Cambridge Research Methods (CaRM) course timetable

Virtual Data Collection in the Time of COVID-19: Practical and Ethical Considerations

Doing data collection in the time of COVID-19 has required the adaptation of existing approaches. While face-to-face data collection is not feasible during the COVID-19 crisis, phone- and internet-based interviews offer an alternative means of collecting primary data. In this workshop, we discus key practical and ethical issues concerning virtual approaches to data collection. We provide practical examples drawing on two related research projects that took place in a lower-middle income context during the Covid-19 school closures.

The module aims to provide students with an introduction to and overview of survey methods and its uses and limitations. It will introduce students both to some of the main theoretical issues involved in survey research (such as survey sampling, non-response and question wording) and to practicalities of the design and analysis of surveys. The module consists of six 1.5 hour sessions, alternating between prerecorded lectures and practical exercises.

This module introduces drawing as a research method, with a particular focus on the key elements and methodological considerations for using drawing as a visual research method, and the pairing of drawing with qualitative interviews. This module explores examples of using drawing as a research method across disciplines, and students are offered hands-on experience to practice using drawing as a research method through a practical workshop. There is also an online clinic session where students are given one-to-one opportunities to ask questions at the end of the course.

The module aims to provide students with an introduction to and overview of survey methods and its uses and limitations. It will introduce students both to some of the main theoretical issues involved in survey research (such as survey sampling, non-response and question wording) and to practicalities of the design and analysis of surveys. The module consists of six 1.5 hour sessions, alternating between prerecorded lectures and practical exercises.

In this two-part course we will introduce students to the concepts of policy analysis and impact evaluation. In doing so we first cover some definitions, what policy impacts or processes are we attempting to measure? We then focus on different ways to measure the impacts of a policy including ex-ante and ex-post methods. On Day 1 we focus on ex-post analysis (i.e., examining what happened after the policy has already been implemented). We focus on the concept and measurement of additionality and spillovers, and then zoom in on types of quantitative econometric methods, as well as qualitative processes tracing approaches. On Day 2 we focus on ex-ante analysis (i.e. examining what may happen if a policy is adopted and implemented). We describe quantitative and qualitative methods that can be used to evaluate potential impacts, including scenario modelling, Delphi analysis, and realist evaluation.

Please note that, on Tuesday 11 March, the first part of Session 1 (09:30-12:30) will take place in Titan Teaching Room 3 in the Cockcroft Building on the New Museums site. After the lunch break, the session will move to the Large Lecture theatre in the main Geography building (13:30-15:30).

This module introduces observation as a research method. Attention is given to the key elements and methodological consideration of conducting observations. It also explores the process of conducting observation, where students are given the opportunity to experiments elements of observation, and to experience the role of researcher in the practical workshop. There is also an online clinic session where students are given one-to-one opportunities to ask questions at the end of the course. This course is especially suitable for those who consider using observation as a research method within educational contexts.

The data we obtain from survey and experimental platforms (for behavioural science) can be very messy and not ready for analysis. For social science researchers, survey data are the most common type of data to deal with. But typically the data are not obtained in a format that permits statistical analyses without first conducting considerable time re-formatting, re-arranging, manipulating columns and rows, de-bugging, re-coding, and linking datasets. In this module students will be introduced to common techniques and tools for preparing and cleaning data ready for analysis to proceed. The module consists of four lab exercises where students make use of real life, large-scale, datasets to obtain practical experience of generating codes and debugging.

The module will introduce students to the study of language use as a distinctive type of social practice. Attention will be focused primarily on the methodological and analytic principles of conversation analysis. (CA). However, it will explore the debates between CA and Critical Discourse Analysis (CDA), as a means of addressing the relationship between the study of language use and the study of other aspects of social life. It will also consider the roots of conversation analysis in the research initiatives of ethnomethodology, and the analysis of ordinary and institutional talk. It will finally consider the interface between CA and CDA.

This module is designed to help students who will need to use archives in their research, and consists of four sessions. The first session will deal with the large variety of material which can be found in archives, how it is organised, and how to use their various different catalogues and use of finding devices. The second session will look at how to plan an archive visit when it is necessary to consult stored documents. Increasingly more archives are making their material available online, and this session will examine how to find out what is available to view and can be download.

