Cambridge Research Methods (CaRM) course timetable
Tuesday 5 November
16:00 |
As a science researcher, you will need to deal with quite heterogeneous and dirty data. The data may have been collected through different approaches: observation, surveys, interviews, experiments, published printed or online sources, etc. Moreover, the data may have been encoded by different software and persons, and comes to you in different formats (e.g., txt, csv, xlsx, json, etc). Therefore, the data typically needs to be preprocessed before you can make sense of it through statistics and graphical representations. For example, you may need to re-encode the information in a way that is more meaningful to your analysis goals. Also, you may need to re-arrange the data and clean it, removing duplicates and incomplete information. Finally, you may need to apply all these transformations to other similarly structured data, over and over again. Doing this “by hand” is an arduous, time-consuming and error-prone task; so, automatizing these routines is the smart way to go! In this course, I will teach you how to read, transform and prepare different kinds of data using Python and its popular libraries NumPy and Pandas. We are going to solve several problems (“Missions”) together, of increasing levels of difficulty. Each Mission will introduce you to new data structures (e.g., dictionaries, series, dataframes), methods and attributes, extending your previous knowledge. The content of the course is designed to be to-the-point and focused on practicality. By the end of the course, you should be able to program preprocessing routines that you can apply to your own data. Moreover, you will have an advanced data-handling blueprint to which you can easily add new information and skills over time. |
Wednesday 6 November
09:00 |
This module will provide an introduction to using Interpretative Phenomenological Analysis (IPA) as a research methodology, including thinking about interview preparation to elicit suitable data, the theoretical underpinning of this approach, and conducting analysis of interview data using IPA. Students will gain confidence in incorporating IPA into their research design and will conduct some set activities to reflect on the practicalities of this. |
10:00 |
Ethical Review for Social Science Research (MT)
In progress
Ethics and the associated process of approval / review are an important component of any research project, not only practically enabling research to take place but also enabling researchers to consider the values underpinning their research. The aim of this course is to take both a practical and reflective approach to ethics. On a practical level, the course will focus on identifying the steps involved in seeking ethical approval or undertaking an ethical review. On a reflective level, the course will explore the values informing key ethical principles and concepts and how these may relate to individual’s research. |
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 |
This module will give students an introduction to using photo elicitation interviews in their research design. It will include planning how photos can be used in research and how they can be paired with qualitative interviews. Set independent work, drawing on the content of the recorded lectures, will give the students practical experience using this method and will help them to consider whether this would be a suitable approach to use in their own research design. |
15:00 |
This course introduces students to discourse analysis with a particular focus on the (re)construction of discourse and meaning in textual data. It takes students through the different stages of conducting a discourse analysis in four practical-oriented sessions. The overall course focus is guided by a Foucauldian and Critical Discourse Analysis approach, conceptualising discourses as not only representing but actively producing the social world and examining its entanglement with power. The first session gives an overview of theoretical underpinnings, exploring the epistemological positions that inform different strands of discourse analysis. In the second session, we delve into the practical application of discourse analysis of textual data. Topics covered include, among others, what research questions and aims are suitable for discourse analysis as well as data sampling. In the third session, we discuss how to analyse textual data based on discourse analysis using the computer-assisted qualitative data analysis software Atlas.ti. The fourth session will take a workshop format in which students apply the gained knowledge by developing their own research design based on discourse analysis. |
17:00 |
Causality in Statistics (MT)
[Places]
The module introduces causal inference methods that are commonly used in quantitative research, in particularly social policy evaluations. It covers the contexts and principles as well as applications of several specific methods - instrumental variable approach, regression discontinuity design, and difference-in-differences analysis. Key aspects of the module include investigations of the theoretical basis, statistical process, and illustrative examples drawn from research papers published on leading academic journals. The module incorporates both formal lecturing and lab practice to facilitate understanding and applications of the specific methods covered. The module is suitable for those who are interested in quantitative research and analysis of causality across a range of topics in social sciences. |
Thursday 7 November
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-4)
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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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. |
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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. |
Advanced Topics in Data Preparation Using R (MT)
In progress
The data we obtain from survey and experimental platforms (for behavioural science) can be very messy and not ready for analysis. For social science researchers, survey data are the most common type of data to deal with. But typically the data are not obtained in a format that permits statistical analyses without first conducting considerable time re-formatting, re-arranging, manipulating columns and rows, de-bugging, re-coding, and linking datasets. In this module students will be introduced to common techniques and tools for preparing and cleaning data ready for analysis to proceed. The module consists of four lab exercises where students make use of real life, large-scale, datasets to obtain practical experience of generating codes and debugging. |
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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:
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16:00 |
Foundations in Applied Statistics Using R (FiAS-4)
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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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. |
Friday 8 November
09:00 |
