Social Sciences Research Methods Programme course timetable

This module is an extension of the three previous modules in the Basic Statistics stream, covering the theory and practice of multivariate analysis. Students will gain deeper knowledge of interaction effects in regression models and its interpretation as well as introduction to ordered and categorical regression models. You will learn why and when to use interaction between explanatory variables, to do simple marginal effects of interaction variables, to understand the principles for employing multinomial and ordered categorical models, to perform simple models or these kind, and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind interaction effects, multinomial and ordered categorical models. The other half is lab-based, in which students will work through practical exercises using Stata statistical software.

All students wishing to book a place on this module must have either:

• Completed the SSRMC Skill Check

OR

• Have successfully completed Foundations in Applied Statistics (FiAS), Basic Quantitative Analysis (BQA) and Doing Multivariate Analysis (DMA)

before a place can be booked for them.

Students that have already completed the SSRMC Skill Check may have had a place booked for them by their Department. Students can check this by typing their CRSid into the search box at the very top right of this page, hitting the enter key then clicking on their name. This will show all module(s) that they are booked onto, as applicable.

Bookings for this module can also be made via:

• The Basic Statistics Stream Booking Form on the SSRMC website.
• In cases where you have a problem or a clash, please contact the SSRMC Administrator who will try to help you.

This module is an extension of the three previous modules in the Basic Statistics stream, covering the theory and practice of multivariate analysis. Students will gain deeper knowledge of interaction effects in regression models and its interpretation as well as introduction to ordered and categorical regression models. You will learn why and when to use interaction between explanatory variables, to do simple marginal effects of interaction variables, to understand the principles for employing multinomial and ordered categorical models, to perform simple models or these kind, and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind interaction effects, multinomial and ordered categorical models. The other half is lab-based, in which students will work through practical exercises using Stata statistical software.

All students wishing to book a place on this module must have either:

• Completed the SSRMC Skill Check

OR

• Have successfully completed Foundations in Applied Statistics (FiAS), Basic Quantitative Analysis (BQA) and Doing Multivariate Analysis (DMA)

before a place can be booked for them.

Students that have already completed the SSRMC Skill Check may have had a place booked for them by their Department. Students can check this by typing their CRSid into the search box at the very top right of this page, hitting the enter key then clicking on their name. This will show all module(s) that they are booked onto, as applicable.

Bookings for this module can also be made via:

• The Basic Statistics Stream Booking Form on the SSRMC website.
• In cases where you have a problem or a clash, please contact the SSRMC Administrator who will try to help you.

This module is an extension of the three previous modules in the Basic Statistics stream, covering the theory and practice of multivariate analysis. Students will gain deeper knowledge of interaction effects in regression models and its interpretation as well as introduction to ordered and categorical regression models. You will learn why and when to use interaction between explanatory variables, to do simple marginal effects of interaction variables, to understand the principles for employing multinomial and ordered categorical models, to perform simple models or these kind, and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind interaction effects, multinomial and ordered categorical models. The other half is lab-based, in which students will work through practical exercises using Stata statistical software.

All students wishing to book a place on this module must have either:

• Completed the SSRMC Skill Check

OR

• Have successfully completed Foundations in Applied Statistics (FiAS), Basic Quantitative Analysis (BQA) and Doing Multivariate Analysis (DMA)

before a place can be booked for them.

Students that have already completed the SSRMC Skill Check may have had a place booked for them by their Department. Students can check this by typing their CRSid into the search box at the very top right of this page, hitting the enter key then clicking on their name. This will show all module(s) that they are booked onto, as applicable.

Bookings for this module can also be made via:

• The Basic Statistics Stream Booking Form on the SSRMC website.
• In cases where you have a problem or a clash, please contact the SSRMC Administrator who will try to help you.

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. This session involves practical examples from qualitative analysis software. The final session provides an opportunity for a hands-on session, to which students should bring their interview material (at whatever stage of the process: whether writing interview questions, coding or analysing data) in order to receive advice and support in taking the interview material/data to the next stage of the research process.

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. Students who attend this course will be able to design their own evaluate research that uses surveys, in particular to understand issues concerning sample selection, response bias and data analysis; to appreciate and use basic principles of questionnaire design; and to trace appropriate sources of data and appropriate exemplars of good survey practice.

This module provides an applied introduction to panel data analysis (PDA). Panel data are gathered by taking repeated observations from a series of research units (eg. individuals, firms) as they move through time. This course focuses primarily on panel data with a large number of research units tracked for a relatively small number of time points.

The module begins by introducing key concepts, benefits and pitfalls of PDA. Students are then taught how to manipulate and describe panel data in Stata. The latter part of the module introduces random and fixed effects panel models for continuous and dichotomous outcomes. The course is taught through a mixture of lectures and practical sessions designed to give students hands-on experience of working with real-world data from the British Household Panel Survey.

This module provides an applied introduction to panel data analysis (PDA). Panel data are gathered by taking repeated observations from a series of research units (eg. individuals, firms) as they move through time. This course focuses primarily on panel data with a large number of research units tracked for a relatively small number of time points.

The module begins by introducing key concepts, benefits and pitfalls of PDA. Students are then taught how to manipulate and describe panel data in Stata. The latter part of the module introduces random and fixed effects panel models for continuous and dichotomous outcomes. The course is taught through a mixture of lectures and practical sessions designed to give students hands-on experience of working with real-world data from the British Household Panel Survey.

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).

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.