All Social Sciences Research Methods Programme courses

Content analysis has been widely used to study different sources of data, such as interviews, conversations, speeches, and other texts. This module adopts an interactive approach, where students are introduced to the key elements of content analysis, how to conduct content analysis, and a range of examples of the use of content analysis. This module offers two practical workshops, where students have a hands-on opportunity to practice performing content analysis, followed by guided reflection.

This module is for anyone considering studying on an SSRMP 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 SSRMP 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.

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 focus group questions, and to experience the role of researcher in the practical workshops.

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 focus group questions, and to experience the role of researcher in the practical workshops.

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 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'''

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 the SSRMP. 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 module offers an introduction to the use of action research in social sciences research. It includes an exploration of paradigmatic, methodological, practical, and ethical considerations.

This module offers an introduction to the use of case studies in social sciences research. It includes an exploration of paradigmatic, methodological, practical, and ethical considerations.

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.

In this module students will be introduced to meta-analysis, a powerful statistical technique allowing researchers to synthesize the available evidence for a given research question using standardized (comparable) effect sizes across studies. The sessions teach students how to compute treatment effects, how to compute effect sizes based on correlational studies, how to address questions such as what is the association of bullying victimization with depression? The module will be useful for students who seek to draw statistical conclusions in a standardized manner from literature reviews they are conducting.

Mixed and multi method approaches are increasingly common in the social sciences. Whilst much has been written about the justification, design and benefit of mixed methods, there is correspondingly little published empirical research which rigorously employs such approaches. In this interactive session, we will consider what mixed and multi methods approaches are, when you might use them, and - most importantly - start to think about how you can integrate quantitative and qualitative data (a) across a series of studies and (b) within a single study.

The neurodiversity module is designed for researchers and academics who wish to expand their knowledge of neurodiversity-friendly practices in research. The module centres around 5 key themes and covers the following:

• What is neurodiversity?

• How does neurodiversity impact research?

• What are specific learning difficulties (SpLD)?

• How do they impact your participants, and the positionality of the researcher?

• Delivering useful approaches and resources

Highlighting the difference between 'integration' and 'inclusion', the content will equip researchers to design the most effective research methods to increase inclusion and lessen the need for 'bolton' practices. The course will also discuss the difference between research design and delivery at the individual level versus the strategic level to be develop universal methods. The course will be practically useful for those wishing to learn about equipment, tools, and techniques additionally available to support researchers and participants alike, and how these can be funded through the University and/or other funding providers.

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.

Panel data consists of repeated observations measured at multiple time points, collected from multiple individuals, entities, or subjects over a period of time. For instance, child A’s numeracy test score in Year 1, Year 2, Year 3 and Year 4. Country B’s GDP per capita in year 2020, 2021, 2022 and 2023. Panel data analysis, as a subset of longitudinal data analysis, is particularly useful for addressing research questions that try to understand how variables change over time and how individual units differ in their responses to changes. An example research question could be: how do children's numeracy scores vary across different socioeconomic backgrounds, and how have these disparities changed over the years? Panel data analysis holds several advantages, such as (1) increased statistical efficiency, (2) more effective at controlling for unobserved individual or entity-specific effects, and (3) more capable to study the dynamics of relationships over time.

Over the course of this module, participants will learn how to work with panel data. Through hands-on exercises and practical examples, participants will gain proficiency in data manipulation, visualisation, and advanced statistical techniques tailored specifically for panel data. It is suitable for postgraduate students and researchers at any stages of their study and research. However, foundational Stata skills are required.

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.

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

Propensity score matching (PSM) is a technique that simulates an experimental study in an observational data set in order to estimate a causal effect. In an experimental study, subjects are randomly allocated to “treatment” and “control” groups; if the randomisation is done correctly, there should be no differences in the background characteristics of the treated and non-treated groups, so any differences in the outcome between the two groups may be attributed to a causal effect of the treatment. An observational survey, by contrast, will contain some people who have been subject to the “treatment” and some people who have not, but they will not have not been randomly allocated to those groups. The characteristics of people in the treatment and control groups may differ, so differences in the outcome cannot be attributed to the treatment. PSM attempts to mimic the experimental situation trial by creating two groups from the sample, whose background characteristics are virtually identical. People in the treatment group are “matched” with similar people in the control group. The difference between the treatment and control groups in this case should may therefore more plausibly be attributed to the treatment itself. PSM is widely applied in many disciplines, including sociology, criminology, economics, politics, and epidemiology. The module covers the basic theory of PSM, the steps in the implementation (e.g. variable choice for matching and types of matching algorithms), and assessment of matching quality. We will also work through practical exercises using Stata, in which students will learn how to apply the technique to the analysis of real data and how to interpret the results.

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 delivered as a mix of lectures and seminars. 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.

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.

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.

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:

• Lecture 1: Introduction to quantitative research design
• Lecture 2: Key statistical concepts and methods
• Lecture 3: Applied social statistics in education research
• Lecture 4: Education and social policy evaluation

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.

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.

This course introduces students to some of the legal issues around academic research involving personal data, and walks them through securing their 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 them. For this reason there is a relevant glossary provided for each session.

Using secondary data (that is, data collected by someone else, usually a government agency or large research organisation) has a number of advantages in social science research: sample sizes are usually larger than can be achieved by primary data collection, samples are more nearly representative of the populations they are drawn from, and using secondary data for a research project often represents significant savings in time and money. This short course, taught by Dr Deborah Wiltshire of the UK Data Archive, will discuss the advantages and limitations of using secondary data for research in the social sciences, and will introduce students to the wide range of available secondary data sources. Students will learn how to search online for suitable secondary data by browsing the database of the UK Data Archive.