All Cambridge Research Methods (CaRM) courses

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.

This is a rigorous 8-hour module consisting of 4 hands-on workshops focused on mastering real-world data cleaning, transformation, DATA EXPLORATION, and visualization using R, with strong support from ChatGPT as a generative AI assistant. The course condenses core content into two intensive instruction plus exercise sessions and dedicates two sessions to structured, high-difficulty practice using real datasets and published research.

Have you received or collected your data (or anticipate doing so!), but are not sure what to do next? This course is designed to equip you with the skills you need to efficiently clean, reformat, and prepare your datasets using Stata. Ideal for social science researchers and analysts who want to use quantitative data for their dissertation or other research project and want to prepare their data efficiently and follow best practices.

Over four interactive sessions, you will master essential techniques for handling missing data, merging and appending datasets, batch processing, and recoding variables. Each session combines concise, focused lectures with practical, hands-on exercises using either your own data or datasets provided by the instructor.

This module will provide an introduction to using Interpretative Phenomenological Analysis (IPA) as a research methodology, including thinking about interview preparation to elicit suitable data, the theoretical underpinning of this approach, and conducting analysis of interview data using IPA. Students will gain confidence in incorporating IPA into their research design and will conduct some set activities to reflect on the practicalities of this.

This module will provide an overview of different qualitative methods which students may wish to use in their social science research. It will explore the advantages and potential drawbacks of different qualitative methods of data collections and analysis. Reflective activities (to be completed independently and as part of the in-person workshops) will encourage the students to consider the best methods for their own research design. It is intended that this module will provide a broad foundation for students to continue on to other CaRM modules on Qualitative Methods.

This course aims to introduce students to archival research. Although primarily used by historians, archives are resources than can be drawn on by a range of disciplines including but not limited to Anthropology, Sociology, researchers in languages and cultures, and others.

Successful archival research requires understanding not only the practicalities of access, but also an awareness of the nature and trajectory of the archive and its collections themselves. The archive is a physical, political and cultural space and as researchers we are also placed in a relationship with it that can raise questions of perspective and ethics.

Building upon the univariate techniques introduced in the Foundations in Applied Statistics (FiAS) module, these sessions aim to provide students with a thorough understanding of statistical methods designed to test associations between two variables (bivariate statistics). Students will learn about the assumptions underlying each test, and will receive practical instruction on how to generate and interpret bivariate results using R. It introduces students to four of the most commonly used statistical tests in the social sciences: correlation, chi-square tests, t-tests, and analysis of variance (ANOVA).

The module is divided between pre-recorded mini-lectures, in which you’ll learn the relevant theory, and in-person, hands-on practical sessions, in which you will learn how to apply these techniques to analyse real data using the statistical package, R.

You will learn the following techniques:

• Cross-tabulations
• Scatterplots
• Covariance and correlation
• Nonparametric methods
• Two-sample t-tests
• ANOVA

As well as viewing the pre-recorded mini lectures via Moodle and attending the live lab sessions, students are expected to do a few hours of independent study each week.

Building upon the univariate techniques introduced in the Foundations in Applied Statistics (FiAS) module, these sessions aim to provide students with a thorough understanding of statistical methods designed to test associations between two variables (bivariate statistics). Students will learn about the assumptions underlying each test, and will receive practical instruction on how to generate and interpret bivariate results using R. It introduces students to four of the most commonly used statistical tests in the social sciences: correlation, chi-square tests, t-tests, and analysis of variance (ANOVA).

The module is divided between pre-recorded mini-lectures, in which you’ll learn the relevant theory, and in-person, hands-on practical sessions, in which you will learn how to apply these techniques to analyse real data using the statistical package, R.

You will learn the following techniques:

• Cross-tabulations
• Scatterplots
• Covariance and correlation
• Nonparametric methods
• Two-sample t-tests
• ANOVA

As well as viewing the pre-recorded mini lectures via Moodle and attending the live lab sessions, students are expected to do a few hours of independent study.

The purpose of this course is to familiarise students with the basic concepts of Bayesian theory. It is designed to provide an introduction to the principles, methods, and applications of Bayesian statistics. Bayesian statistics offers a powerful framework for data analysis and inference, allowing for the incorporation of prior knowledge and uncertainty in a coherent and systematic manner.

Throughout this course, we will cover key concepts such as Bayes' theorem, prior and posterior distributions, likelihood functions, and the fundamental differences between Bayesian and frequentist approaches. You will learn to formulate and estimate statistical models, update beliefs using new data, and make informed decisions based on the posterior probabilities generated through Bayesian inference. By the end of this course, you will possess the necessary skills to perform Bayesian data analysis, interpret results, and apply Bayesian methods in various contexts.

The module introduces causal inference methods that are commonly used in quantitative research, in particularly social policy evaluations. It covers the contexts and principles as well as applications of several specific methods - instrumental variable approach, regression discontinuity design, and difference-in-differences analysis. Key aspects of the module include investigations of the theoretical basis, statistical process, and illustrative examples drawn from research papers published on leading academic journals. The module incorporates both formal lecturing and lab practice to facilitate understanding and applications of the specific methods covered. The module is suitable for those who are interested in quantitative research and analysis of causality across a range of topics in social sciences.

