Theme: Elements of Social Science Research

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

• Session 1: The Ethnographic Method (Dr Andrew Sanchez)
• Session 2: Multimodal Ethnography Part I (Dr Kelly Fagan Robinson)
• Session 3: Digital Ethnography (Summer Qassim)
• Session 4: Multimodal Ethnography Part II (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: 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.

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.

The aim of this course is to introduce students to comparative historical research methods and encourage them to engage with practical exercises, to distinguish between different approaches in comparative historical research methods in social sciences.

Through the reading and seminars students will learn how to distinguish between different texts, theorists and approaches and learn how to apply these approaches to their own research and writing.

Comparative historical sociology studies major social transformations over periods of time and across different states, societies, and regions.

This module is for anyone considering studying on an SSRMP module but not sure which one/s to choose. It provides an overview of the research process and issues in research design. Through reflection on a broad overview of empirical research, the module aims to encourage students to consider where they may wish to develop their research skills and knowledge. The module will signpost the different modules, both quantitative and qualitative, offered by SSRMP and encourage students to consider what modules might be appropriate for their research and career development.

Please note: This module has pre-recorded lectures which need to be watched before the live workshop session.

Mixed and multi method approaches are increasingly common in the social sciences. Whilst much has been written about the justification, design and benefit of mixed methods, there is correspondingly little published empirical research which rigorously employs such approaches. In this interactive session, we will consider what mixed and multi methods approaches are, when you might use them, and - most importantly - start to think about how you can integrate quantitative and qualitative data (a) across a series of studies and (b) within a single study.

Social Network Analysis (SNA) is “a distinct research perspective in the behavioural and social sciences” because it elevates relationships as the primary unit of analysis when attempting to understand and explain social phenomena (Wasserman and Faust, 1994, p. 4). This methods module will introduce you to network research tools used to explore the social constructs that surround all of us, continuously facilitating and frustrating our individual ambitions. Each of our three sessions will focus on a primary component of modern SNA: relational data collection, network visualisation, and descriptive network statistics and modelling. We will use real relational datasets from historical network studies. Participants will also be encouraged to develop their own relational data and complete a basic descriptive analysis and network visualisation of their data. This module will make use of web-based tools and open-source options in the R environment. However, no previous training in SNA methods or R will be assumed by the instructor.

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.

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 delivered as a mix of lectures and seminars. 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.

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.

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.

This module offers an introduction to the use of case studies in social sciences research. It includes an exploration of paradigmatic, methodological, practical, and ethical considerations.

Using secondary data (that is, data collected by someone else, usually a government agency or large research organisation) has a number of advantages in social science research: sample sizes are usually larger than can be achieved by primary data collection, samples are more nearly representative of the populations they are drawn from, and using secondary data for a research project often represents significant savings in time and money. This short course, taught by Dr Deborah Wiltshire of the UK Data Archive, will discuss the advantages and limitations of using secondary data for research in the social sciences, and will introduce students to the wide range of available secondary data sources. Students will learn how to search online for suitable secondary data by browsing the database of the UK Data Archive.

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 two practical workshops, where students have a hands-on opportunity to practice performing content analysis, followed by guided reflection.

The module provides a practical guide to designing and developing a research project based on quantitative dates. It focuses on key aspects of research design, how to work with theory, identify key concepts and operationalise them with quantitative data. It will explore the use of applied statistical methods for data analysis, their applications in academic research, and how to interpret statistical outputs. Although the illustrative examples are mainly drawn from education and policy research, the statistical and design knowledge and skills acquired via this module are also applicable to other social sciences research topics and areas.

Outline

The module consists of four lectures (two-hours per session) including:

• Lecture 1: Introduction to quantitative research design
• Lecture 2: Key statistical concepts and methods
• Lecture 3: Applied social statistics in education research
• Lecture 4: Education and social policy evaluation

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 graphs, 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 a few causal inference methods, such as matching, instrumental variables, difference-in-differences, and regression discontinuity design, which are commonly used in social policy evaluations.

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.

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.

Web scraping has great potential as a research tool that can be applied across various fields of research including social science and humanities, and allows us to reach beyond the ‘quantitative and qualitative divide’. The programming and code-reading/analysing skills used in web scraping can enhance our understanding of digital power beyond the traditional limits of computing techniques.

This two-hour training module (plus 1-hour online Q&A session) introduces researchers to how to use Python software for web scraping. You will learn what web scraping means, the principles behind it, and ethical considerations, and importantly how to use Python to achieve web scraping. The module provides a good opportunity to learn how to enhance your coding and code-reading skills, from which you can reflect on how digital power especially web scraping and coding is shaping contemporary research. The training is programming beginner friendly.