Social Sciences Research Methods Programme course timetable

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 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.

NB. This module has pre-recorded lectures which need watching before the live workshop session, advertised, below."

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 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 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.

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.

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 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 course provides an introduction to the management and analysis of qualitative data using Atlas.ti. It is divided between pre-recorded lectures, in which you’ll learn the relevant strategies and techniques, and hands-on live practical sessions in Zoom, in which you will learn how to analyse qualitative data using the software.

The sessions will introduce participants to the following:

• consideration of the advantages and limitations of using qualitative analysis software
• setting-up a research project in Atlas.ti
• use of Atlas.ti's menus and tool bars
• importing and organising data
• starting data analysis using Atlas.ti’s coding tools
• exploring data using query and visualization tools

Please note: Atlas.ti for Mac will not be covered.

This course provides an introduction to the management and analysis of qualitative data using Atlas.ti. It is divided between pre-recorded lectures, in which you’ll learn the relevant strategies and techniques, and hands-on live practical sessions in Zoom, in which you will learn how to analyse qualitative data using the software.

The sessions will introduce participants to the following:

• consideration of the advantages and limitations of using qualitative analysis software
• setting-up a research project in Atlas.ti
• use of Atlas.ti's menus and tool bars
• importing and organising data
• starting data analysis using Atlas.ti’s coding tools
• exploring data using query and visualization tools

Please note: Atlas.ti for Mac will not be covered.

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.

Historically, qualitative research has been criticised for being less rigorous than quantitative research through not fulfilling quality standards such as objectivity, validity, and reliability. This leads to questions whether qualitative research can fulfil these specific markers of rigour, how it can come as close as possible to fulfilling them, and whether qualitative research should at all attempt to live up to these understandings of research quality. Responding to this debate, many methodologists have argued for the need of translating objectivity, validity, and reliability within qualitative research designs.

The discussion of rigour is a loaded one, among methodologists of all three research approaches (qualitative, quantitative, mixed-methods) as well as mong qualitative researchers themselves. This course introduces different quality strategies for qualitative research to help students make informed decisions for improving their own empirical work and to better judge the rigour of empirical qualitative research done by others.

The course will provide students with an introduction to the popular and powerful statistics package Stata. Stata is commonly used by analysts in both the social and natural sciences, and is the statistics package used most widely by the SSRMC. You will learn:

• How to open and manage a dataset in Stata
• How to recode variables
• How to select a sample for analysis
• The commands needed to perform simple statistical analyses in Stata
• Where to find additional resources to help you as you progress with Stata

The course is intended for students who already have a working knowledge of statistics - it's designed primarily as a ""second language"" course for students who are already familiar with another package, perhaps R or SPSS. Students who don't already have a working knowledge of applied statistics should look at courses in our Basic Statistics Stream.

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.