Please note that an additional session on overseas archives, offered as part of the History Faculty general training, can be booked separately.

Web scraping has great potential as a research tool that can be applied across various fields of research including social science and humanities, and allows us to reach beyond the ‘quantitative and qualitative divide’. The programming and code-reading/analysing skills used in web scraping can enhance our understanding of digital power beyond the traditional limits of computing techniques.

This two-hour training module (plus 1-hour online Q&A session) introduces researchers to how to use Python software for web scraping. You will learn what web scraping means, the principles behind it, and ethical considerations, and importantly how to use Python to achieve web scraping. The module provides a good opportunity to learn how to enhance your coding and code-reading skills, from which you can reflect on how digital power especially web scraping and coding is shaping contemporary research. The training is programming beginner friendly.

For the 1hr online Q&A session the first 30mins are used for extend web scraping with other tools including R, followed by Q&A tutorials. Please ensure you have basic knowledge in using R if you would like to join the first 30mins, but the Q&A are available to all the students.

This module introduces observation as a research method. Attention is given to the key elements and methodological consideration of conducting observations. It also explores the process of conducting observation, where students are given the opportunity to experiments elements of observation, and to experience the role of researcher in the practical workshop. There is also an online clinic session where students are given one-to-one opportunities to ask questions at the end of the course. This course is especially suitable for those who consider using observation as a research method within educational contexts.

This course will provide a detailed critique of the methods and philosophy of the Null Hypothesis Significance Testing (NHST) approach to statistics which is currently dominant in social and biomedical science. We will contrast NHST with alternatives, especially with Bayesian methods. We will use computer code to demonstrate some issues. However, we will focus on the big picture rather on the implementation of specific procedures.

Researchers often feel overwhelmed by large amounts of qualitative data, wondering how to organize and analyse it, and use it effectively as primary research evidence. This module introduces principles and methods of sense-making, helping researchers identify and understand the patterns, themes, and meanings embedded in their data.

The module consists of a comprehensive lecture and two hands-on workshops. Session 1 introduces the basic principles and methods, focusing on the progression from data to sense-making, how to relate data to existing literature, and how to construct a well-supported argument based on the empirical evidence. The two workshops are designed for students to experience and practice coding (manually or using software) and to develop their own arguments. In Session 2, students can apply sense-making techniques to their own data and practice interpreting data to draw meaningful insights manually. Session 3 focuses on data analysis using Atlas.ti software, which allows students to practise coding, categorising, and conceptualising their own empirical data or open-sourced datasets.

Sessions:

Session 1: Lecture: Analysing and interpreting qualitative data

Session 2: Practical workshop: Making sense of data

Session 3: Practical workshop: Software coding demonstration

Please note that Sessions 1 and 2 will be held on the same day (Wednesday 12 March 2025).

In this two-part course we will introduce students to the concepts of policy analysis and impact evaluation. In doing so we first cover some definitions, what policy impacts or processes are we attempting to measure? We then focus on different ways to measure the impacts of a policy including ex-ante and ex-post methods. On Day 1 we focus on ex-post analysis (i.e., examining what happened after the policy has already been implemented). We focus on the concept and measurement of additionality and spillovers, and then zoom in on types of quantitative econometric methods, as well as qualitative processes tracing approaches. On Day 2 we focus on ex-ante analysis (i.e. examining what may happen if a policy is adopted and implemented). We describe quantitative and qualitative methods that can be used to evaluate potential impacts, including scenario modelling, Delphi analysis, and realist evaluation.

Please note that, on Tuesday 11 March, the first part of Session 1 (09:30-12:30) will take place in Titan Teaching Room 3 in the Cockcroft Building on the New Museums site. After the lunch break, the session will move to the Large Lecture theatre in the main Geography building (13:30-15:30).

This module is centred around identifying biases from the start of research design, through to final write-up; establishing one's epistemological position in assessing research problems and conducting analysis, including assumption of findings.