Cambridge Research Methods (CaRM) course timetable
Thursday 13 February
10:00 |
Evaluation Methods
[Places]
This course aims to provide students with a range of specific technical skills that will enable them to undertake impact evaluation of policy. Too often policy is implemented but not fully evaluated. Without evaluation we cannot then tell what the short or longer term impact of a particular policy has been. On this course, students will learn the skills needed to evaluate particular policies and will have the opportunity to do some hands on data manipulation. A particular feature of this course is that it provides these skills in a real world context of policy evaluation. It also focuses primarily not on experimental evaluation (Random Control Trials) but rather quasi-experimental methodologies that can be used where an experiment is not desirable or feasible. |
Qualitative Data Analysis with Atlas.ti
In progress
This course provides an introduction to the management and analysis of qualitative data using Atlas.ti. It is divided between mini-lectures, in which you’ll learn the relevant strategies and techniques, and hands-on live practical sessions, in which you will learn how to analyse qualitative data using the software. The sessions will introduce participants to the following:
Please note: Atlas.ti for Mac will not be covered. |
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12:00 |
Causality in Statistics (LT)
![]() 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. |
14:00 |
Evaluation Methods
[Places]
This course aims to provide students with a range of specific technical skills that will enable them to undertake impact evaluation of policy. Too often policy is implemented but not fully evaluated. Without evaluation we cannot then tell what the short or longer term impact of a particular policy has been. On this course, students will learn the skills needed to evaluate particular policies and will have the opportunity to do some hands on data manipulation. A particular feature of this course is that it provides these skills in a real world context of policy evaluation. It also focuses primarily not on experimental evaluation (Random Control Trials) but rather quasi-experimental methodologies that can be used where an experiment is not desirable or feasible. |
15:30 |
Ethnographic Methods
In progress
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 overview Session 1: The Ethnographic Method
Session 2: Digital Ethnography Part I In these sessions, we discuss anthropologically-informed ethnographic practices of "the digital." In the first session we define what is meant by "digital" and delineate the various ways in which the digital presents itself in everyday life, in order to ascertain the appropriate ethnographic methods for each. The first session explores theoretical conversations and research ethics before moving on to discuss the implications of digital mediations on people's lives and on ethnographic practice, including reconsiderations of what online and offline behavior represents. What are some similarities, differences, connections, and disconnections between ‘online’ and ‘offline’ forms of interaction, sociality, and social norms? Do people act in the same ways in ‘online’ versus ‘offline’ spaces? Is even such a distinction valuable? A case study will be provided to consider these issues. Session 3: Digital Ethnography Part II In the second session we will focus on digital technologies as 'tools' in facilitating and/or complementing ethnographic fieldwork. We will look at various case studies (provided in the reading list; participants are asked to read at least one beforehand) in order to assess the advantages and potential limits of digital technologies such as mobile/smart phones, geospatial tracking/mapping technologies, recording and data storage technologies, software for organizing and analyzing field data, and the mining of ‘big data’ sets. Session 4: Youth-centred and Symmetric Classroom Ethnography This session provides an introduction to ethnographic research methods with a particular focus on working with young interlocutors. While grounded in social anthropology, it is designed to be accessible to students across the social sciences. We will explore the distinctive challenges and opportunities of researching youth and youth cultures, especially within educational settings. Recognizing the varying demands of different research contexts, we will discuss approaches to conducting both immersive and shorter-term, youth-centered ethnographies, inside and outside the classroom. Emphasis will be placed on the principles of symmetry and reciprocity in the researcher-participant relationship. The session will open with a theoretical overview of key themes, followed by an analysis of a case study drawn from long-term anthropological research within a multicultural educational environment, also highlighting the evolving youth cultures within such a milieu. The latter part of the session will involve interactive activities designed to equip students with practical tools for applying ethnographic methods in their own research projects. Session 5: 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. |
16:00 |
Reading and Understanding Statistics (LT)
In progress
This module is for students who don’t plan to use quantitative methods in their own research, but who need to be able to read and understand published research using quantitative methods. You will learn how to interpret graphs, frequency tables and multivariate regression results, and to ask intelligent questions about sampling, methods and statistical inference. The module is aimed at complete beginners, with no prior knowledge of statistics or quantitative methods. |
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. |
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17:00 |
Semiotic and Cultural Semantic Analysis
![]() The module aims to provide students with an introduction to semiotics and cultural semantics. It will overview semiotic and cultural sematic approaches to cultural, literary, and social studies. The focus is on key aspects of semiotics and cultural semantics, including their key concepts and usage in research design and objectives. The module will explore the differences between approaches as opposed perspectives on cultural symbolism. While illustrative examples are mainly drawn from cultural, visual, and literary research, the skills acquired through this module are also applicable to other topics and areas in the social sciences. Outline The module is structured into two lectures and two workshops, each lasting two hours:
Contents Lecture 1 will cover a brief overview of semiotics and cultural semantics, introducing key terms and distinctions between semiotic and semantic approaches to cultural studies. It will address strategies for investigating cultural symbolism and the meaning-making process. Lecture 2 will delve into widely used concepts in both fields, such as cultural meaning, cultural text, symbol, sign, elementary communication structure and sign structure. This focus is on understanding cultural semiosis, symbolisation, and the meaning-making process. The lecture will explore both approaches in discussing cultural values, meanings, texts, and artifacts. Workshop 3 will teach students how to reconstruct cultural code as a key structure for understanding cultural symbolisation. It will include the practical examples of reconstructing the cultural code related to single motherhood through literary texts. Workshop 4 will introduce recent studies in visual grammar, drawing on surveys in children’s picturebooks. This session aims to explore the application of social semiotics in visual studies, emphasizing the analysis of visual elements in cultural symbolism and meaning making. |
17:30 |
Open Source Investigation for Academics (LT)
In progress
Open Source Investigation for Academics is methodology course run by Cambridge’s Digital Verification Corps, in partnership with Cambridge’s Centre of Governance and Human Rights, Cambridge Research Methods and Cambridge Digital Humanities, as well as with the Citizen Evidence Lab at Amnesty International. Please note that places on this module are extremely limited, so please only make a booking if you are able to attend all of the sessions. |
Friday 14 February
09:00 |
Introduction to R (LT)
[Places]
This module introduces the use of R, a free programming language originally developed for statistical data analysis. In this course, we will use R through R Studio, a user-friendly interface. Students will learn:
This module is suitable for students who have no prior experience in programming, but participants will be assumed to have a good working knowledge of basic statistical techniques. For an online example of how R can be used: https://www.ssc.wisc.edu/sscc/pubs/RFR/RFR_Introduction.html''' |
10:00 |
This module aims to provide a practical guide to developing research projects using quantitative methods. It will focus on quantitative research design, key statistical concepts and methods, applied social statistics in education research and social policy evaluation. While the illustrative examples will mainly come from education and policy research, the knowledge and skills acquired through this module may also apply to other quantitative social sciences research projects. Outline The module consists of four lectures (two-hours per session) including:
Contents Lecture 1 will focus on how to design quantitative studies, including formulating research questions, engaging with theoretical and empirical evidence, developing hypothesises, as well as preparing relevant data. Lecture 2 will cover some of the widely used statistical toolkits for data description and hypothesis testing, such as z-score, conference intervals, parametric and non-parametric tests, correlation and regression analyses. Lecture 3 applies the principles of research design and key statistical methods to examples drawn from education research. It will highlight regression analyses and the interpretation of statistical outputs. Lecture 4 will introduce causal inference methods, such as instrumental variables, difference-in-differences and regression discontinuity design, which are commonly used in social policy evaluation. |
14:00 |
Factor Analysis
[Places]
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.
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Introduction to R (LT)
[Places]
This module introduces the use of R, a free programming language originally developed for statistical data analysis. In this course, we will use R through R Studio, a user-friendly interface. Students will learn:
This module is suitable for students who have no prior experience in programming, but participants will be assumed to have a good working knowledge of basic statistical techniques. For an online example of how R can be used: https://www.ssc.wisc.edu/sscc/pubs/RFR/RFR_Introduction.html''' |
Monday 17 February
09:00 |
Meta-Analysis
[Places]
This module offers an introduction to meta-analysis, a powerful statistical technique that enables researchers to synthesise evidence across multiple studies, using standardised effect sizes for a given research question. During the sessions, students will learn how to calculate treatment effects and standardised effect sizes, exploring questions such as, “What is the effectiveness of a new treatment in reducing anxiety symptoms?” or “How does physical activity correlate with cognitive decline?” Meta-analysis will also enable the testing of associations between variables across the literature, providing a comprehensive assessment of both the strength and direction of these relationships. For example, it allows researchers to examine the association between specific risk factors, such as smoking, and health outcomes like cardiovascular disease, or to evaluate how a psychological risk factor, such as chronic stress, correlates with mental health outcomes like depression. The module equips students with essential skills to draw statistically rigorous conclusions from literature reviews, making it especially valuable for those seeking to enhance the rigour and coherence of their research synthesis in the health and psychological sciences. |
14:00 |
Public Policy Analysis
In progress
The analysis of policy depends on many disciplines and techniques and so is difficult for many researchers to access. This module provides a mixed perspective on policy analysis, taking both an academic and a practitioner perspective. This is because the same tools and techniques can be used in academic research on policy options and change as those used in practice in a policy environment. This course is provided as three 2-hour sessions. No direct analysis work will be done in the sessions themselves, but some sample data and questions will be provided for students who wish to take the material into practice. |
Content analysis has been widely used to study different sources of data, such as interviews, conversations, speeches, and other texts. This module adopts an interactive approach, where students are introduced to the key elements of content analysis, how to conduct content analysis, and a range of examples of the use of content analysis. This module offers a practical workshop where students have a hands-on opportunity to practice elements of content analysis, and a clinic, where students are given one-to-one opportunities to ask questions at the end of the course respectively. |
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Panel Data Analysis
