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.

As a science researcher, you will need to deal with quite heterogeneous and dirty data. The data may have been collected through different approaches: observation, surveys, interviews, experiments, published printed or online sources, etc. Moreover, the data may have been encoded by different software and persons, and comes to you in different formats (e.g., txt, csv, xlsx, json, etc). Therefore, the data typically needs to be preprocessed before you can make sense of it through statistics and graphical representations. For example, you may need to re-encode the information in a way that is more meaningful to your analysis goals. Also, you may need to re-arrange the data and clean it, removing duplicates and incomplete information. Finally, you may need to apply all these transformations to other similarly structured data, over and over again. Doing this “by hand” is an arduous, time-consuming and error-prone task; so, automatizing these routines is the smart way to go!

In this course, I will teach you how to read, transform and prepare different kinds of data using Python and its popular libraries NumPy and Pandas. We are going to solve several problems (“Missions”) together, of increasing levels of difficulty. Each Mission will introduce you to new data structures (e.g., dictionaries, series, dataframes), methods and attributes, extending your previous knowledge. The content of the course is designed to be to-the-point and focused on practicality. By the end of the course, you should be able to program preprocessing routines that you can apply to your own data. Moreover, you will have an advanced data-handling blueprint to which you can easily add new information and skills over time.

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.

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 short course introduces Embodied Inquiry as a research method interested in knowledge generated through the body, not just knowledge of the body. Embodied Inquiry has gained traction as a creative research method capable of challenging the mind-body split and exploring the possible role of the body in research, both for the researcher and for participants. The course will provide a broad overview of the theoretical grounding for embodied inquiry, what embodied inquiry can look like within the social sciences as well as the benefits and pitfalls of embodied inquiry as a method. In addition, the course will provide opportunities to consider how embodied inquiry might relate to individual’s research projects and identifying where to find out more about embodied inquiry.

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.

With such a large variety of qualitative research methods to choose from, creating a research design can be confusing and difficult without a sufficiently informed overview. This module aims to provide an overview by introducing qualitative data collection and analysis methods commonly used in social science research. The module provides a foundation for other SSRMP qualitative methods modules such as ethnography, discourse analysis, interviews, or diary research. Knowing what is ‘out there’ will help a researcher purposefully select further modules to study on, provide readings to deepen knowledge on specific methods, and will facilitate a more informed research design that contributes to successful empirical research.

NB. This module has video content that needs watching prior to the advertised start date, which can be found on the Moodle page.

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.

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.

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.

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).

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 course introduces students to discourse analysis with a particular focus on the (re)construction of discourse and meaning in textual data. It takes students through the different stages of conducting a discourse analysis in four practical-oriented sessions. The overall course focus is guided by a Foucauldian and Critical Discourse Analysis approach, conceptualising discourses as not only representing but actively producing the social world and examining its entanglement with power.

The first session gives an overview of theoretical underpinnings, exploring the epistemological positions that inform different strands of discourse analysis. In the second session, we delve into the practical application of discourse analysis of textual data. Topics covered include, among others, what research questions and aims are suitable for discourse analysis as well as data sampling. In the third session, we discuss how to analyse textual data based on discourse analysis using the computer-assisted qualitative data analysis software Atlas.ti. The fourth session will take a workshop format in which students apply the gained knowledge by developing their own research design based on discourse analysis.

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.

The module explores Good Data Visualisation (GDV) and graph creation using Python.

In this module we demystify the principles of data visualisation, using Python software, to help researchers to 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 an online Zoom, self-paced, practical workshop. In addition there will be post-class exercises and a 1-hour asynchronous Q&A forum on Moodle Forum.

This short course will be an opportunity for us to engage with a variety of decolonial theories and methodologies and to consider the implications of these approaches on a variety of elements of our research processes. Each session will consist of a presentation which engages with selected decolonial theory and methods, examples of ‘methods in practice’ drawn from across the social sciences and time for self-reflexive individual and group discussion.

The course will not prescriptively define and provide instructions for ‘decolonial methods’, but instead be a space to consider a variety of ways in which scholars, activists and those working outside the traditional boundaries of ‘the academy’ have thought decolonially about social science research methodologies. The course’s workshop format will enable opportunities for us to apply some of these insights to our own scholarship.

This is the first in a series of three workshops, which extend last term's teaching on 'Decoloniality in Research Methods'. In each session, participants will be presented with a range of theoretical concepts as well as case studies from a variety of scholars who mobilise these concepts to shape their methodologies. At least half of each session will be dedicated to practical application – participants will be encouraged to engage in a range of individual and group reflections, discussions and exercises.

Participants will be encouraged to reflect on how decolonial thought affects each stage of their research project. Beginning with initial research design and literature reviews, and ending with dissemination and research impact, each session focuses on a different stage in the research cycle, bringing a range of decolonial thought and scholar-activism into conversation with our research methods.

