Cambridge Research Methods (CaRM) course timetable
Wednesday 21 February
09:00 |
Structural Equation Modelling
Finished
This intensive course on structural equation modelling will provide an introduction to SEM using the statistical software Stata. The aim of the course is to introduce structural equation modelling as an analytical framework and to familiarize participants with the applications of the technique in the social sciences. The application of the structural equation modelling framework to a variety of social science research questions will be illustrated through examples of published papers. The examples used are drawn from recent papers as well as from publications from the early days of the technique; some use path analysis using cross-national data, others confirmatory factor analysis, and other still full structural models, to test particular hypotheses. Some example papers may be found below, though they should not be treated as the gold standard, rather as an illustration of the variety of approaches and reporting techniques within SEM.
Students will engage in a critique of such examples, with the aim of gaining a better understanding of the SEM framework, as well as its application to real-life data. To further facilitate this application focus, the theoretical introduction will be accompanied by practical examples based on real, publicly-available data. |
Propensity Score Matching
Finished
Propensity score matching (PSM) is a technique that simulates an experimental study in an observational data set in order to estimate a causal effect. In an experimental study, subjects are randomly allocated to “treatment” and “control” groups; if the randomisation is done correctly, there should be no differences in the background characteristics of the treated and non-treated groups, so any differences in the outcome between the two groups may be attributed to a causal effect of the treatment. An observational survey, by contrast, will contain some people who have been subject to the “treatment” and some people who have not, but they will not have not been randomly allocated to those groups. The characteristics of people in the treatment and control groups may differ, so differences in the outcome cannot be attributed to the treatment. PSM attempts to mimic the experimental situation trial by creating two groups from the sample, whose background characteristics are virtually identical. People in the treatment group are “matched” with similar people in the control group. The difference between the treatment and control groups in this case should may therefore more plausibly be attributed to the treatment itself. PSM is widely applied in many disciplines, including sociology, criminology, economics, politics, and epidemiology. The module covers the basic theory of PSM, the steps in the implementation (e.g. variable choice for matching and types of matching algorithms), and assessment of matching quality. We will also work through practical exercises using Stata, in which students will learn how to apply the technique to the analysis of real data and how to interpret the results. |
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13:00 |
Qualitative research methods are often used in the social sciences to learn more about the world and are often considered to be particularly appropriate for people who might be considered vulnerable. The goal of this course is to encourage students to think critically about the concept of 'vulnerability'; to offer a practical guide to conducting qualitative research that responds to the vulnerabilities of participants and researchers; and to explore ways of challenging and resisting research practices that could be extractive or harmful. It will be highly discursive and will draw throughout on ‘real life’ research examples. The course will be of interest to students who are conducting, or planning to conduct, research with a group considered vulnerable, and will also be of interest to students who want to critically engage with such research in their field. For a more detailed outline of each session please see the 'Learning Outcomes' section below. Content warning: Throughout, the course will cover the experience and effects of different forms of trauma. The first session will touch on the lecturer's research with people affected by criminal exploitation. Content warnings for other sessions will be raised at the end of the preceding session and emailed, where necessary. If you have any concerns you would like to raise with me regarding these matters, please do email the lecturer. |
14:00 |
Data Visualisation Using Python
Finished
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. |
15:30 |
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. |
Thursday 22 February
10:00 |
Evaluation Methods
Finished
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. |
Atlas.ti
Finished
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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14:00 |
Evaluation Methods
Finished
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
Finished
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: Multimodal Ethnography Part I 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: Digital Ethnography In this session, we discuss anthropologically-informed ethnographic practices of "the digital." In order to do so, we first define what is meant by "digital", as well as delineate the various ways in which the digital presents itself in everyday life, in order to ascertain the appropriate ethnographic methods for each. The session combines theoretical conversations, research ethics, and practical tips on how to conduct research on digital platforms like social media sites, messaging apps, immersive virtual games, and how to mix methods when encountering intersections thereof. Session 4: Multimodal Ethnography Part II In this session, we will revisit multimodal approaches and reflect on relational dynamics in the field with particular attention to the ways in which methods have been used to address power imbalances in research methods, representation, and analysis. In particular we will think through the role of multimodal approaches as part of participant-led research. We will discuss how researchers can foster greater legibility and inclusion of research participants-- particularly those who are more marginalised --in discussions, debates and decisions about their lives and futures, equalizing, as far as possible, power hierarchies and epistemic imbalances. |