![]() Panel data consists of repeated observations measured at multiple time points, collected from multiple individuals, entities, or subjects over a period of time. For instance, child A’s numeracy test score in Year 1, Year 2, Year 3 and Year 4. Country B’s GDP per capita in year 2020, 2021, 2022 and 2023. Panel data analysis, as a subset of longitudinal data analysis, is particularly useful for addressing research questions that try to understand how variables change over time and how individual units differ in their responses to changes. An example research question could be: how do children's numeracy scores vary across different socioeconomic backgrounds, and how have these disparities changed over the years? Panel data analysis holds several advantages, such as (1) increased statistical efficiency, (2) more effective at controlling for unobserved individual or entity-specific effects, and (3) more capable to study the dynamics of relationships over time. Over the course of this module, participants will learn how to work with panel data. Through hands-on exercises and practical examples, participants will gain proficiency in data manipulation, visualisation, and advanced statistical techniques tailored specifically for panel data. It is suitable for postgraduate students and researchers at any stages of their study and research. However, foundational Stata skills are required. |
Tuesday 18 February
09:00 |
Meta-Analysis
[Places]
This module offers an introduction to meta-analysis, a powerful statistical technique that enables researchers to synthesise evidence across multiple studies, using standardised effect sizes for a given research question. During the sessions, students will learn how to calculate treatment effects and standardised effect sizes, exploring questions such as, “What is the effectiveness of a new treatment in reducing anxiety symptoms?” or “How does physical activity correlate with cognitive decline?” Meta-analysis will also enable the testing of associations between variables across the literature, providing a comprehensive assessment of both the strength and direction of these relationships. For example, it allows researchers to examine the association between specific risk factors, such as smoking, and health outcomes like cardiovascular disease, or to evaluate how a psychological risk factor, such as chronic stress, correlates with mental health outcomes like depression. The module equips students with essential skills to draw statistically rigorous conclusions from literature reviews, making it especially valuable for those seeking to enhance the rigour and coherence of their research synthesis in the health and psychological sciences. |
Panel Data Analysis
![]() Panel data consists of repeated observations measured at multiple time points, collected from multiple individuals, entities, or subjects over a period of time. For instance, child A’s numeracy test score in Year 1, Year 2, Year 3 and Year 4. Country B’s GDP per capita in year 2020, 2021, 2022 and 2023. Panel data analysis, as a subset of longitudinal data analysis, is particularly useful for addressing research questions that try to understand how variables change over time and how individual units differ in their responses to changes. An example research question could be: how do children's numeracy scores vary across different socioeconomic backgrounds, and how have these disparities changed over the years? Panel data analysis holds several advantages, such as (1) increased statistical efficiency, (2) more effective at controlling for unobserved individual or entity-specific effects, and (3) more capable to study the dynamics of relationships over time. Over the course of this module, participants will learn how to work with panel data. Through hands-on exercises and practical examples, participants will gain proficiency in data manipulation, visualisation, and advanced statistical techniques tailored specifically for panel data. It is suitable for postgraduate students and researchers at any stages of their study and research. However, foundational Stata skills are required. |
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10:00 |
This module offers an introduction to the use of case studies in social science research. It includes an exploration of paradigmatic, methodological, practical, and ethical considerations. This module offers a practical workshop where students have a hands-on opportunity to practice elements of case study research, and a clinic, where students are given one-to-one opportunities to ask questions at the end of the course respectively. |
11:00 |
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. |
14:00 |
This module is an extension of the three previous modules in the Basic Statistics stream, and introduces more complex and nuanced aspects of the theory and practice of mutivariate analysis. Students will learn the theory behind the methods covered, how to implement them in practice, how to interpret their results, and how to write intelligently about their findings. 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, Stata. Topics covered include:
To get the most out of the course, you should also expect to spend some time between sessions building your own statistical models. |
This module offers an introduction to the use of action research in social science research. It includes an exploration of paradigmatic, methodological, practical, and ethical considerations. This module offers a practical workshop where students have a hands-on opportunity to practice elements of action research, and a clinic, where students are given one-to-one opportunities to ask questions at the end of the course. |
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Conversation and Discourse Analysis
[Places]
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. |
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This module is an extension of the three previous modules in the Basic Statistics stream, and introduces more complex and nuanced aspects of the theory and practice of mutivariate analysis. Students will learn the theory behind the methods covered, how to implement them in practice, how to interpret their results, and how to write intelligently about their findings. 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 packages, R and R-Studio. Topics covered include:
To get the most out of the course, you should also expect to spend some time between sessions building your own statistical models. |
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16:00 |
This module is an extension of the three previous modules in the Basic Statistics stream, and introduces more complex and nuanced aspects of the theory and practice of mutivariate analysis. Students will learn the theory behind the methods covered, how to implement them in practice, how to interpret their results, and how to write intelligently about their findings. 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, Stata. Topics covered include:
To get the most out of the course, you should also expect to spend some time between sessions building your own statistical models. |
Archival Research
![]() 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. |