of principles of research design and strategy, including an understanding of how to formulate research questions which are amenable to empirical investigation and an appreciation of alternative approaches to research.
1.2 Understanding of a broad range of research methods; quantitative, qualitative, and mixed methods including awareness and understanding of AI and machine learning as research tool and the value of using existing surveys and repositories of visual, text data, social media and big data.
1.3 Understanding of appropriate data generation and use either through sampling, subject selection or via the use of secondary data from existing sources.
1.4 Understanding of analytical approaches, including data analysis software.
1.5 Understanding of the significance of alternative epistemological positions and how epistemological choices impact on findings produced.
1.6 Understanding of the application of good ethical practice across the entire research process including data ethics which is inclusive of digital approaches.
1.7 Understanding of how to conduct and disseminate research in a way that is consistent with both professional practice and the standard principles of research ethics.
1.8 Understanding Open Science.

1.1 Comprehension of principles of research design and strategy, including an understanding of how to formulate research questions which are amenable to empirical investigation and an appreciation of alternative approaches to research. *

1.2 Understanding of a broad range of research methods; quantitative, qualitative, and mixed methods including awareness and understanding of AI and machine learning as research tool and the value of using existing surveys and repositories of visual, text data, social media and big data. *

1.3 Understanding of appropriate data generation and use either through sampling, subject selection or via the use of secondary data from existing sources. *

1.4 Understanding of analytical approaches, including data analysis software. *

1.5 Understanding of the significance of alternative epistemological positions and how epistemological choices impact on findings produced. *

1.6 Understanding of the application of good ethical practice across the entire research process including data ethics which is inclusive of digital approaches. *

1.7 Understanding of how to conduct and disseminate research in a way that is consistent with both professional practice and the standard principles of research ethics. *

1.8 Understanding Open Science. *

2. In what ways can you demonstrate competence in the following general research skills?

Question overview

2.1 Competency in the skills to manage data effectively:
- 2.1.1 Open science principles and practices
- 2.1.2 Checking and cleaning data for analysis
- 2.1.3 Data quality assurance measures
- 2.1.4 Manipulating and coding data
- 2.1.5 Data representation/visualisation
- 2.1.6 Secure data storage
- 2.1.7 Preparing data for dissemination/deposit
- 2.1.8 Archiving data
- 2.1.9 General Data Protection & Regulation
2.2 Competency in identifying and addressing ethical and legal issues
2.3 Competency in digital and bibliographic skills
2.4 Necessary language skills
2.5 Understanding of mechanisms for achieving and maximizing the impact of the research
2.6 Knowledge of Intellectual Property Rights

2.1 Competency in the skills to manage data effectively:

2.1.1 Open science principles and practices *

2.1.2 Checking and cleaning data for analysis *

2.1.3 Data quality assurance measures *

2.1.4 Manipulating and coding data *

2.1.5 Data representation/visualisation *

2.1.6 Secure data storage *

2.1.7 Preparing data for dissemination/deposit *

2.1.8 Archiving data *

2.1.9 General Data Protection & Regulation *

2.2 Competency in identifying and addressing ethical and legal issues *

2.3 Competency in digital and bibliographic skills *

2.4 Necessary language skills *

2.5 Understanding of mechanisms for achieving and maximizing the impact of the research *

2.6 Knowledge of Intellectual Property Rights *

3. In what ways can you demonstrate the following specialist skills?

Question overview

3.1 In-depth knowledge of the latest thinking and ideas in your field of enquiry including subject knowledge, theoretical positions and research methods.
3.2 Competency in specialist qualitative methods appropriate to your discipline (e.g. analysis methods for textual, aural, visual data; participatory, multimodal, arts-based approaches; historical, comparative, archival methods; discourse, content, narrative analysis; computer assisted data analysis).
3.3 Competency in specialist quantitative methods appropriate to your discipline (e.g. analysing complex numerical data; inferential statistical tests for parametric and nonparametric data; statistical inference from survey or experimental research; specific statistical approaches such as ANOVA, correlation, linear/non-linear regression, multivariate modelling, fixed and random effect models, growth trajectory and multi-level modelling; data reduction and grouping methods, such as factor and cluster analysis; multidimensional scaling; longitudinal analysis, event history analysis, agent-based modelling or similar).

3.1 In-depth knowledge of the latest thinking and ideas in your field of enquiry including subject knowledge, theoretical positions and research methods *

3.2 Competency in specialist qualitative methods appropriate to your discipline (e.g. analysis methods for textual, aural, visual data; participatory, multimodal, arts-based approaches; historical, comparative, archival methods; discourse, content, narrative analysis; computer assisted data analysis). *

3.3 Competency in specialist quantitative methods appropriate to your discipline (e.g. analysing complex numerical data; inferential statistical tests for parametric and nonparametric data; statistical inference from survey or experimental research; specific statistical approaches such as ANOVA, correlation, linear/non-linear regression, multivariate modelling, fixed and random effect models, growth trajectory and multi-level modelling; data reduction and grouping methods, such as factor and cluster analysis; multidimensional scaling; longitudinal analysis, event history analysis, agent-based modelling or similar). *

Hi {name-1-first-name} {name-1-last-name},

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