All Social Sciences Research Methods Programme courses

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.

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.

This course provides an introduction to the management and analysis of qualitative data using Atlas.ti. It is divided between pre-recorded lectures, in which you’ll learn the relevant strategies and techniques, and hands-on live practical sessions in Zoom, in which you will learn how to analyse qualitative data using the software.

The sessions will introduce participants to the following:

• consideration of the advantages and limitations of using qualitative analysis software
• setting-up a research project in Atlas.ti
• use of Atlas.ti's menus and tool bars
• importing and organising data
• starting data analysis using Atlas.ti’s coding tools
• exploring data using query and visualization tools

Please note: Atlas.ti for Mac will not be covered.

This module follows on from Foundations in Applied statistics, and will teach you the basics of common bivariate techniques (that is, techniques that examine the associations between two variables). The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to apply these techniques to the analysis of real data.

Techniques to be covered include:

• Cross-tabulations
• Scatterplots
• Covariance and correlation
• Nonparametric methods
• Two-sample t-tests
• ANOVA
• Ordinary Least Squares (OLS)

For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class.

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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.

The course offers an introduction to critical approaches to discourse analysis with a focus on linking theory with method. Students will be equipped with the conceptual and practical knowledge to analyse a broad range of issues based on text documents. The topic of the course will be approached from a broadly Foucauldian angle, considering discourse as social practices that create reality rather than merely reflect it. The emphasis of the three lectures will thus be less upon what is known as ‘conversation analysis’ or ‘content analysis’ and more on text and speech as gateways to understand the making of social phenomena and corresponding power relations.

In the first session, we will discuss the theoretical ideas and origins behind discourse analysis. In the second lecture, we will dive into methodological discussions around doing discourse analysis. In the third session, we will apply the method of discourse analysis with support of a qualitative text analysis software.

The module explores Good Data Visualisation (GDV) and graph creation using Python.

In this module we demystify the principles of data visualisation, using Python software, to help researchers to better understand and reflect how the “5 Principles” of GDV can be achieved. We also examine how we can develop Python’s application in data visualisation beyond analysis. Students will have the opportunity to apply GDV knowledge and skills to data using Python in an online Zoom, self-paced, practical workshop. In addition there will be post-class exercises and a 1-hour asynchronous Q&A forum on Moodle Forum.

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.

This is the first in a series of three workshops, which extend last term's teaching on 'Decoloniality in Research Methods'. In each session, participants will be presented with a range of theoretical concepts as well as case studies from a variety of scholars who mobilise these concepts to shape their methodologies. At least half of each session will be dedicated to practical application – participants will be encouraged to engage in a range of individual and group reflections, discussions and exercises.

Participants will be encouraged to reflect on how decolonial thought affects each stage of their research project. Beginning with initial research design and literature reviews, and ending with dissemination and research impact, each session focuses on a different stage in the research cycle, bringing a range of decolonial thought and scholar-activism into conversation with our research methods.

Please note: Participants can choose whether to attend a single session or multiple sessions, as each will be a 'stand alone' workshop. However, each workshop must be booked sepaarately.

Workshop 1: Research design and the impact of (de)coloniality on our research projects

In this session we’ll place our disciplines in the historic context of their emergence and ask what implications this historicization has on our research in the present. We’ll then discuss a number of scholars who propose decoloniality and/or decolonisation as theoretical frames through which we can approach our research. In terms of practical skills, we’ll look to the emerging field of citational justice, asking how who and what we cite impacts the work we produce. We’ll also examine our research questions and explore their potential contributions to the reproduction of or resistance to deeper structures of power.

This is the second in a series of three workshops, which extend last term's teaching on 'Decoloniality in Research Methods'. In each session, participants will be presented with a range of theoretical concepts as well as case studies from a variety of scholars who mobilise these concepts to shape their methodologies. At least half of each session will be dedicated to practical application – participants will be encouraged to engage in a range of individual and group reflections, discussions and exercises.

Participants will be encouraged to reflect on how decolonial thought affects each stage of their research project. Beginning with initial research design and literature reviews, and ending with dissemination and research impact, each session focuses on a different stage in the research cycle, bringing a range of decolonial thought and scholar-activism into conversation with our research methods. Please note: Participants can choose whether to attend a single session or multiple sessions, as each will be a 'stand alone' workshop. However, each workshop must be booked separately.

