Cambridge Research Methods (CaRM) course timetable
Monday 14 October
| 14:00 |
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. |
Tuesday 15 October
| 09:00 |
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. |
| 11:00 |
Research Data Security (MT)
[Places]
This course covers basic security for all your research data. In this course, research data means research files, folders, programs, participation sheets, notes, audio recordings, databases, spreadsheets, videos, transcripts, collaborations, datasets, agreements, diagrams, images, etc. that have value to you and your research. It is not just about personal data. Part 1 introduces students to some of the legal issues around academic research involving personal data. Parts 2, 3 and 4 cover basic information and cyber security, a quick impact assessment specifically for researchers and then covers the full risk assessment process by walking you through securing your research by conceptualizing and then assessing possible risks, followed by examining different ways to reduce those risks. This is delivered in a practical and non-technical way although there are some terms to do with risk assessment which may be unfamiliar to you. For this reason there is a glossary available. |
Wednesday 16 October
| 10:00 |
Introduction to Stata (MT)
[Places]
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:
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. |
| 14:00 |
Introduction to Stata (MT)
[Places]
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:
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. |
Thursday 17 October
| 17:30 |
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. |
Friday 18 October
| 09:00 |
Introduction to R (MT)
[Places]
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:
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''' |
| 14:00 |
Introduction to R (MT)
[Places]
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:
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''' |
Monday 21 October
| 09:00 |
Introduction to Python (MT)
[Places]
This module introduces the use of Python, a free programming language originally developed for statistical data analysis. Students will learn:
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. |
| 11:00 |
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. |
| 13:00 |
Introduction to Python (MT)
[Places]
This module introduces the use of Python, a free programming language originally developed for statistical data analysis. Students will learn:
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. |
Tuesday 22 October
| 09:00 |
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. |
| 10:00 |
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. |
| 14:00 |
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. |
Wednesday 23 October
| 10:00 |
Introduction to Stata (MT)
[Places]
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:
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. |
| 12:00 |
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. |
| 14:00 |
Introduction to Stata (MT)
[Places]
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:
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. |
| 16:00 |
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. |
Thursday 24 October
| 10:00 |
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. |
| 17:30 |
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. |
Friday 25 October
| 10:00 |
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. |
Monday 28 October
| 09:00 |
Longitudinal Data Analysis
[Places]
Longitudinal data analysis is a statistical method used to examine data collected from the same subjects or entities over multiple time points. This type of data analysis is particularly valuable for understanding how variables change over time and for investigating trends, patterns, and relationships within a dynamic context. For instance, how does children’s early home environment affect their future mathematical development? Longitudinal data analysis holds several advantages, such as (1) understanding individual-level trajectories, enabling a deeper understanding of how different subjects respond to interventions or external factors over time, (2) supporting stronger causal inference by tracking changes before and after an intervention and (3) accounting for heterogeneity since it recognises that not all subjects respond uniformly to changes over time. Over the course of this module, participants will learn how to work with longitudinal data. Through hands-on exercises and practical examples, participants will gain proficiency in data manipulation, visualisation, and advanced statistical techniques tailored specifically for longitudinal data. From understanding growth trajectories to uncovering causal relationships, this module will empower participants to navigate the complexities of longitudinal data with confidence. It is suitable for postgraduate students and researchers at any stages of their study and research. However, foundational Stata skills are required. |
| 10:00 |
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 in-person, hands-on practical sessions in which you will learn how to analyse real data using the statistical package, Stata. You will learn:
|
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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 in-person, hands-on practical sessions in which you will learn how to analyse real data using the statistical package, Stata. You will learn:
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| 11:00 |
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. |
| 14:00 |
Historical Sociological Methods
[Places]
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. |
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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 in-person, hands-on practical sessions in which you will learn how to analyse real data using the statistical package, Stata. You will learn:
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| 16:00 |
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 in-person, hands-on practical sessions in which you will learn how to analyse real data using the statistical package, Stata. You will learn:
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