Social Sciences Research Methods Programme course timetable

This module introduces the time series techniques relevant to forecasting in social science research and computer implementation of the methods. Background in basic statistical theory and regression methods is assumed. Topics covered include time series regression, Vector Error Correction and Vector Autoregressive Models, Time-varying Volatility, and ARCH models. The study of applied work is emphasized in this non-specialist module. Topics include:

• Introduction to Time Series: Time series and cross-sectional data; Components of a time series, Forecasting methods overview; Measuring forecasting accuracy, Choosing a forecasting technique
• Time Series Regression; Modelling linear and nonlinear trend; Detecting autocorrelation; Modelling seasonal variation by using dummy variables
• Stationarity; Unit Root test; Cointegration
• Vector Error Correlation and Vector Autoregressive models; Impulse responses and variance decompositions
• Time-varying volatility and ARCH models; GARCH models

This module introduces the time series techniques relevant to forecasting in social science research and computer implementation of the methods. Background in basic statistical theory and regression methods is assumed. Topics covered include time series regression, Vector Error Correction and Vector Autoregressive Models, Time-varying Volatility, and ARCH models. The study of applied work is emphasized in this non-specialist module. Topics include:

• Introduction to Time Series: Time series and cross-sectional data; Components of a time series, Forecasting methods overview; Measuring forecasting accuracy, Choosing a forecasting technique
• Time Series Regression; Modelling linear and nonlinear trend; Detecting autocorrelation; Modelling seasonal variation by using dummy variables
• Stationarity; Unit Root test; Cointegration
• Vector Error Correlation and Vector Autoregressive models; Impulse responses and variance decompositions
• Time-varying volatility and ARCH models; GARCH models

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 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.

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.

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 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 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

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 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.

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 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.

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 intensive course on structural equation modelling will provide an introduction to SEM using the statistical software Stata. The aim of the course is to introduce structural equation modelling as an analytical framework and to familiarize participants with the applications of the technique in the social sciences.

The application of the structural equation modelling framework to a variety of social science research questions will be illustrated through examples of published papers. The examples used are drawn from recent papers as well as from publications from the early days of the technique; some use path analysis using cross-national data, others confirmatory factor analysis, and other still full structural models, to test particular hypotheses. Some example papers may be found below, though they should not be treated as the gold standard, rather as an illustration of the variety of approaches and reporting techniques within SEM.

• Duff, A., Boyle, E., Dunleavy, K., & Ferguson, J. (2004). The relationship between personality, approach to learning and academic performance. Personality and individual differences, 36(8), 1907-1920.
  
• Garnier, M., & Hout, M. (1976). Inequality of educational opportunity in France and the United States. Social Science Research, 5(3), 225-246.
  
• Helm, F., Müller-Kalthoff, H., Mukowski, R., & Möller, J. (2018). Teacher judgment accuracy regarding students' self-concepts: Affected by social and dimensional comparisons?. Learning and Instruction, 55, 1-12.
  
• Parker, P. D., Jerrim, J., Schoon, I., & Marsh, H. W. (2016). A multination study of socioeconomic inequality in expectations for progression to higher education: The role of between-school tracking and ability stratification. American Educational Research Journal, 53(1), 6-32.
  

Students will engage in a critique of such examples, with the aim of gaining a better understanding of the SEM framework, as well as its application to real-life data. To further facilitate this application focus, the theoretical introduction will be accompanied by practical examples based on real, publicly-available data.

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.