Social Sciences Research Methods Programme course timetable

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.

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 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.

This is an introductory course for students who have little or no prior training in statistics.

The module is divided between pre-recorded mini-lectures, in which you'll learn the relevant theory, and hands-on live practical sessions in Zoom, in which you will learn how to analyse real data using the statistical package, Stata.

You will learn:

• The key features of quantitative analysis, and how it differs from other types of empirical analysis
• The basics of formal hypothesis testing
• Basic concepts: what is a variable? what is the distribution of a variable? and how can we best represent a distribution graphically?
• Features of statistical distributions: measures of central tendency and dispersion
• The normal distribution
• Why statistical testing works
• Statistical methods used to test simple hypotheses
• How to use Stata

This is an introductory course for students who have little or no prior training in statistics.

The module is divided between pre-recorded mini-lectures, in which you'll learn the relevant theory, and hands-on live practical sessions in Zoom, in which you will learn how to analyse real data using the statistical package, Stata.

You will learn:

• The key features of quantitative analysis, and how it differs from other types of empirical analysis
• The basics of formal hypothesis testing
• Basic concepts: what is a variable? what is the distribution of a variable? and how can we best represent a distribution graphically?
• Features of statistical distributions: measures of central tendency and dispersion
• The normal distribution
• Why statistical testing works
• Statistical methods used to test simple hypotheses
• How to use Stata

This is an introductory course for students who have little or no prior training in statistics.

The module is divided between pre-recorded mini-lectures, in which you'll learn the relevant theory, and hands-on live practical sessions in Zoom, in which you will learn how to analyse real data using the statistical package, Stata.

You will learn:

• The key features of quantitative analysis, and how it differs from other types of empirical analysis
• The basics of formal hypothesis testing
• Basic concepts: what is a variable? what is the distribution of a variable? and how can we best represent a distribution graphically?
• Features of statistical distributions: measures of central tendency and dispersion
• The normal distribution
• Why statistical testing works
• Statistical methods used to test simple hypotheses
• How to use Stata

This is an introductory course for students who have little or no prior training in statistics.

The module is divided between pre-recorded mini-lectures, in which you'll learn the relevant theory, and hands-on live practical sessions in Zoom, in which you will learn how to analyse real data using the statistical package, Stata.

You will learn:

• The key features of quantitative analysis, and how it differs from other types of empirical analysis
• The basics of formal hypothesis testing
• Basic concepts: what is a variable? what is the distribution of a variable? and how can we best represent a distribution graphically?
• Features of statistical distributions: measures of central tendency and dispersion
• The normal distribution
• Why statistical testing works
• Statistical methods used to test simple hypotheses
• How to use Stata

This is an introductory course for students who have little or no prior training in statistics.

The module is divided between pre-recorded mini-lectures, in which you'll learn the relevant theory, and hands-on live practical sessions in Zoom, in which you will learn how to analyse real data using the statistical package, Stata.

You will learn:

• The key features of quantitative analysis, and how it differs from other types of empirical analysis
• The basics of formal hypothesis testing
• Basic concepts: what is a variable? what is the distribution of a variable? and how can we best represent a distribution graphically?
• Features of statistical distributions: measures of central tendency and dispersion
• The normal distribution
• Why statistical testing works
• Statistical methods used to test simple hypotheses
• How to use Stata

This is an introductory course for students who have little or no prior training in statistics.

The module is divided between pre-recorded mini-lectures, in which you'll learn the relevant theory, and hands-on live practical sessions in Zoom, in which you will learn how to analyse real data using the statistical package, Stata.

You will learn:

• The key features of quantitative analysis, and how it differs from other types of empirical analysis
• The basics of formal hypothesis testing
• Basic concepts: what is a variable? what is the distribution of a variable? and how can we best represent a distribution graphically?
• Features of statistical distributions: measures of central tendency and dispersion
• The normal distribution
• Why statistical testing works
• Statistical methods used to test simple hypotheses
• How to use Stata

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.