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Cambridge Research Methods

Cambridge Research Methods (CaRM) course timetable

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Sun 16 Feb – Thu 20 Feb

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Monday 17 February

09:00
Meta-Analysis (1 of 2) POSTPONED 09:00 - 13:00 Titan Teaching Room 1, New Museums Site

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 (3 of 3) In progress 14:00 - 16:00 Department of Genetics, Biffen Lecture Theatre

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.

Introduction to Content Analysis new (1 of 3) [Full] 14:00 - 16:00 Titan Teaching Room 1, New Museums Site

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.

Panel Data Analysis new (1 of 2) [Places] 14:00 - 18:00 University Centre, Cormack Room

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 (2 of 2) POSTPONED 09:00 - 13:00 Titan Teaching Room 2, New Museums Site

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 new (2 of 2) [Places] 09:00 - 13:00 University Centre, Hicks Room

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.

10:00
Introduction to Using Case Studies in Research new (1 of 3) [Full] 10:00 - 12:00 Titan Teaching Room 3, New Museums Site

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
Advanced Topics in Data Preparation Using R (LT) new (1 of 4) [Places] 11:00 - 13:00 Titan Teaching Room 1, New Museums Site

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
Further Topics in Multivariate Analysis Using Stata (FTMA-1) (1 of 4) Not bookable 14:00 - 16:00 CaRM pre-recorded lecture(s) on Moodle

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:

  • Interaction effects in regression models: how to estimate these and how to interpret them
  • Marginal effects from interacted models
  • Ordered and categorical discrete dependent variable models (ordered and multinomial logit and probit)

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

Introduction to Using Action Research in Social Science new (1 of 3) [Places] 14:00 - 16:00 University Centre, Hicks Room

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.

Conversation and Discourse Analysis (1 of 4) [Places] 14:00 - 15:30 New Museums Site, Hopkinson Lecture Theatre

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.

Further Topics in Multivariate Analysis Using R (FTMA-4) (1 of 3) Not bookable 14:00 - 16:00 CaRM pre-recorded lecture(s) on Moodle

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:

  • Interaction effects in regression models: how to estimate these and how to interpret them
  • Marginal effects from interacted models
  • Ordered and categorical discrete dependent variable models (ordered and multinomial logit and probit)

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

16:00
Further Topics in Multivariate Analysis Using Stata (FTMA-1) (2 of 4) Not bookable 16:00 - 18:00 University Centre, Hicks Room

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:

  • Interaction effects in regression models: how to estimate these and how to interpret them
  • Marginal effects from interacted models
  • Ordered and categorical discrete dependent variable models (ordered and multinomial logit and probit)

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 new (1 of 3) [Full] 16:00 - 17:00 Titan Teaching Room 1, New Museums Site

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.

Wednesday 19 February

09:00
Text Analysis/Mining in R new (1 of 2) [Places] 09:00 - 13:00 Titan Teaching Room 1, New Museums Site

Text analysis, also known as text mining or natural language processing (NLP), involves the examination and extraction of meaningful information from text-based data. This includes various tasks such as sentiment analysis and topic modelling. It is a very useful technique for quantifying qualitative data, which serves as a great asset for mixed-method research projects. This technique also allows researchers to move towards participant-centred research. For example, traditional survey data use forced-choice questions to explore people’s satisfaction with clinical services by asking “to what extent do you agree or disagree” and allowing participants to rate their experience using a five-point Likert scale. The results offer limited insights into the nuances of participants' experiences and opinions. In contrast, text analysis allows researchers to explore the richness of open-ended responses, uncovering deeper insights and subtleties that fixed-response options cannot capture.

Text analysis is also useful for understanding large volumes of textual data, uncovering hidden patterns, and generating insights that can inform decision-making. Continuing the example of patients' satisfaction with local clinical support, patients might provide detailed comments about their experiences with wait times, staff behaviour, patient communication skills, and the cleanliness of facilities. Text analysis can help identify common complaints, such as long waiting periods or unresponsive staff, as well as positive feedback, like professional and compassionate care. Sentiment analysis can further quantify the overall tone of patient feedback, distinguishing between positive, negative, and neutral sentiments.

Throughout this module, students will learn the fundamentals of text analysis. Through hands-on exercises and practical examples, participants will gain proficiency in text preprocessing, data visualisation, and advanced text analysis techniques.

