Cambridge Research Methods (CaRM) course timetable

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 Easter Term, the course is entirely virtual, comprising the online resources, supported by 3 x zoom Q&A sessions.

The purpose of this course is to familiarise students with the basic concepts of Bayesian theory. It is designed to provide an introduction to the principles, methods, and applications of Bayesian statistics. Bayesian statistics offers a powerful framework for data analysis and inference, allowing for the incorporation of prior knowledge and uncertainty in a coherent and systematic manner.

Throughout this course, we will cover key concepts such as Bayes' theorem, prior and posterior distributions, likelihood functions, and the fundamental differences between Bayesian and frequentist approaches. You will learn to formulate and estimate statistical models, update beliefs using new data, and make informed decisions based on the posterior probabilities generated through Bayesian inference. By the end of this course, you will possess the necessary skills to perform Bayesian data analysis, interpret results, and apply Bayesian methods in various contexts.

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 Easter Term, the course is entirely virtual, comprising the online resources, supported by 3 x zoom Q&A sessions.

The purpose of this course is to familiarise students with the basic concepts of Bayesian theory. It is designed to provide an introduction to the principles, methods, and applications of Bayesian statistics. Bayesian statistics offers a powerful framework for data analysis and inference, allowing for the incorporation of prior knowledge and uncertainty in a coherent and systematic manner.

Throughout this course, we will cover key concepts such as Bayes' theorem, prior and posterior distributions, likelihood functions, and the fundamental differences between Bayesian and frequentist approaches. You will learn to formulate and estimate statistical models, update beliefs using new data, and make informed decisions based on the posterior probabilities generated through Bayesian inference. By the end of this course, you will possess the necessary skills to perform Bayesian data analysis, interpret results, and apply Bayesian methods in various contexts.

The purpose of this course is to familiarise students with the basic concepts of Bayesian theory. It is designed to provide an introduction to the principles, methods, and applications of Bayesian statistics. Bayesian statistics offers a powerful framework for data analysis and inference, allowing for the incorporation of prior knowledge and uncertainty in a coherent and systematic manner.

Throughout this course, we will cover key concepts such as Bayes' theorem, prior and posterior distributions, likelihood functions, and the fundamental differences between Bayesian and frequentist approaches. You will learn to formulate and estimate statistical models, update beliefs using new data, and make informed decisions based on the posterior probabilities generated through Bayesian inference. By the end of this course, you will possess the necessary skills to perform Bayesian data analysis, interpret results, and apply Bayesian methods in various contexts.

The purpose of this course is to familiarise students with the basic concepts of Bayesian theory. It is designed to provide an introduction to the principles, methods, and applications of Bayesian statistics. Bayesian statistics offers a powerful framework for data analysis and inference, allowing for the incorporation of prior knowledge and uncertainty in a coherent and systematic manner.

Throughout this course, we will cover key concepts such as Bayes' theorem, prior and posterior distributions, likelihood functions, and the fundamental differences between Bayesian and frequentist approaches. You will learn to formulate and estimate statistical models, update beliefs using new data, and make informed decisions based on the posterior probabilities generated through Bayesian inference. By the end of this course, you will possess the necessary skills to perform Bayesian data analysis, interpret results, and apply Bayesian methods in various contexts.

This module is for anyone considering studying on a CaRM 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 Cambridge Research Methods and encourage students to consider what modules might be appropriate for their research and career development.

Please note: This module has pre-recorded lectures which students need watching before the live workshop session.

This module will provide an overview of different qualitative methods which students may wish to use in their social science research. It will explore the advantages and potential drawbacks of different qualitative methods of data collections and analysis. Reflective activities (to be completed independently and as part of the in-person workshops) will encourage the students to consider the best methods for their own research design. It is intended that this module will provide a broad foundation for students to continue on to other CaRM modules on Qualitative Methods.

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 CaRM. 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.

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 CaRM. 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, Cambridge Research Methods and Cambridge Digital Humanities, as well as with the Citizen Evidence Lab at Amnesty International.

Please note that places on this module are limited, so please only make a booking if you are able to attend all of the sessions.

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'''

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'''

This module introduces the use of Python, a free programming language originally developed for statistical data analysis. Students will learn:

• Ways of reading data into Python
• How to manipulate data in major data types
• How to draw basic graphs and figures with Python
• 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.

This module will provide an overview of different qualitative methods which students may wish to use in their social science research. It will explore the advantages and potential drawbacks of different qualitative methods of data collections and analysis. Reflective activities (to be completed independently and as part of the in-person workshops) will encourage the students to consider the best methods for their own research design. It is intended that this module will provide a broad foundation for students to continue on to other CaRM modules on Qualitative Methods.

This module introduces the use of Python, a free programming language originally developed for statistical data analysis. Students will learn:

• Ways of reading data into Python
• How to manipulate data in major data types
• How to draw basic graphs and figures with Python
• 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.

This module will provide an overview of different qualitative methods which students may wish to use in their social science research. It will explore the advantages and potential drawbacks of different qualitative methods of data collections and analysis. Reflective activities (to be completed independently and as part of the in-person workshops) will encourage the students to consider the best methods for their own research design. It is intended that this module will provide a broad foundation for students to continue on to other CaRM modules on Qualitative Methods.

As a researcher, you will collect a lot of data throughout your research. Whether that data takes the form of spreadsheets, audio-visual recordings, images, interview transcripts, or something entirely different, it's crucial that you manage it well throughout your project. Funders also require applicants to demonstrate they have a clear idea of how research data is going to be managed throughout a project. This course will introduce both students and researchers to some of the basics of managing research data and equip them with strategies for effective data management throughout the research life cycle. It will complement other CaRM modules you are undertaking such as Research Data Security, Ethical Review for Social Science research, and Qualitative Methods. Data management may seem daunting but once you have a grasp of the concepts introduced in this course, it will allow you to work more efficiently and help you to identify and address any issues before you start a project. This course is designed to provide you with some data management strategies that you can immediately implement into your research process.

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.

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 CaRM. 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.

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 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 CaRM. 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.

This course will introduce students to the general philosophical debates concerning scientific methodology, assessing their ramifications for the conduct of qualitative social research. It will enable students to critically evaluate major programmes in the philosophy of sciences, considering whether there are important analytic differences between the social and natural sciences; and whether qualitative methods themselves comprise a unified approach to the study of social reality.

Geographic Information Systems (GIS) play a crucial role in understanding spatial data and making informed decisions across various fields such as urban planning, environmental management, epidemiology, and business analysis. This 4-week module provides a comprehensive introduction to GIS, covering fundamental concepts, tools, and practical applications.

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, Cambridge Research Methods and Cambridge Digital Humanities, as well as with the Citizen Evidence Lab at Amnesty International.

Please note that places on this module are limited, so please only make a booking if you are able to attend all of the sessions.