Cambridge Research Methods course timetable

Longitudinal data analysis is a statistical method used to examine data collected from the same subjects or entities over multiple time points. This type of data analysis is particularly valuable for understanding how variables change over time and for investigating trends, patterns, and relationships within a dynamic context. For instance, how does children’s early home environment affect their future mathematical development?

Longitudinal data analysis holds several advantages, such as (1) understanding individual-level trajectories, enabling a deeper understanding of how different subjects respond to interventions or external factors over time, (2) supporting stronger causal inference by tracking changes before and after an intervention and (3) accounting for heterogeneity since it recognises that not all subjects respond uniformly to changes over time.

Over the course of this module, participants will learn how to work with longitudinal data. Through hands-on exercises and practical examples, participants will gain proficiency in data manipulation, visualisation, and advanced statistical techniques tailored specifically for longitudinal data. From understanding growth trajectories to uncovering causal relationships, this module will empower participants to navigate the complexities of longitudinal data with confidence. It is suitable for postgraduate students and researchers at any stages of their study and research. However, foundational Stata skills are required.

This module follows on from Foundations in Applied statistics, and will teach you the basics of common bivariate techniques (that is, techniques that examine the associations between two variables). The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to apply these techniques to the analysis of real data.

Techniques to be covered include:

• Cross-tabulations
• Scatterplots
• Covariance and correlation
• Nonparametric methods
• Two-sample t-tests
• ANOVA
• Ordinary Least Squares (OLS)

For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class.

This module follows on from Foundations in Applied statistics, and will teach you the basics of common bivariate techniques (that is, techniques that examine the associations between two variables). The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to apply these techniques to the analysis of real data.

Techniques to be covered include:

• Cross-tabulations
• Scatterplots
• Covariance and correlation
• Nonparametric methods
• Two-sample t-tests
• ANOVA
• Ordinary Least Squares (OLS)

For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class.

This module introduces focus group research as a qualitative research method. Attention is given to the key elements and methodological consideration of conducting focus group research. It also explores the process of conducting focus group research, where students are given the opportunity to design focus group questions, and to experience the role of researcher in the practical workshops.

Longitudinal data analysis is a statistical method used to examine data collected from the same subjects or entities over multiple time points. This type of data analysis is particularly valuable for understanding how variables change over time and for investigating trends, patterns, and relationships within a dynamic context. For instance, how does children’s early home environment affect their future mathematical development?

Longitudinal data analysis holds several advantages, such as (1) understanding individual-level trajectories, enabling a deeper understanding of how different subjects respond to interventions or external factors over time, (2) supporting stronger causal inference by tracking changes before and after an intervention and (3) accounting for heterogeneity since it recognises that not all subjects respond uniformly to changes over time.

Over the course of this module, participants will learn how to work with longitudinal data. Through hands-on exercises and practical examples, participants will gain proficiency in data manipulation, visualisation, and advanced statistical techniques tailored specifically for longitudinal data. From understanding growth trajectories to uncovering causal relationships, this module will empower participants to navigate the complexities of longitudinal data with confidence. It is suitable for postgraduate students and researchers at any stages of their study and research. However, foundational Stata skills are required.

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.

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.

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.

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.

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:

• Ways of reading data into R
• How to manipulate data in major data types
• How to draw basic graphs and figures with R
• How to summarise data using descriptive statistics
• How to perform basic inferential statistics

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'''

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.

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.

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.

Face-to-face interviews are used to collect a wide range of information in the social sciences. They are appropriate for the gathering of information on individual and institutional patterns of behaviour; complex histories or processes; identities and cultural meanings; routines that are not written down; and life-history events. Face-to-face interviews thus comprise an appropriate method to generate information on individual behaviour, the reasons for certain patterns of acting and talking, and the type of connection people have with each other.

The first session provides an overview of interviewing as a social research method, then focuses on the processes of organising and conducting qualitative interviews. The second session explores the ethics and practical constraints of interviews as a research method, particularly relevant when attempting to engage with marginalised or stigmatised communities. The third session focuses on organisation and analysis after interviews, including interpretation through coding and close reading.

In Lent Term, the online resources are supported by 1 x zoom Q&A session, and 2 x in-person workshops. During the first in-person workshop students will role-play interviews using the scenarios outlined in the course moodle pages. During the second in-person workshop students will work in pairs on their interview material (at whatever stage of the process: whether writing interview questions, coding or analysing data) in order to receive advice and support in taking the interview material/data to the next stage of the research process.

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.

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.

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.