Please note: Participants can choose whether to attend a single session or multiple sessions, as each will be a 'stand alone' workshop. However, each workshop must be booked sepaarately.

Workshop 1: Research design and the impact of (de)coloniality on our research projects

In this session we’ll place our disciplines in the historic context of their emergence and ask what implications this historicization has on our research in the present. We’ll then discuss a number of scholars who propose decoloniality and/or decolonisation as theoretical frames through which we can approach our research. In terms of practical skills, we’ll look to the emerging field of citational justice, asking how who and what we cite impacts the work we produce. We’ll also examine our research questions and explore their potential contributions to the reproduction of or resistance to deeper structures of power.

This is the second in a series of three workshops, which extend last term's teaching on 'Decoloniality in Research Methods'. In each session, participants will be presented with a range of theoretical concepts as well as case studies from a variety of scholars who mobilise these concepts to shape their methodologies. At least half of each session will be dedicated to practical application – participants will be encouraged to engage in a range of individual and group reflections, discussions and exercises.

Participants will be encouraged to reflect on how decolonial thought affects each stage of their research project. Beginning with initial research design and literature reviews, and ending with dissemination and research impact, each session focuses on a different stage in the research cycle, bringing a range of decolonial thought and scholar-activism into conversation with our research methods. Please note: Participants can choose whether to attend a single session or multiple sessions, as each will be a 'stand alone' workshop. However, each workshop must be booked separately.

Session 2: The role of ‘the researcher’ & the importance of reflexivity

In this session, we’ll discuss the notion of ‘reflexivity’, considering our disciplines, our roles as researchers within the University, and our experiences as individual researchers with our own life experiences and histories. We’ll then explore seven commonly used research methods (the development of ‘social theory’, quantitative analysis, ethnography, autoethnography, qualitative interviews, digital methods and archival research). We’ll ask what happens to these methods when we place them into a wider frame of decolonial analysis and look to other scholars who are using these methods to advance the goals of decolonization.

In terms of practical skills, participants will be encouraged to bring their own reflexive writing to the session, and we’ll explore how different theories relating to standpoint, positionality and intersectionality help us make sense of the approaches we are taking. Participants will be encouraged to bring an outline of their research methods and will work in thematic groups to place their methods in conversation with decolonial thought.

This is the third and last in a series of three workshops, which extend last term's teaching on 'Decoloniality in Research Methods'. In each session, participants will be presented with a range of theoretical concepts as well as case studies from a variety of scholars who mobilise these concepts to shape their methodologies. At least half of each session will be dedicated to practical application – participants will be encouraged to engage in a range of individual and group reflections, discussions and exercises.

Participants will be encouraged to reflect on how decolonial thought affects each stage of their research project. Beginning with initial research design and literature reviews, and ending with dissemination and research impact, each session focuses on a different stage in the research cycle, bringing a range of decolonial thought and scholar-activism into conversation with our research methods.

Please note: Participants can choose whether to attend a single session or multiple sessions, as each will be a 'stand alone' workshop. However, each workshop must be booked separately.

Session 3: From data collection to analysis to dissemination

In this session, we’ll begin with Linda Tuhiwai Smith’s (2012:226) claim that researchers ‘must get the story right as well as tell the story well’. We’ll think about what it means to analyse our data and create a product (a dissertation, research paper) which exists within the wider context of the academy. We’ll examine six different ways in which different researchers have oriented themselves towards their research, and their research towards the future (including an ‘ethics of care’, ‘rage anger and complaint’, ‘love, empathy, solidarity and desire’ and ‘action, speculation and movement’).

In terms of practical skills, we’ll think about our research outputs, the potential impacts of their design and dissemination and how these considerations might impact the earlier stages of our research projects, such as in the way we collect and store our data. Participants will also be encouraged to think about their own research orientation and place their project into a wider speculative context.

This SSRMP module introduces solicited diaries as a qualitative data collection method. Diary methodology is a flexible and versatile tool which has been used across a variety of disciplines (e.g. public health, nursing, psychology, media studies, education, sociology).

Solicited diaries are particularly powerful in combination with qualitative interviews, enabling the remote collection of rich data on intimate or unobservable topic areas over a longer period of time. This multi-method approach, also known as the ‘diary-interview method’ (DIM), has been originally developed as an alternative to participant observation (see: Zimmerman, D. H., & Wieder, D. L. (1977). The Diary: Diary-Interview Method. Urban Life, 5(4), 479–498.), which makes it an especially attractive qualitative data collection method in Covid-19 times.

In addition to the engagement with pre-recorded videos on Moodle (covering diary methodology basics), you will get hands-on experience with designing your own qualitative diary (4 hours live workshop) and trying out the role of a researcher as well as research participant (teaming up with a module colleague and filling out each other’s diaries). We will reflect on these experiences and answer remaining questions in a final 1-hour live session.

The module is suitable for anybody interested in learning more about the method and/or using solicited qualitative diaries in their own research projects.

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.

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 Stata. 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.