Social Sciences Research Methods Programme course timetable

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?

In this module, students will be introduced to multilevel modelling, also known as hierarchical linear modelling. MLM allows the user to analyse how outcomes are influenced by factors acting at multiple levels. So, for example, we might conceptualise children's educational process as being influenced by individual or family-level factors, as well as by factors operating at the level of the school or the neighbourhood. Similarly, outcomes for prisoners might be influenced by individual and/or family-level characteristics, as well as by the characteristics of the prison in which they are detained.

• Introduction to Stata/MLM theory
• Applications I - Random intercept models
• Applications II - Random slope models
• Applications III - Revision session/growth models

In this module, students will be introduced to multilevel modelling, also known as hierarchical linear modelling. MLM allows the user to analyse how outcomes are influenced by factors acting at multiple levels. So, for example, we might conceptualise children's educational process as being influenced by individual or family-level factors, as well as by factors operating at the level of the school or the neighbourhood. Similarly, outcomes for prisoners might be influenced by individual and/or family-level characteristics, as well as by the characteristics of the prison in which they are detained.

• Introduction to Stata/MLM theory
• Applications I - Random intercept models
• Applications II - Random slope models
• Applications III - Revision session/growth models

The analysis of policy depends on many disciplines and techniques and so is difficult for many researchers to access. This module provides a mixed perspective on policy analysis, taking both an academic and a practitioner perspective. This is because the same tools and techniques can be used in academic research on policy options and change as those used in practice in a policy environment. This course is provided as three 2 hour sessions delivered as a mix of lectures and seminars. No direct analysis work will be done in the sessions themselves, but sample data and questions will be provided for students who wish to take the material into practice.

Session 1
How do we analyse policy development and change over time? The policy cycle and models of policy change In studying how policies are developed and chosen there are two different timescales to consider- the immediate process of policy development (the policy cycle) and the evolution of a policy over long periods of time (models of policy change). This session will outline both timescales and discuss how these models can be applied to study policy change, highlighting the contested nature of most models of policy.

Session 2
What tools do we use to analyse policy options I – CBA and MCDA in policy analysis Policy analysis is a distinct practice that is forward looking, taking an issue and trying to both develop options and to provide a decision framework for making a policy choice. This first of two sessions provides a brief overview of cost-benefit analysis (CBA) and multi-criteria decision analysis (MCDA) and gives examples of their use in policy decision making.

Session 3
What tools do we use to analyse policy options II – using regressions in policy analysis Much of the information that policymakers need is provided through the outputs of regression analysis of varying complexity. This session will review the output of ordinary least squares and logistic regressions and use examples of their use in policy to discuss the strengths and weaknesses of using regression analysis in different policy analysis contexts.

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.)