Social Sciences Research Methods Programme course timetable

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.

The module aims to provide students with an introduction to and overview of survey methods and its uses and limitations. It will introduce students both to some of the main theoretical issues involved in survey research (such as survey sampling, non-response and question wording) and to practicalities of the design and analysis of surveys. The module consists of six 1.5 hour sessions, alternating between prerecorded lectures and practical exercises.

This module introduces the statistical techniques of Exploratory and Confirmatory Factor Analyses. Exploratory Factor Analysis (EFA) is used to uncover the latent structure (dimensions) of a set of variables. It reduces the attribute space from a larger number of variables to a smaller number of factors. Confirmatory Factor Analysis (CFA) examines whether collected data correspond to a model of what the data are meant to measure. STATA will be introduced as a powerful tool to conduct confirmatory factor analysis. A brief introduction will be given to confirmatory factor analysis and structural equation modelling.

• Session 1: Exploratory Factor Analysis Introduction
• Session 2: Factor Analysis Applications
• Session 3: CFA and Path Analysis with STATA
• Session 4: Introduction to SEM and programming

This module introduces drawing as a research method, with a particular focus on the key elements and methodological considerations for using drawing as a visual research method, and the pairing of drawing with qualitative interviews. This module explores examples of using drawing as a research method across disciplines, and students are offered hands-on experience to practice using drawing as a research method through a practical workshop.

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 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.

The module will introduce students to the study of language use as a distinctive type of social practice. Attention will be focused primarily on the methodological and analytic principles of conversation analysis. (CA). However, it will explore the debates between CA and Critical Discourse Analysis (CDA), as a means of addressing the relationship between the study of language use and the study of other aspects of social life. It will also consider the roots of conversation analysis in the research initiatives of ethnomethodology, and the analysis of ordinary and institutional talk. It will finally consider the interface between CA and CDA.

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.

This course will provide a detailed critique of the methods and philosophy of the Null Hypothesis Significance Testing (NHST) approach to statistics which is currently dominant in social and biomedical science. We will briefly contrast NHST with alternatives, especially with Bayesian methods. We will use some computer code (Matlab and R) to demonstrate some issues. However, we will focus on the big picture rather on the implementation of specific procedures.

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.

Research proposals, written consent forms, participant information sheets, letters of intent, briefs and proposals on university headed paper are all claims to power, neutrality and control in the research process. Though ethically imperative, this course is an opportunity to reflect upon these “fetishes of consent” (Wynn and Israel, 2018) and the unequal power relations they may produce between participant and researcher. Employing creative methods within the research process, from start to end, is an opportunity to communicate meaningfully with all stakeholders; from a struggling mother with low literacy levels in a Mumbai slum, to a time conscious policy official in Cape Town who refuses to glance past the first paragraph of your research proposal. The ability to communicate complex and often abstract ideas beyond an academic audience is pivotal to doing research with impact, and it is also a vital part of a decolonial agenda. While “the proof of the [decolonial] pudding” is arguably identified in how research is analysed and presented (Hitchings and Latham, 2020:392), it is crucial that methodologies are subject to critical reflexivity, and foster knowledge exchange between scholars, practitioners, and respondents.

In this course we will explore a variety of “creative methods” that have been developed for use in the field, and to generate empirical data. This course then goes further, to explore ways of incorporating creativity throughout the research process in areas such as stakeholder engagement, participant recruitment, consent processes, and gatekeeper conflict during data collection and research dissemination. As part of the course, you will make a simple means for creative outreach such as a video, presentation, drawing, or video recording (etc.) that communicates your research to intended stakeholder(s). We will think critically about intended audience demographics (i.e. elderly, working mothers, young people, peasant farmers, NGO workers or city officials) and reflect upon the creative materials we have produced as a group and discuss its methodological implications. The goal is not to use creative practice as simply another empirical data gathering tool, but to address the hierarchies within academic processes and knowledge production. Creative practice is an opportunity to build new communication strategies that foster the reflexivity, flexibility, and wonder of the unknown within co-production, enabling us to move towards more equitable ways of building and cocreating knowledge.

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.

This module introduces drawing as a research method, with a particular focus on the key elements and methodological considerations for using drawing as a visual research method, and the pairing of drawing with qualitative interviews. This module explores examples of using drawing as a research method across disciplines, and students are offered hands-on experience to practice using drawing as a research method through a practical workshop.

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.

This module introduces drawing as a research method, with a particular focus on the key elements and methodological considerations for using drawing as a visual research method, and the pairing of drawing with qualitative interviews. This module explores examples of using drawing as a research method across disciplines, and students are offered hands-on experience to practice using drawing as a research method through a practical workshop.