14:00
Feminist Research Practice (3 of 4) In progress 14:00 - 15:15 University Centre, Cormack Room

This series of workshops are aimed at students interested in interdisciplinary and feminist research practice. The course revolves around a simple query: what makes research feminist? It is the starting point to engage with classic and more contemporary writings on feminist knowledge production to answer some of the following questions: what are the ‘proper’ objects of feminist research? Who can do feminist research? Why do we do feminist research, and what is its relevance? Who do we cite in our research? We will have in-class discussions and hands-on assignments that will allow students to practice some of the main debates we will read about.

Further Topics in Multivariate Analysis Using R (FTMA-3) (1 of 4) Not bookable 14:00 - 16:00 CaRM pre-recorded lecture(s) on Moodle

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:

  • Interaction effects in regression models: how to estimate these and how to interpret them
  • Marginal effects from interacted models
  • Ordered and categorical discrete dependent variable models (ordered and multinomial logit and probit)

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

15:00
Critical Approaches to Discourse Analysis (LT) (2 of 4) In progress 15:00 - 16:30 New Museums Site, Cockcroft Lecture Theatre

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
Further Topics in Multivariate Analysis Using R (FTMA-3) (2 of 4) Not bookable 16:00 - 18:00 University Centre, Hicks Room

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:

  • Interaction effects in regression models: how to estimate these and how to interpret them
  • Marginal effects from interacted models
  • Ordered and categorical discrete dependent variable models (ordered and multinomial logit and probit)

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

17:00
Ethical Review for Social Science Research (LT) new (2 of 2) In progress 17:00 - 18:30 CaRM Zoom

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.

Thursday 20 February

09:00
Text Analysis/Mining in R new (2 of 2) [Places] 09:00 - 13:00 Titan Teaching Room 1, New Museums Site

Text analysis, also known as text mining or natural language processing (NLP), involves the examination and extraction of meaningful information from text-based data. This includes various tasks such as sentiment analysis and topic modelling. It is a very useful technique for quantifying qualitative data, which serves as a great asset for mixed-method research projects. This technique also allows researchers to move towards participant-centred research. For example, traditional survey data use forced-choice questions to explore people’s satisfaction with clinical services by asking “to what extent do you agree or disagree” and allowing participants to rate their experience using a five-point Likert scale. The results offer limited insights into the nuances of participants' experiences and opinions. In contrast, text analysis allows researchers to explore the richness of open-ended responses, uncovering deeper insights and subtleties that fixed-response options cannot capture.

Text analysis is also useful for understanding large volumes of textual data, uncovering hidden patterns, and generating insights that can inform decision-making. Continuing the example of patients' satisfaction with local clinical support, patients might provide detailed comments about their experiences with wait times, staff behaviour, patient communication skills, and the cleanliness of facilities. Text analysis can help identify common complaints, such as long waiting periods or unresponsive staff, as well as positive feedback, like professional and compassionate care. Sentiment analysis can further quantify the overall tone of patient feedback, distinguishing between positive, negative, and neutral sentiments.

Throughout this module, students will learn the fundamentals of text analysis. Through hands-on exercises and practical examples, participants will gain proficiency in text preprocessing, data visualisation, and advanced text analysis techniques.

10:00
Evaluation Methods (3 of 8) In progress 10:00 - 11:15 CaRM pre-recorded lecture(s) on Moodle

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.

14:00
Evaluation Methods (4 of 8) In progress 14:00 - 15:15 University Centre, Cormack Room

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.

Introduction to Content Analysis new (2 of 3) [Full] 14:00 - 16:00 Titan Teaching Room 1, New Museums Site

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.

15:30
Ethnographic Methods (3 of 5) In progress 15:30 - 17:00 McCrum Lecture Theatre

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: Digital Ethnography Part I (Dr Summer Qassim)
  • Session 3: Digital Ethnography Part II (Dr Summer Qassim)
  • Session 4: Youth-centred and Symmetric Classroom Ethnography (Dr Angela Giattino)
  • Session 5: Multimodal Youth-led Citizen Social Science (Dr Kelly Fagan Robinson)

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: 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) (3 of 4) In progress 16:00 - 18:00 CaRM Zoom

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.

Data Visualisation, Inclusive Cartography and Python new (2 of 2) [Places] 16:00 - 18:00 Titan Teaching Room 1, New Museums Site

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.

17:00
Semiotic and Cultural Semantic Analysis new (2 of 4) CANCELLED 17:00 - 19:00 University Centre, Cormack Room

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:

  • Lecture 1: Introduction to Semiotics and Cultural Semantics
  • Lecture 2: Key Semiotic and Cultural Semantic Concepts and Methods
  • Workshop 3: Reconstruction of Cultural Code
  • Workshop 4: Social Semiotic in Visual Studies

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) (5 of 8) In progress 17:30 - 18:30 CaRM Zoom

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.