Cambridge Research Methods course timetable

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.

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.

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.

This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself, and to interpret and write about your results intelligently.

Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software.

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

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.

This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself, and to interpret and write about your results intelligently.

Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software.

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

This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself, and to interpret and write about your results intelligently.

Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software.

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

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.

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.

This short course will be an opportunity for us to engage with a variety of decolonial theories and methodologies and to consider the implications of these approaches on a variety of elements of our research processes. Each session will consist of a presentation which engages with selected decolonial theory and methods, examples of ‘methods in practice’ drawn from across the social sciences and time for self-reflexive individual and group discussion.

The course will not prescriptively define and provide instructions for ‘decolonial methods’, but instead be a space to consider a variety of ways in which scholars, activists and those working outside the traditional boundaries of ‘the academy’ have thought decolonially about social science research methodologies. The course’s workshop format will enable opportunities for us to apply some of these insights to our own scholarship.

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.

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.

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.

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.

This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself, and to interpret and write about your results intelligently.

Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software.

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

This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself, and to interpret and write about your results intelligently.

Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software.

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