Social Sciences Research Methods Programme course timetable

The course will provide students with an introduction to the popular and powerful statistics package Stata. Stata is commonly used by analysts in both the social and natural sciences, and is the statistics package used most widely by the SSRMC. You will learn:

• How to open and manage a dataset in Stata
• How to recode variables
• How to select a sample for analysis
• The commands needed to perform simple statistical analyses in Stata
• Where to find additional resources to help you as you progress with Stata

The course is intended for students who already have a working knowledge of statistics - it's designed primarily as a ""second language"" course for students who are already familiar with another package, perhaps R or SPSS. Students who don't already have a working knowledge of applied statistics should look at courses in our Basic Statistics Stream.

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.

Topics:

• Session 1: The Roots of Conversation Analysis
• Session 2: Ordinary Talk
• Session 3: Institutional Talk
• Session 4: Conversation Analysis and Critical Discourse Analysis

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 is shared with Geography. Students from the Department of Geography MUST book places on this course via the Department; any bookings made by Geography students via the SSRMC portal will be cancelled.

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.

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 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 four two-hour sessions, each of which has two parts.

The first hour of each session will consist of a lecture. The four lectures cover: the background to and history of survey research (with examples mostly drawn from political polling); an overview of the issues involved in analysing data from surveys conducted by others and some practical advice on how to evaluate such data; issues of sampling, non-response and different ways of doing surveys; issues related to questionnaire design (question wording, answer options, etc.) and ethical considerations. These lectures are relevant for all students taking the module, irrespective of whether they will conduct surveys themselves or are 'passive' users of survey results. Students who have attended these lectures will be able to evaluate research that uses surveys, in particular to understand issues concerning sample selection, response bias and data analysis; to appreciate and understand basic principles of questionnaire design; and to trace appropriate sources of data and appropriate exemplars of good survey practice.

The second hour of each session will focus more on the practical aspects of designing surveys and will feature some practical exercises. The focus will primarily be on issues directly related to questionnaires (and less on issues of sampling), such as the wording of questions, the order of questions, and the use of different answer options. Most of the exercises will be provided by the instructors (and we may provide opportunities to field successful exercises as part of YouGov surveys), but there will also be opportunities for students to bring in examples of surveys they would like to develop for their own research (and participants in the sessions may be asked to answer each other's surveys as a pilot test). We encourage all students registered for the module to attend these second parts of the sessions, but it will be of most direct relevant to who are using, or plan to use, surveys in their research. (It should also be noted that all students attending the second hour of the sessions are expected to participate and engage with the exercises.)

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.

Topics:

• Session 1: The Roots of Conversation Analysis
• Session 2: Ordinary Talk
• Session 3: Institutional Talk
• Session 4: Conversation Analysis and Critical Discourse Analysis

This introductory course is for graduate students who have no prior training in social network analysis (SNA). The course overviews the literature on SNA, and teaches how to handle databases, run network statistics, and visualise graphs.

Topics covered

• An overview of themes in the literature on SNA
• Searching, producing, and formating relational data
• Basic network statistics using R
• Visualisation of graphs

This introductory course is for graduate students who have no prior training in social network analysis (SNA). The course overviews the literature on SNA, and teaches how to handle databases, run network statistics, and visualise graphs.

Topics covered

• An overview of themes in the literature on SNA
• Searching, producing, and formating relational data
• Basic network statistics using R
• Visualisation of graphs

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 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 four two-hour sessions, each of which has two parts.

The first hour of each session will consist of a lecture. The four lectures cover: the background to and history of survey research (with examples mostly drawn from political polling); an overview of the issues involved in analysing data from surveys conducted by others and some practical advice on how to evaluate such data; issues of sampling, non-response and different ways of doing surveys; issues related to questionnaire design (question wording, answer options, etc.) and ethical considerations. These lectures are relevant for all students taking the module, irrespective of whether they will conduct surveys themselves or are 'passive' users of survey results. Students who have attended these lectures will be able to evaluate research that uses surveys, in particular to understand issues concerning sample selection, response bias and data analysis; to appreciate and understand basic principles of questionnaire design; and to trace appropriate sources of data and appropriate exemplars of good survey practice.

The second hour of each session will focus more on the practical aspects of designing surveys and will feature some practical exercises. The focus will primarily be on issues directly related to questionnaires (and less on issues of sampling), such as the wording of questions, the order of questions, and the use of different answer options. Most of the exercises will be provided by the instructors (and we may provide opportunities to field successful exercises as part of YouGov surveys), but there will also be opportunities for students to bring in examples of surveys they would like to develop for their own research (and participants in the sessions may be asked to answer each other's surveys as a pilot test). We encourage all students registered for the module to attend these second parts of the sessions, but it will be of most direct relevant to who are using, or plan to use, surveys in their research. (It should also be noted that all students attending the second hour of the sessions are expected to participate and engage with the exercises.)

The focus of these two sessions will be the linking of theory to method, paying particular attention to the relationship between language or other forms of representation or communication and the broader social milieu with special attention to power relations. The topic will be approached from a broadly Foucauldian angle: Foucault writes that discourse “consists of not—of no longer—treating discourses as groups of signs signifying elements referring to contents of representations, but as practices that systematically form the objects of which they speak.” The emphasis of these two lectures will be less upon what is known as ‘conversation analysis’ or ‘content analysis’ and more on methods based on post-positivist methods and critical theory which emphasize how language and other social practices create reality rather than reflect it, and thus methods of interpreting discourse are themselves not ideologically or politically neutral practices.

Session 1: The origins of critical discourse analysis (the Frankfurt school, Foucault, post-structuralism, feminism); how theoretical backgrounds shape research design
Session 2: 'Doing' discourse analysis: analysing methods and approaches

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.

This module is an introduction to ethnographic fieldwork and analysis.

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.

This module 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.

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: Photography and Audio Recording in Ethnographic Work What kinds of audiovisual equipment, and practices of photography and sound recording, can be used to support an ethnographer’s research process? What kinds of the epistemological, theoretical, social, and ethical considerations tend to arise around possible use of these technologies in anthropological fieldwork and analysis?