Activities and resources from the Centre for Doctoral Studies to help you collect, manage, and analyse your data.

Research Methods and Data Skills is one of our eight key doctoral development themes, designed to help postgraduate research students across King’s navigate the support on offer.

For guidance on booking live workshops and registering for on-demand services, see How to Access our Courses.

A Comparison of Qualitative Methods (PGR337)

This course will compare and contrast the aims, data collection preferences, analytic style, limitations and appropriate usage of four different qualitative methods - grounded theory, thematic analysis, interpretative phenomenological analysis and narrative analysis – in order to identify the types of research questions to which each method is best suited. The possibility of conducting by-person or case analyses using qualitative data will also be considered.

Analysing Qualitative Data (PGR316)

This course will consider and discuss a range of issues relative to the micro-analysis of qualitative data. Using example data throughout, issues covered will include the analyst’s perspective (the aims and nature of their engagement with the data), coding systems, how to choose extracts for analysis in a systematic fashion, the meaning and importance of interpretation, generalising from qualitative findings and various write-up issues, including the relationship between the analysis and discussion sections of a qualitative report and the creation of impact.

Managing Research Data (LIB842)

An introduction to research data management which aims to equip researchers with the knowledge and skills they need to manage data more effectively in order to meet the expectations of the university, funders and publishers, and to reduce the risk of breaching legislation and codes of conduct. It includes advice about creating, storing, securing, keeping and disposal of data, as well as advice on data management planning, to ensure that research data remain accessible for as long as required.

Qualitative Interviewing (PGR338)

Consider four different types of interviewing, but with a particular focus on semi-structured or ‘qualitative’ interviewing. Learn about the nature of interview questions, the design and structure of an effective interview schedule and the mechanics of conducting a successful interview.

Using NVivo for Qualitative Research (PGR317)

This course will introduce students to the NVivo software package, which is designed to help in the organisation, management and analysis of qualitative data. All the basic functions of NVivo will be covered, including the importation, storage, and organisation of various data sources, the potential for transcription, the creation of cases and nodes, classifications and attributes, and the meanings of these terms will be explained. The process of coding and data analysis will be demonstrated and the use of ‘queries’ explored as a ‘top down’ means of data access. The pros and cons of using software to conduct qualitative research will also be considered.

To find out more about our on-demand platforms and how to access them, see How to Access our Courses.

PGR Core Library

• Introduction to Statistics for Researchers

The course is designed for researchers who have little understanding of statistical methods and wish to apply statistical techniques in practice. By the end of the sessions, you will understand fundamental statistical concepts and be able to use SPSS to apply the relevant techniques.

• Measures of Agreement and Statistical Methods for Assessing Diagnostic Tests

This course focuses on the statistical methods used for measuring agreement for both categorical and quantitative data. You will learn about standard measures and techniques that are used to evaluate the performance of diagnostic tests and assess the agreement between observers.

• Survival Analysis

This course will focus on statistical methods used to assess the time of an event. You will be shown how to perform survival analysis through the statistics package SPSS, and the interpretation of SPSS output will be considered.

Nature Masterclasses

• Managing Data to Unlock its Full Potential

A 10-hour certified course teaches you the importance of data management, the best practices of organising, storing, archiving and quality checking your data, and options for sharing your data to maximise the impact of your research. It is suitable for researchers in any discipline.

• Data Analysis: Planning and Preparing

This short 4-hour certified course introduces the essential elements of robust data analysis in detail so that learners will be equipped with the knowledge to implement best practices in planning and preparing for data analysis. It is suitable for researchers in any discipline who want to develop their data analysis skills to maximise the outputs of their research data.

• Data Analysis: Conducting and Troubleshooting

This 5-hour certified course is suitable for researchers in any discipline who want to develop their data analysis skills by learning about the key concepts, processes, and methodologies of effective data analysis during research projects. Specific tools for exploring various datasets are also briefly introduced.

SAGE Campus

SAGE Campus has e-modules on many aspects of qualitative data collection and analysis, including Working with Transcribed Data, Do Your Interviews, Gather Your Data Online, and Collecting Social Media Data. In addition, there are several learner pathways dedicated to statistical and data skills.

• Handle Numbers and Data Learner Pathway

This pathway is aimed at all students who haven’t worked with numbers or stats for a while and need a refresher.  Using different sets of data, learners will practice finding statistically significant results, discuss p-values and work with samples to measure and investigate people, organizations and societies.

• Master R Programming for Research Learner Pathway 

This pathway begins with an overview of the basic R commands and data structures for manipulating data. Learners will progress to generating inferences using quantitative statistical methods, eventually presenting data in an engaging and visual way. 

• Visualize Your Data Learner Pathway 

This pathway teaches an overview of the principles, techniques, and tools for presenting data in visually attractive and powerful visualizations. By the end of the learning pathway, learners will be able to present their data in interactive ways using the R programming language.   

• Program in Python Learner Pathway 

This pathway teaches an overview of the core elements of the Python programming language and how these can feed into social scientific work. Reviewing essential elements of Python programming, learners will be able to extract data and use visualization techniques when conducting social science research.

LinkedIn Learning

There are many courses on LinkedIn Learning that enable you to learn essential tools used in statistical and data analysis. You can practice your skills in NViVo, Python, R, and SPSS.

 Research Support - Libraries and Collections

King's Library has collections of resources to help with:

• Managing Research Data

• Copyright