Please note this course predominantly involves theoretical exploration of video methods, not practical sessions. This short course provides an introduction to the use of video as a research method within research projects. The use of video in research is not new. However, with technological and societal shifts, researchers frequently turn to video as a way to explore social phenomena. This course explores the proposed affordances of video methods such as claims of neutrality, durability, closeness to data and richness. These claims are also critiqued. We will consider the role of subjectivity in the use of video, the incompleteness and fragility of video data, the social and physical accessibility of devices and platforms as well as the challenge to inclusion associated with a visual method that prioritises the sense of sight. The course will present specific research case studies, associated with videography, video elicitation, content analysis as well as participatory, creative and non-representational approaches. Video methods are, perhaps, conventionally associated with data collection, which will be a substantial focus of the course alongside approaches to analysing video data. Additionally, the course will also consider how video can be used as part of research storytelling. Alongside discussing existing research, the course will provide opportunities for attendees to plan for the use of video methods. |
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. |
14:00 |
Qualitative Interviews with Vulnerable Groups
In progress
Qualitative research methods are often used in the social sciences to learn more about the world and are often considered to be particularly appropriate for people who might be considered vulnerable. The goal of this course is to encourage students to think critically about the concept of 'vulnerability'; to offer a practical guide to conducting qualitative research that responds to the vulnerabilities of participants and researchers; and to explore ways of challenging and resisting research practices that could be extractive or harmful. It will be highly discursive and will draw throughout on ‘real life’ research examples. The course will be of interest to students who are conducting, or planning to conduct, research with a group considered vulnerable, and will also be of interest to students who want to critically engage with such research in their field. For a more detailed outline of each session please see the 'Learning Outcomes' section below. Content warning: Throughout, the course will cover the experience and effects of different forms of trauma. The first session will touch on the lecturer's research with people affected by criminal exploitation. Content warnings for other sessions will be raised at the end of the preceding session and emailed, where necessary. If you have any concerns you would like to raise with me regarding these matters, please do email the lecturer. |
Monday 11 November
10:00 |
Basic Quantitative Analysis Using Stata (BQA-1)
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 each week. |
Basic Quantitative Analysis Using Stata (BQA-2)
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 each week. |
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14:00 |
Basic Quantitative Analysis Using Stata (BQA-1)
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 each week. |
Historical Sociological Methods
In progress
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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Introduction to Focus Group Research
In progress
This module introduces focus group research as a qualitative research method. Attention is given to the key elements and methodological consideration of conducting focus group research. It also explores the process of conducting focus group research, where students are given the opportunity to design elements of focus group research, and to experience the role of researcher in the practical workshop. There is also an online clinic session where students are given 1-to-1 opportunities to ask questions at the end of the course. |
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16:00 |
Basic Quantitative Analysis Using Stata (BQA-2)
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 each week. |
Tuesday 12 November
10:00 |
Have you received or collected your data (or anticipate doing so!), but are not sure what to do next? This course is designed to equip you with the skills you need to efficiently clean, reformat, and prepare your datasets using Stata. Ideal for social science researchers and analysts who want to use quantitative data for their dissertation or other research project and want to prepare their data efficiently and follow best practices. Over four interactive sessions, you will master essential techniques for handling missing data, merging and appending datasets, batch processing, and recoding variables. Each session combines concise, focused lectures with practical, hands-on exercises using either your own data or datasets provided by the instructor. |
14:00 |
Reading and Understanding Statistics (MT)
In progress
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. |
Oral History
[Places]
Oral history research methods are employed by numerous disciplines, including history, sociology, human geography, and communication. Oral history involves conducting historical research through interviews, where an interviewer speaks with a narrator who has first-hand experience of significant historical events. The oral history method is a data collection technique that can also be used to enrich the mainstream historical record. Session 1: Oral history: Principles and methods Session 2: Making oral history: Practical workshop |
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16:00 |
As a science researcher, you will need to deal with quite heterogeneous and dirty data. The data may have been collected through different approaches: observation, surveys, interviews, experiments, published printed or online sources, etc. Moreover, the data may have been encoded by different software and persons, and comes to you in different formats (e.g., txt, csv, xlsx, json, etc). Therefore, the data typically needs to be preprocessed before you can make sense of it through statistics and graphical representations. For example, you may need to re-encode the information in a way that is more meaningful to your analysis goals. Also, you may need to re-arrange the data and clean it, removing duplicates and incomplete information. Finally, you may need to apply all these transformations to other similarly structured data, over and over again. Doing this “by hand” is an arduous, time-consuming and error-prone task; so, automatizing these routines is the smart way to go! In this course, I will teach you how to read, transform and prepare different kinds of data using Python and its popular libraries NumPy and Pandas. We are going to solve several problems (“Missions”) together, of increasing levels of difficulty. Each Mission will introduce you to new data structures (e.g., dictionaries, series, dataframes), methods and attributes, extending your previous knowledge. The content of the course is designed to be to-the-point and focused on practicality. By the end of the course, you should be able to program preprocessing routines that you can apply to your own data. Moreover, you will have an advanced data-handling blueprint to which you can easily add new information and skills over time. |