This module provides a comprehensive introduction to coding in qualitative research, suitable for participants with little or no prior experience. It is designed to help researchers systematically identify, organise, and interpret patterns, themes, and meanings within qualitative data. The course combines a lecture with a hands-on workshop, enabling participants to develop practical coding skills while understanding the theoretical principles and strategies that underpin qualitative analysis.

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.

The module explores Good Data Visualisation (GDV), inclusive cartography, and graph creation using Python, as well as an introduction and application of mainstream software such as Microsoft Excel and QGIS and Generative AI.

We demystify the principles of data visualisation, theories and practices on inclusive cartography, using Python and other software, to help researchers better understand and reflect how the “5 Principles” of GDV can be achieved. We also examine how we can develop Python’s application in data visualisation beyond analysis. Students will have the opportunity to apply GDV knowledge and skills to data using Python in class and a self-paced practical workshop. There will be post-class exercises and a 1-hour asynchronous Q&A forum on Moodle Forum.

This module consists of three two-hour long workshops in which we will deepen and apply the insights gained during the Michaelmas Term module 'Decoloniality in Research Methods: an Introduction'. We will grapple with the main aspects of social scientific research – ethics, data collection & analysis, research dissemination – from a decolonial perspective. We will ask questions such as: What does a decolonial research design look like? What are decolonial ethics? What do we owe research participants? How might the nature and purpose of research be reimagined from a decolonial perspective? Each session will have taught and hands-on elements; participants will be asked to work in groups, participate in discussions and reflect privately.

This module provides an introduction to digital and online methods through qualitative, quantitative or mixed method research. While digital methodology has a longstanding history in social sciences, the use of technology-mediated tools to investigate the social world has accelerated after the Covid-19 pandemic. Digital methods have become a priority area for UK and international research as demonstrated by the current ESRC mission of developing methodological capability in the social sciences. The module discusses the opportunities and limitations that qualitative and quantitative digital methodology presents for social research.

This module introduces students to the core theories, methods, and ethical considerations of digital ethnography. It focuses on how ethnographic approaches can be adapted to study online cultures, communities, and platforms. Through a combination of pre-recorded lectures and hands-on workshops, students will develop practical skills in observing, analysing, and reflecting on digital life.

The module is delivered across five sessions:

• Lecture 1: Introduction to Digital Ethnography (pre-recorded lectures)
• Lecture 2: Ethics and Reflexivity in Digital Research (pre-recorded lectures)
• Workshop 1: Entering and Observing Digital Fields
• Workshop 2: Reflexivity, Ethics, and Researcher Presence
• Workshop 3: Tracing and Analysing Digital Culture

This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself , and to interpret and write about your results intelligently.

Half of the module is based on pre-recorded lectures that can be accessed via the Moodle page where you will be introduced to statistical theory, concepts, and techniques. Although these pre-recorded lectures will be available for you to access over the academic year, it is important that you watch the appropriate pre-recorded lectures before the start of each corresponding practical workshop. The other half of the module consists two in-person practical workshops. In these workshops you will have the opportunity to apply the newly learned methods and techniques of multivariate regression by working through practical exercises using the software R. During the workshops staff and demonstrators will be at hand to answer answer any questions or issues you may have.

To get the most out of the course, you should also expect to spend some time between sessions having fun by building your own statistical models.

Face-to-face interviews are used to collect a wide range of information in the social sciences. They are appropriate for the gathering of information on individual and institutional patterns of behaviour; complex histories or processes; identities and cultural meanings; routines that are not written down; and life-history events. Face-to-face interviews thus comprise an appropriate method to generate information on individual behaviour, the reasons for certain patterns of acting and talking, and the type of connection people have with each other.

The first session provides an overview of interviewing as a social research method, then focuses on the processes of organising and conducting qualitative interviews. The second session explores the ethics and practical constraints of interviews as a research method, particularly relevant when attempting to engage with marginalised or stigmatised communities. The third session focuses on organisation and analysis after interviews, including interpretation through coding and close reading.

In Lent Term, the online resources are supported by 1 x zoom Q&A session, and 2 x in-person workshops. During the first in-person workshop students will role-play interviews using the scenarios outlined in the course moodle pages. During the second in-person workshop students will work in pairs on their interview material (at whatever stage of the process: whether writing interview questions, coding or analysing data) in order to receive advice and support in taking the interview material/data to the next stage of the research process.

This course introduces students to the best practice principles of visualising research data, concepts and relationships effectively, clearly and creatively through graphs, charts, and infographics. Participants will learn how to transform data (including concepts and frameworks) into clear, engaging visuals that enhance the communication of research findings in academic papers, presentations, and public engagement materials.

