Social Sciences Research Methods Programme course timetable
Friday 20 October 2023
10:00 |
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. |
13:00 |
Diary Methodology
Finished
This SSRMP module introduces solicited diaries as a qualitative data collection method. Diary methodology is a flexible and versatile tool which has been used across a variety of disciplines (e.g. public health, nursing, psychology, media studies, education, sociology). Solicited diaries are particularly powerful in combination with qualitative interviews, enabling the remote collection of rich data on intimate or unobservable topic areas over a longer period of time. This multi-method approach, also known as the ‘diary-interview method’ (DIM), has been originally developed as an alternative to participant observation (see: Zimmerman, D. H., & Wieder, D. L. (1977). The Diary: Diary-Interview Method. Urban Life, 5(4), 479–498.), which makes it an especially attractive qualitative data collection method in Covid-19 times. In addition to the engagement with pre-recorded videos on Moodle (covering diary methodology basics), you will get hands-on experience with designing your own qualitative diary (4 hours live workshop) and trying out the role of a researcher as well as research participant (teaming up with a module colleague and filling out each other’s diaries). We will reflect on these experiences and answer remaining questions in a final 1-hour live session. The module is suitable for anybody interested in learning more about the method and/or using solicited qualitative diaries in their own research projects. |
15:00 |
Historically, qualitative research has been criticised for being less rigorous than quantitative research through not fulfilling quality standards such as objectivity, validity, and reliability. This leads to questions whether qualitative research can fulfil these specific markers of rigour, how it can come as close as possible to fulfilling them, and whether qualitative research should at all attempt to live up to these understandings of research quality. Responding to this debate, many methodologists have argued for the need of translating objectivity, validity, and reliability within qualitative research designs. The discussion of rigour is a loaded one, among methodologists of all three research approaches (qualitative, quantitative, mixed-methods) as well as mong qualitative researchers themselves. This course introduces different quality strategies for qualitative research to help students make informed decisions for improving their own empirical work and to better judge the rigour of empirical qualitative research done by others. |
Monday 23 October 2023
10:00 |
This module is shared with Psychology. Students from the Department of Psychology MUST book places on this course via the Department; any bookings made by Psychology students via the SSRMP portal will be cancelled. 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 More information on the course can be found here |
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:
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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 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:
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14:00 |
This module is shared with Psychology. Students from the Department of Psychology MUST book places on this course via the Department; any bookings made by Psychology students via the SSRMP portal will be cancelled. 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 More information on the course can be found here |
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:
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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 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:
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Tuesday 24 October 2023
10:00 |
Historical Sociological Methods
Finished
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. |
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. |
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, 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. |
Wednesday 25 October 2023
14:00 |
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. |
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 26 October 2023
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 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:
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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 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:
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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 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:
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15:30 |
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. |
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 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:
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Friday 27 October 2023
13:00 |
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 30 October 2023
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 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:
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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 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:
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The module provides a practical guide to designing and developing a research project based on quantitative dates. It focuses on key aspects of research design, how to work with theory, identify key concepts and operationalise them with quantitative data. It will explore the use of applied statistical methods for data analysis, their applications in academic research, and how to interpret statistical outputs. Although the illustrative examples are mainly drawn from education and policy research, the statistical and design knowledge and skills acquired via this module are also applicable to other social sciences research topics and areas. 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 graphs, 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 a few causal inference methods, such as matching, instrumental variables, difference-in-differences, and regression discontinuity design, which are commonly used in social policy evaluations. |
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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 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:
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The module provides a practical guide to designing and developing a research project based on quantitative dates. It focuses on key aspects of research design, how to work with theory, identify key concepts and operationalise them with quantitative data. It will explore the use of applied statistical methods for data analysis, their applications in academic research, and how to interpret statistical outputs. Although the illustrative examples are mainly drawn from education and policy research, the statistical and design knowledge and skills acquired via this module are also applicable to other social sciences research topics and areas. 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 graphs, 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 a few causal inference methods, such as matching, instrumental variables, difference-in-differences, and regression discontinuity design, which are commonly used in social policy evaluations. |
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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 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:
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