16:00 |
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. |
Friday 23 February
09:00 |
Time Series Analysis
Finished
This module introduces the time series techniques relevant to forecasting in social science research and computer implementation of the methods. Background in basic statistical theory and regression methods is assumed. Topics covered include time series regression, Vector Error Correction and Vector Autoregressive Models, Time-varying Volatility, and ARCH models. The study of applied work is emphasized in this non-specialist module. Topics include:
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14:00 |
Time Series Analysis
Finished
This module introduces the time series techniques relevant to forecasting in social science research and computer implementation of the methods. Background in basic statistical theory and regression methods is assumed. Topics covered include time series regression, Vector Error Correction and Vector Autoregressive Models, Time-varying Volatility, and ARCH models. The study of applied work is emphasized in this non-specialist module. Topics include:
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The neurodiversity module is designed for researchers and academics who wish to expand their knowledge of neurodiversity-friendly practices in research. The module centres around 5 key themes and covers the following: • What is neurodiversity? • How does neurodiversity impact research? • What are specific learning difficulties (SpLD)? • How do they impact your participants, and the positionality of the researcher? • Delivering useful approaches and resources Highlighting the difference between 'integration' and 'inclusion', the content will equip researchers to design the most effective research methods to increase inclusion and lessen the need for 'bolton' practices. The course will also discuss the difference between research design and delivery at the individual level versus the strategic level to be develop universal methods. The course will be practically useful for those wishing to learn about equipment, tools, and techniques additionally available to support researchers and participants alike, and how these can be funded through the University and/or other funding providers. |
Monday 26 February
10:00 |
Survey Research and Design (LT)
Finished
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. |
16:00 |
Survey Research and Design (LT)
Finished
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. |
Tuesday 27 February
09:00 |
Introduction to Python (LT)
Finished
This module introduces the use of Python, a free programming language originally developed for statistical data analysis. 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. |
13:00 |
Introduction to Python (LT)
Finished
This module introduces the use of Python, a free programming language originally developed for statistical data analysis. 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. |
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. Half of the module is based in the lecture theatre; the other half is lab-based, in which students will work through practical exercises using the statistical software 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. |
Conversation and Discourse Analysis
Finished
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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16:00 |
Archival Research
Finished
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. The final session on overseas archives is given as part of the History Faculty general training. |
17:00 |
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 is methodology course run by Cambridge’s Digital Verification Corps, in partnership with Cambridge’s Centre of Governance and Human Rights, Social Sciences Research Methods Programme and Cambridge Digital Humanities, as well as with the Citizen Evidence Lab at Amnesty International. NB. Places on this module are extremely limited, so please only make a booking if you are able to attend all of the sessions. |
Wednesday 28 February
09:00 |
Structural Equation Modelling
Finished
This intensive course on structural equation modelling will provide an introduction to SEM using the statistical software Stata. The aim of the course is to introduce structural equation modelling as an analytical framework and to familiarize participants with the applications of the technique in the social sciences. The application of the structural equation modelling framework to a variety of social science research questions will be illustrated through examples of published papers. The examples used are drawn from recent papers as well as from publications from the early days of the technique; some use path analysis using cross-national data, others confirmatory factor analysis, and other still full structural models, to test particular hypotheses. Some example papers may be found below, though they should not be treated as the gold standard, rather as an illustration of the variety of approaches and reporting techniques within SEM.
Students will engage in a critique of such examples, with the aim of gaining a better understanding of the SEM framework, as well as its application to real-life data. To further facilitate this application focus, the theoretical introduction will be accompanied by practical examples based on real, publicly-available data. |
14:00 |
An Introduction to Embodied Inquiry
Finished
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. |
Data Visualisation Using Python
Finished
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. |
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15:30 |
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. |
16:00 |
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. |