Cambridge Research Methods (CaRM) course timetable
Wednesday 4 December 2024
10:00 |
Introduction to Mixed Methods (MT)
In progress
Mixed method and multi method approaches are increasingly popular in the social and behavioral sciences. Much has been written on the benefits of mixed methods approaches, integrating the strengths of both qualitative and quantitative research methods to better address multifaceted and complex phenomena. This introductory course is a starting point for those who are interested in learning more about mixed methods approaches. During the course, we will cover common mixed methods research designs, and discuss the benefits and challenges associated with these approaches. We will critically discuss examples of mixed method research, and practice the process of integrating qualitative and quantitative analysis in mixed method projects. This course is aimed at students who are contemplating using mixed methods in their own research, and will feature participatory opportunities for students to share and discuss their own research proposals. |
Thursday 5 December 2024
10:00 |
Doing Multivariate Analysis Using R (DMA-2)
Not bookable
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. |
14:00 |
Decoloniality in Research Methods
In progress
This short course will be an opportunity for us to engage with a variety of decolonial theories and methodologies and to consider the implications of these approaches on a variety of elements of our research processes. Each session will consist of a presentation which engages with selected decolonial theory and methods, examples of ‘methods in practice’ drawn from across the social sciences and time for self-reflexive individual and group discussion. The course will not prescriptively define and provide instructions for ‘decolonial methods’, but instead be a space to consider a variety of ways in which scholars, activists and those working outside the traditional boundaries of ‘the academy’ have thought decolonially about social science research methodologies. The course’s workshop format will enable opportunities for us to apply some of these insights to our own scholarship. |
Doing Multivariate Analysis Using R (DMA-2)
Not bookable
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. |
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17:30 |
Open Source Investigation for Academics (MT)
In progress
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. |
Thursday 23 January 2025
10:00 |
Introduction to Stata (LT)
[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 (LT)
[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 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 CaRM 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 need to be watched before the live workshop session. |
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 extremely limited, so please only make a booking if you are able to attend all of the sessions. |
Monday 27 January 2025
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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Foundations in Applied Statistics Using R (FiAS-6)
Not bookable
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, R. You will learn:
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14:00 |
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:
|
16:00 |
Foundations in Applied Statistics Using R (FiAS-6)
Not bookable
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, R. You will learn:
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Tuesday 28 January 2025
10:30 |
Doing Qualitative Interviews (LT)
[Places]
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 Lent 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. |
Wednesday 29 January 2025
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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The course focuses on practical hands-on variable handling and programming implementation using rather than on theory. This course is intended for those who have never programmed before, including those who only call/run Matlab scripts but are not familiar with how code works and how matrices are handled in Matlab. (Note that calling a couple of scripts is not 'real' programming.) MATLAB (C) is a powerful scientific programming environment optimal for data analysis and engineering solutions. More information on the programme and its uses can be found here |
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Foundations in Applied Statistics Using R (FiAS-6)
Not bookable
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, R. You will learn:
|
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14:00 |
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:
|
The course focuses on practical hands-on variable handling and programming implementation using rather than on theory. This course is intended for those who have never programmed before, including those who only call/run Matlab scripts but are not familiar with how code works and how matrices are handled in Matlab. (Note that calling a couple of scripts is not 'real' programming.) MATLAB (C) is a powerful scientific programming environment optimal for data analysis and engineering solutions. More information on the programme and its uses can be found here |
|
16:00 |
Foundations in Applied Statistics Using R (FiAS-6)
Not bookable
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, R. You will learn:
|
Thursday 30 January 2025
10:00 |
Introduction to Stata (LT)
[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. |
This course provides an introduction to the management and analysis of qualitative data using Atlas.ti. It is divided between mini-lectures, in which you’ll learn the relevant strategies and techniques, and hands-on live practical sessions, in which you will learn how to analyse qualitative data using the software. The sessions will introduce participants to the following:
Please note: Atlas.ti for Mac will not be covered. |
|
14:00 |
Introduction to Stata (LT)
[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. |
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 extremely limited, so please only make a booking if you are able to attend all of the sessions. |
Monday 3 February 2025
10:00 |
Basic Quantitative Analysis Using R (BQA-6)
Not bookable
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 R. 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 in-person, hands-on practical sessions, in which you will learn how to apply these techniques to analyse real data using the statistical package, R. You will learn the following techniques:
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. |
This module aims to provide a practical guide to developing research projects using quantitative methods. It will focus on quantitative research design, key statistical concepts and methods, applied social statistics in education research and social policy evaluation. While the illustrative examples will mainly come from education and policy research, the knowledge and skills acquired through this module may also apply to other quantitative social sciences research projects. Outline The module consists of four lectures (two-hours per session) including:
Contents Lecture 1 will focus on how to design quantitative studies, including formulating research questions, engaging with theoretical and empirical evidence, developing hypothesises, as well as preparing relevant data. Lecture 2 will cover some of the widely used statistical toolkits for data description and hypothesis testing, such as z-score, conference intervals, parametric and non-parametric tests, correlation and regression analyses. Lecture 3 applies the principles of research design and key statistical methods to examples drawn from education research. It will highlight regression analyses and the interpretation of statistical outputs. Lecture 4 will introduce causal inference methods, such as instrumental variables, difference-in-differences and regression discontinuity design, which are commonly used in social policy evaluation. |
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Basic Quantitative Analysis Using Stata (BQA-5)
Not bookable
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 in-person, hands-on practical sessions, 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:
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. |
|
14:00 |
Public Policy Analysis
[Places]
The analysis of policy depends on many disciplines and techniques and so is difficult for many researchers to access. This module provides a mixed perspective on policy analysis, taking both an academic and a practitioner perspective. This is because the same tools and techniques can be used in academic research on policy options and change as those used in practice in a policy environment. This course is provided as three 2-hour sessions. No direct analysis work will be done in the sessions themselves, but some sample data and questions will be provided for students who wish to take the material into practice. |
Basic Quantitative Analysis Using Stata (BQA-5)
Not bookable
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 in-person, hands-on practical sessions, 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:
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. |
|
16:00 |
Basic Quantitative Analysis Using R (BQA-6)
Not bookable
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 R. 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 in-person, hands-on practical sessions, in which you will learn how to apply these techniques to analyse real data using the statistical package, R. You will learn the following techniques:
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. |