Session 2: The role of ‘the researcher’ & the importance of reflexivity

In this session, we’ll discuss the notion of ‘reflexivity’, considering our disciplines, our roles as researchers within the University, and our experiences as individual researchers with our own life experiences and histories. We’ll then explore seven commonly used research methods (the development of ‘social theory’, quantitative analysis, ethnography, autoethnography, qualitative interviews, digital methods and archival research). We’ll ask what happens to these methods when we place them into a wider frame of decolonial analysis and look to other scholars who are using these methods to advance the goals of decolonization.

In terms of practical skills, participants will be encouraged to bring their own reflexive writing to the session, and we’ll explore how different theories relating to standpoint, positionality and intersectionality help us make sense of the approaches we are taking. Participants will be encouraged to bring an outline of their research methods and will work in thematic groups to place their methods in conversation with decolonial thought.

This is the third and last in a series of three workshops, which extend last term's teaching on 'Decoloniality in Research Methods'. In each session, participants will be presented with a range of theoretical concepts as well as case studies from a variety of scholars who mobilise these concepts to shape their methodologies. At least half of each session will be dedicated to practical application – participants will be encouraged to engage in a range of individual and group reflections, discussions and exercises.

Participants will be encouraged to reflect on how decolonial thought affects each stage of their research project. Beginning with initial research design and literature reviews, and ending with dissemination and research impact, each session focuses on a different stage in the research cycle, bringing a range of decolonial thought and scholar-activism into conversation with our research methods.

Please note: Participants can choose whether to attend a single session or multiple sessions, as each will be a 'stand alone' workshop. However, each workshop must be booked separately.

Session 3: From data collection to analysis to dissemination

In this session, we’ll begin with Linda Tuhiwai Smith’s (2012:226) claim that researchers ‘must get the story right as well as tell the story well’. We’ll think about what it means to analyse our data and create a product (a dissertation, research paper) which exists within the wider context of the academy. We’ll examine six different ways in which different researchers have oriented themselves towards their research, and their research towards the future (including an ‘ethics of care’, ‘rage anger and complaint’, ‘love, empathy, solidarity and desire’ and ‘action, speculation and movement’).

In terms of practical skills, we’ll think about our research outputs, the potential impacts of their design and dissemination and how these considerations might impact the earlier stages of our research projects, such as in the way we collect and store our data. Participants will also be encouraged to think about their own research orientation and place their project into a wider speculative context.

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 (3 hours live workshop via Zoom) and trying out the role of a researcher as well as research participant over a 5-day period (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 via Zoom.

The module is suitable for anybody interested in learning more about the method and/or using solicited qualitative diaries in their own research projects.

Virtual Data Collection in the Time of COVID-19: Practical and Ethical Considerations

Doing data collection in the time of COVID-19 has required the adaptation of existing approaches. While face-to-face data collection is not feasible during the COVID-19 crisis, phone- and internet-based interviews offer an alternative means of collecting primary data. In this workshop, we discus key practical and ethical issues concerning virtual approaches to data collection. We provide practical examples drawing on two related research projects that took place in a lower-middle income context during the Covid-19 school closures.

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.

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 course is entirely virtual, comprising the online resources, supported by 3 x zoom Q&A sessions.

This module is an introduction to ethnographic fieldwork and analysis and is intended for students in fields other than anthropology. It provides an introduction to contemporary debates in ethnography, and an outline of how selected methods may be used in ethnographic study.

The ethnographic method was originally developed in the field of social anthropology, but has grown in popularity across several disciplines, including sociology, geography, criminology, education and organization studies.

Ethnographic research is a largely qualitative method, based upon participant observation among small samples of people for extended periods. A community of research participants might be defined on the basis of ethnicity, geography, language, social class, or on the basis of membership of a group or organization. An ethnographer aims to engage closely with the culture and experiences of their research participants, to produce a holistic analysis of their fieldsite.

Session 1: The Ethnographic Method What is ethnography? Can ethnographic research and writing be objective? How does one conduct ethnographic research responsibly and ethically?

