Social Sciences Research Methods Programme course timetable

This is an Open Access module, so please read the course description carefully before making a booking, and be advised that spaces may be limited.

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 bases around tools in GIS software packages (mainly ArcGIS).

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.

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.

Students are introduced to meta-analysis, a powerful statistical technique allowing researchers to synthesize 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 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.

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.

Societies can be viewed as path-dependent dynamical systems in which the interactions between multiple heterogeneous actors, and the institutions and organisations they create, lead to complex overlapping patterns of change over different space and time-scales. Agent-based models are exploratory tools for trying to understand some of this complexity. They use computational methods to represent individual people, households, organisations, or other types of agent, and help to make explicit the potential consequences of hypotheses about the way people act, interact and engage with their environment. These types of models have been used in fields as diverse as Architecture, Archaeology, Criminology, Economics, Epidemiology, Geography, and Sociology, covering all kinds of topics including social networks and formation of social norms, spatial distribution of criminal activity, spread of disease, issues in health and welfare, warfare and disasters, behaviour in stock-markets, land-use change, farming,forestry, fisheries, traffic flow, planning and development of cities, flooding and water management. This course introduces a popular freely available software tool, Netlogo, which is accessible to those with no initial programming experience, and shows how to use it to develop a variety of simple models so that students would be able to see how it might apply to their own research.

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.

This is an Open Access module, so please read the course description carefully before making a booking, and be advised that spaces may be limited.

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 bases around tools in GIS software packages (mainly ArcGIS).

Students are introduced to meta-analysis, a powerful statistical technique allowing researchers to synthesize 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.

Societies can be viewed as path-dependent dynamical systems in which the interactions between multiple heterogeneous actors, and the institutions and organisations they create, lead to complex overlapping patterns of change over different space and time-scales. Agent-based models are exploratory tools for trying to understand some of this complexity. They use computational methods to represent individual people, households, organisations, or other types of agent, and help to make explicit the potential consequences of hypotheses about the way people act, interact and engage with their environment. These types of models have been used in fields as diverse as Architecture, Archaeology, Criminology, Economics, Epidemiology, Geography, and Sociology, covering all kinds of topics including social networks and formation of social norms, spatial distribution of criminal activity, spread of disease, issues in health and welfare, warfare and disasters, behaviour in stock-markets, land-use change, farming,forestry, fisheries, traffic flow, planning and development of cities, flooding and water management. This course introduces a popular freely available software tool, Netlogo, which is accessible to those with no initial programming experience, and shows how to use it to develop a variety of simple models so that students would be able to see how it might apply to their own research.

Students are introduced to multilevel modelling techniques (a.k.a. hierarchical linear modelling). MLM allows one to analyse how contexts influence outcomes ie do schools/neighbourhoods influence behaviour.

Stata will be used during this module. No prior knowledge of Stata will be assumed.

Students are introduced to multilevel modelling techniques (a.k.a. hierarchical linear modelling). MLM allows one to analyse how contexts influence outcomes ie do schools/neighbourhoods influence behaviour.

Stata will be used during this module. No prior knowledge of Stata will be assumed.

This is an Open Access module, so please read the course description carefully before making a booking, and be advised that spaces may be limited.

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 bases around tools in GIS software packages (mainly ArcGIS).

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, moving average, exponential smoothing and decomposition. The study of applied work is emphasized in this non-specialist module.

The challenge of causal inference is ubiquitous in social science. Nearly every research project fundamentally is about causes and effects. This course will introduce graduate students to core issues about causal inference in quantitative social research, focusing especially on how one can move from demonstrating correlation to causation. The first lecture will define key concepts of correlates, risk factors, causes, mediators and moderators. The second lecture will discuss quasi-experimental research designs (studies without random assignment), and issues of “validity” in drawing causal conclusions. The third and fourth sessions will be lectures and practicals introducing two key analytic methods (propensity score matching and fixed effects regression models) that can be used to help identify causes. The course will focus on studies in which individual people are the basic unit of analyses, particularly longitudinal studies which follow the same people over multiple waves of assessment.

The challenge of causal inference is ubiquitous in social science. Nearly every research project fundamentally is about causes and effects. This course will introduce graduate students to core issues about causal inference in quantitative social research, focusing especially on how one can move from demonstrating correlation to causation. The first lecture will define key concepts of correlates, risk factors, causes, mediators and moderators. The second lecture will discuss quasi-experimental research designs (studies without random assignment), and issues of “validity” in drawing causal conclusions. The third and fourth sessions will be lectures and practicals introducing two key analytic methods (propensity score matching and fixed effects regression models) that can be used to help identify causes. The course will focus on studies in which individual people are the basic unit of analyses, particularly longitudinal studies which follow the same people over multiple waves of assessment.

This course will show, in a very practical way, 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 lead 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.

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, moving average, exponential smoothing and decomposition. The study of applied work is emphasized in this non-specialist module.

Ethics is becoming an increasingly important issue for all researchers and the aim of these three sessions is to demonstrate the practical value of thinking seriously and systematically about what constitutes ethical conduct in social science research. The sessions will involve some small-group work.

This event details how the SSRMC works, more about the modules we offer, and everything you need to know about making a booking.

NB. ALL STUDENTS WISHING TO TAKE SSRMC COURSES THIS YEAR ARE EXPECTED TO ATTEND THIS INDUCTION SESSION

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 SSRMC 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: https://www.mathworks.com/products/matlab.html

More information on the course can be found, here: http://www.psychol.cam.ac.uk/grads/grads/pg-prog/programming#section-0

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 SSRMC 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: https://www.mathworks.com/products/matlab.html

More information on the course can be found, here: http://www.psychol.cam.ac.uk/grads/grads/pg-prog/programming#section-0