Ethics and the associated process of approval / review are an important component of any research project, not only practically enabling research to take place but also enabling researchers to consider the values underpinning their research. The aim of this course is to take both a practical and reflective approach to ethics. On a practical level, the course will focus on identifying the steps involved in seeking ethical approval or undertaking an ethical review. On a reflective level, the course will explore the values informing key ethical principles and concepts and how these may relate to individual’s research.

This module is an introduction to ethnographic fieldwork and analysis, as these are practiced and understood by anthropologists. The module is intended for students in fields other than anthropology.

• Session 1: The Ethnographic Method (Dr Andrew Sanchez)
• Session 2: Multimodal Youth-led Citizen Social Science (Dr Kelly Fagan Robinson)
• Session 3: Ethnography in the City (Dr Caroline Bazambanza)
• Session 4: Ethnography and AI (Dr Xin Zhan)

Session overview

Session 1: The Ethnographic Method

• What is ethnography?
• Can ethnographic research and writing be objective?
• How does one conduct ethnographic research responsibly and ethically?

Session 2: Multimodal Youth-led Citizen Social Science

In this session students will be introduced to 'multimodal' thinking and doing in fieldwork (multimodal literally means 'the different ways in which something occurs or is experienced'). We will practically unpack some of the ways of crafting what are known as 'fieldnotes', which are most commonly done via text but which can take a number of different forms.  We will also think about how the varied approaches anthropologists take to document what they meet in their fieldsites can significantly impact the shaping of their subsequent analysis. We will unpack the pros and cons of different techniques of documentation including: text, drawing, sound recording, filmic capture, and photovoice.

Session 3: Ethnography in the City

This session will tackle approaches to doing fieldwork in big cities. We will think about collaboration with groups, charities, or third-sector and private organisations, as well as how to keep up with interlocutors spread over large areas. The session will also address what can be gained from doing fieldwork on the margins of large institutions, drawing on some of Caroline Bazambanza’s work in London with NHS midwives and other healthcare professionals ‘off the clock’. Throughout, we will ask why people might want to engage in anthropological research and the tensions of sustaining consent in the everyday. Overall, the session intends to think critically about strategic ethnographic approaches and anthropology’s potential in urban environments.

Session 4: Ethnography and AI

As AI increasingly permeates different fields and reshapes life both online and offline, this session explores the relationship between ethnography and AI. We will consider:

• How to research algorithmic systems: what it means to study “algorithms as culture,” and how to conduct ethnographic research in communities and contexts that are algorithmically mediated.
• How to use algorithms as tools in research: even if your project is not directly about AI, algorithms can be leveraged as powerful methodological tools.
• How AI is reshaping the practice of ethnography itself: from questions of access and ethics to the possibilities and limits of collaborating with intelligent systems in fieldwork.

This module introduces the statistical techniques of Exploratory and Confirmatory Factor Analyses. Exploratory Factor Analysis (EFA) is used to uncover the latent structure (dimensions) of a set of variables. It reduces the attribute space from a larger number of variables to a smaller number of factors. Confirmatory Factor Analysis (CFA) examines whether collected data correspond to a model of what the data are meant to measure. STATA will be introduced as a powerful tool to conduct confirmatory factor analysis. A brief introduction will be given to confirmatory factor analysis and structural equation modelling.

• Session 1: Exploratory Factor Analysis Introduction
• Session 2: Factor Analysis Applications
• Session 3: CFA and Path Analysis with STATA
• Session 4: Introduction to SEM and programming

What makes research feminist? The module starts with this question to explore how feminist theory and praxis shape research practice. We will first examine how feminist perspectives challenge the epistemological and methodological conventions of science, with a particular focus on issues of knowledge production, positionality, and research ethics. We will then move to discussing how feminist commitments shape concrete research decisions, from choosing research questions and ‘objects’ to selecting specific methods. Through readings, in-class discussions, and hands-on exercises, students will develop the theoretical grounding and gain practical tools to apply a feminist lens to their own research projects.

This is an introductory course for students who have little or no prior training in statistics.

The module is divided between pre-recorded mini-lectures, in which you’ll learn the relevant theory, and in-person, hands-on practical sessions in which you will learn how to analyse real data using the statistical package, R.

You will learn:

• The key features of quantitative analysis, and how it differs from other types of empirical analysis
• The basics of formal hypothesis testing
• Basic concepts: what is a variable? what is the distribution of a variable? and how can we best represent a distribution graphically?
• Features of statistical distributions: measures of central tendency and dispersion
• The normal distribution
• Why statistical testing works
• Statistical methods used to test simple hypotheses

This is an introductory course for students who have little or no prior training in statistics.

The module is divided between pre-recorded mini-lectures, in which you’ll learn the relevant theory, and in-person, hands-on practical sessions in which you will learn how to analyse real data using the statistical package, R.

You will learn:

• The key features of quantitative analysis, and how it differs from other types of empirical analysis
• The basics of formal hypothesis testing
• Basic concepts: what is a variable? what is the distribution of a variable? and how can we best represent a distribution graphically?
• Features of statistical distributions: measures of central tendency and dispersion
• The normal distribution
• Why statistical testing works
• Statistical methods used to test simple hypotheses