Session 2: Recording the field: Notes, Images, Sounds

Session 3: Intersubjectivity, Vulnerability and Collaboration

Session 4: Found Objects: Building and Reading an Archive

This course aims to provide students with a range of specific technical skills that will enable them to undertake impact evaluation of policy. Too often policy is implemented but not fully evaluated. Without evaluation we cannot then tell what the short or longer term impact of a particular policy has been. On this course, students will learn the skills needed to evaluate particular policies and will have the opportunity to do some hands on data manipulation. A particular feature of this course is that it provides these skills in a real world context of policy evaluation. It also focuses primarily not on experimental evaluation (Random Control Trials) but rather quasi-experimental methodologies that can be used where an experiment is not desirable or feasible.

Topics:

• Regression-based techniques
• Evaluation framework and concepts
• The limitations of regression based approaches and RCTs
• Before/After, Difference in Difference (DID) methods
• Computer exercise on difference in difference methods
• Instrumental variables techniques
• Regression discontinuity design.

This course offers an introduction to event history analysis, which is a tool used for analyzing the occurrence and timing of events. Typical examples are life course transitions such as the transition to parenthood and partnership formation processes, labour market processes such as job promotions, mortality, and transitions to and from sickness and disability. The researcher may be interested in examining how the rate of a particular event varies over time or with individual characteristics, social conditions, or other factors. Event History Analysis lets the researcher handle censoring and truncation, include time-varying independent variables, account for unobserved heterogeneity (frailty), and so on. The course will rely on Stata as the main computing tool, but users of other statistical software could still benefit from the course. The course is taught through both lectures and lab sessions.

This course will introduce students to the approach called "Exploratory Data Analysis" (EDA) where the aim is to extract useful information from data, with an enquiring, open and sceptical mind. It is, in many ways, an antidote to many advanced modelling approaches, where researchers lose touch with the richness of their data. Seeing interesting patterns in the data is the goal of EDA, rather than testing for statistical significance. The course will also consider the recent critiques of conventional "significance testing" approaches that have led some journals to ban significance tests.

Students who take this course will hopefully get more out of their data, achieve a more balanced overview of data analysis in the social sciences.

• To understand that the emphasis on statistical significance testing has obscured the goals of analysing data for many social scientists.
• To discuss other ways in which the significance testing paradigm has perverted scientific research, such as through the replication crisis and fraud.
• To understand the role of graphics in EDA

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 series of workshops are aimed at students interested in interdisciplinary and feminist research practice. The course revolves around a simple query: what makes research feminist? It is the starting point to engage with classic and more contemporary writings on feminist knowledge production to answer some of the following questions: what are the ‘proper’ objects of feminist research? Who can do feminist research? Why do we do feminist research, and what is its relevance? Who do we cite in our research? We will have in-class discussions and hands-on assignments that will allow students to practice some of the main debates we will read about.

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:

• The key features of quantitative analysis, and how it differs from other types of empirical analysis
• The basics of formal hypothesis testing
• Basic concepts: what is a variable? what is the distribution of a variable? and how can we best represent a distribution graphically?
• Features of statistical distributions: measures of central tendency and dispersion
• The normal distribution
• Why statistical testing works
• Statistical methods used to test simple hypotheses
• How to use Stata

This module is an extension of the three previous modules in the Basic Statistics stream, and introduces more complex and nuanced aspects of the theory and practice of mutivariate analysis. Students will learn the theory behind the methods covered, how to implement them in practice, how to interpret their results, and how to write intelligently about their findings. Half of the module is based in the lecture theatre; the other half is lab-based, in which students will work through practical exercises using the statistical software Stata.

Topics covered include:

• Interaction effects in regression models: how to estimate these and how to interpret them
• Marginal effects from interacted models
• Ordered and categorical discrete dependent variable models (ordered and multinomial logit and probit)

To get the most out of the course, you should also expect to spend some time between sessions building your own statistical models.

This workshop series aims to provide introductory training on Geographical Information Systems. Material covered includes the construction of geodatabases from a range of data sources, geovisualisation and mapping from geodatasets, raster-based modeling and presentation of maps and charts and other geodata outputs. Each session will start with an introductory lecture followed by practical exercises using GIS software.

FOR FACE-TO-FACE PRACTICAL TEACHING YOU WILL BE REQUIRED TO BRING YOUR OWN FULLY CHARGED LAPTOP WITH THE REQUIRED SOFTWARE LOADED ONTO IT. CHARGING POINTS ARE NOT ALWAYS AVAILABLE IN THE TRAINING ROOMS.