Social Sciences Research Methods Programme course timetable

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

The course offers an introduction to critical approaches to discourse analysis with a focus on linking theory with method. Students will be equipped with the conceptual and practical knowledge to analyse a broad range of issues based on text documents. The topic of the course will be approached from a broadly Foucauldian angle, considering discourse as social practices that create reality rather than merely reflect it. The emphasis of the three lectures will thus be less upon what is known as ‘conversation analysis’ or ‘content analysis’ and more on text and speech as gateways to understand the making of social phenomena and corresponding power relations.

In the first session, we will discuss the theoretical ideas and origins behind discourse analysis. In the second lecture, we will dive into methodological discussions around doing discourse analysis. In the third session, we will apply the method of discourse analysis with support of a qualitative text analysis software.

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

With such a large variety of qualitative research methods to choose from, creating a research design can be confusing and difficult without a sufficiently informed overview. This module aims to provide an overview by introducing qualitative data collection and analysis methods commonly used in social science research. The module provides a foundation for other SSRMP qualitative methods modules such as ethnography, discourse analysis, interviews, or diary research. Knowing what is ‘out there’ will help a researcher purposefully select further modules to study on, provide readings to deepen knowledge on specific methods, and will facilitate a more informed research design that contributes to successful empirical research.

NB. This module has video content that needs watching prior to the advertised start date, which can be found on the Moodle page.

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.

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

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.

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 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

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 Michaelmas 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.

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

The course offers an introduction to critical approaches to discourse analysis with a focus on linking theory with method. Students will be equipped with the conceptual and practical knowledge to analyse a broad range of issues based on text documents. The topic of the course will be approached from a broadly Foucauldian angle, considering discourse as social practices that create reality rather than merely reflect it. The emphasis of the three lectures will thus be less upon what is known as ‘conversation analysis’ or ‘content analysis’ and more on text and speech as gateways to understand the making of social phenomena and corresponding power relations.

In the first session, we will discuss the theoretical ideas and origins behind discourse analysis. In the second lecture, we will dive into methodological discussions around doing discourse analysis. In the third session, we will apply the method of discourse analysis with support of a qualitative text analysis software.

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 workshop series aims to provide introductory training on Geographical Information Systems. Material covered includes the construction of geodatabases from a range of data sources, geovisualisation and mapping from geodatasets, raster-based modeling and presentation of maps and charts and other geodata outputs. Each session will start with an introductory lecture followed by practical exercises using GIS software.

FOR FACE-TO-FACE PRACTICAL TEACHING YOU WILL BE REQUIRED TO BRING YOUR OWN FULLY CHARGED LAPTOP WITH THE REQUIRED SOFTWARE LOADED ONTO IT. CHARGING POINTS ARE NOT ALWAYS AVAILABLE IN THE TRAINING ROOMS.

Building upon the univariate techniques introduced in the Foundations in Applied Statistics (FiAS) module, these sessions aim to provide students with a thorough understanding of statistical methods designed to test associations between two variables (bivariate statistics). Students will learn about the assumptions underlying each test, and will receive practical instruction on how to generate and interpret bivariate results using Stata. It introduces students to four of the most commonly used statistical tests in the social sciences: correlation, chi-square tests, t-tests, and analysis of variance (ANOVA).

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 apply these techniques to analyse real data using the statistical package, Stata.

You will learn the following techniques:

• Cross-tabulations
• Scatterplots
• Covariance and correlation
• Nonparametric methods
• Two-sample t-tests
• ANOVA

As well as viewing the pre-recorded mini lectures via Moodle and attending the live lab sessions, students are expected to do a few hours of independent study each week.

With such a large variety of qualitative research methods to choose from, creating a research design can be confusing and difficult without a sufficiently informed overview. This module aims to provide an overview by introducing qualitative data collection and analysis methods commonly used in social science research. The module provides a foundation for other SSRMP qualitative methods modules such as ethnography, discourse analysis, interviews, or diary research. Knowing what is ‘out there’ will help a researcher purposefully select further modules to study on, provide readings to deepen knowledge on specific methods, and will facilitate a more informed research design that contributes to successful empirical research.

NB. This module has video content that needs watching prior to the advertised start date, which can be found on the Moodle page.

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.

Building upon the univariate techniques introduced in the Foundations in Applied Statistics (FiAS) module, these sessions aim to provide students with a thorough understanding of statistical methods designed to test associations between two variables (bivariate statistics). Students will learn about the assumptions underlying each test, and will receive practical instruction on how to generate and interpret bivariate results using Stata. It introduces students to four of the most commonly used statistical tests in the social sciences: correlation, chi-square tests, t-tests, and analysis of variance (ANOVA).

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 apply these techniques to analyse real data using the statistical package, Stata.

You will learn the following techniques:

• Cross-tabulations
• Scatterplots
• Covariance and correlation
• Nonparametric methods
• Two-sample t-tests
• ANOVA

As well as viewing the pre-recorded mini lectures via Moodle and attending the live lab sessions, students are expected to do a few hours of independent study each week.