Social Sciences Research Methods Programme course timetable

This module is an extension of the three previous modules in the Basic Statistics stream, and introduces more complex and nuanced aspects of the theory and practice of mutivariate analysis. Students will learn the theory behind the methods covered, how to implement them in practice, how to interpret their results, and how to write intelligently about their findings. Half of the module is based in the lecture theatre; the other half is lab-based, in which students will work through practical exercises using the statistical software Stata.

Topics covered include:

• Interaction effects in regression models: how to estimate these and how to interpret them
• Marginal effects from interacted models
• Ordered and categorical discrete dependent variable models (ordered and multinomial logit and probit)

To get the most out of the course, you should also expect to spend some time between sessions building your own statistical models.

This module is an introduction to ethnographic fieldwork and analysis and 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.

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.

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: Ethnographies in Confinement
The practice of ethnography varies greatly depending on its setting. This session will consider the value, practice, epistemology and ethics of ethnographic research conducted in organisations, particularly those, such as prisons and psychiatric institutions, which confine people. How can we ensure access, and what are the political and ethical ramifications of doing so? How can we ethically conduct research in an institution in which people are held against their will? What are the epistemological issues when ‘free’ researchers conduct research in spaces of confinement?

Session 3: Ethnographies of Freedom
Building on the previous week’s session, this session this session will consider how the practice of ethnography differs when it is conducted in more permeable institutions. There are many advantages to conducting research where the setting is less boundaried – access is less complex, and consent can feel harder to gauge – but other issues are raised. What is the role of the ethnographer in something that looks like everyday life? What does it mean to leave the field? What is the difference between ‘research’ and ‘friendship’? And what actually is the site of study?

Session 4: 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?

Propensity score matching (PSM) is a technique that simulates an experimental study in an observational data set in order to estimate a causal effect. In an experimental study, subjects are randomly allocated to “treatment” and “control” groups; if the randomisation is done correctly, there should be no differences in the background characteristics of the treated and non-treated groups, so any differences in the outcome between the two groups may be attributed to a causal effect of the treatment. An observational survey, by contrast, will contain some people who have been subject to the “treatment” and some people who have not, but they will not have not been randomly allocated to those groups. The characteristics of people in the treatment and control groups may differ, so differences in the outcome cannot be attributed to the treatment. PSM attempts to mimic the experimental situation trial by creating two groups from the sample, whose background characteristics are virtually identical. People in the treatment group are “matched” with similar people in the control group. The difference between the treatment and control groups in this case should may therefore more plausibly be attributed to the treatment itself. PSM is widely applied in many disciplines, including sociology, criminology, economics, politics, and epidemiology. The module covers the basic theory of PSM, the steps in the implementation (e.g. variable choice for matching and types of matching algorithms), and assessment of matching quality. We will also work through practical exercises using Stata, in which students will learn how to apply the technique to the analysis of real data and how to interpret the results.

Propensity score matching (PSM) is a technique that simulates an experimental study in an observational data set in order to estimate a causal effect. In an experimental study, subjects are randomly allocated to “treatment” and “control” groups; if the randomisation is done correctly, there should be no differences in the background characteristics of the treated and non-treated groups, so any differences in the outcome between the two groups may be attributed to a causal effect of the treatment. An observational survey, by contrast, will contain some people who have been subject to the “treatment” and some people who have not, but they will not have not been randomly allocated to those groups. The characteristics of people in the treatment and control groups may differ, so differences in the outcome cannot be attributed to the treatment. PSM attempts to mimic the experimental situation trial by creating two groups from the sample, whose background characteristics are virtually identical. People in the treatment group are “matched” with similar people in the control group. The difference between the treatment and control groups in this case should may therefore more plausibly be attributed to the treatment itself. PSM is widely applied in many disciplines, including sociology, criminology, economics, politics, and epidemiology. The module covers the basic theory of PSM, the steps in the implementation (e.g. variable choice for matching and types of matching algorithms), and assessment of matching quality. We will also work through practical exercises using Stata, in which students will learn how to apply the technique to the analysis of real data and how to interpret the results.

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.

In order for the findings of statistical analysis to be generalisable, the sample on which the analysis is based should be representative of the population from which it is drawn. But it is well known that some groups are under-represented in social science surveys: they may be harder to contact in the first place, less likely to agree to participate in the survey, or less likely to answer particular questions even if they do agree to participate.

This short module will introduce students to the techniques used by survey statisticians to overcome these problems. Weighting is used to deal with the problem of certain groups being under-represented in the sample; imputation is used to deal with missing answers to individual questions. Students will learn how and why weighting and imputation work, and will be taken through practical lab-based exercises which will teach them how to work with secondary data containing weights or imputed values.

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 three three-hour sessions, split between lectures and practical exercises.

At the start of the module, the theoretical aspects of designing surveys will feature more, and topics covered include: 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.

As the module progresses the practical aspects of designing surveys will feature more, particularly 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, 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 the more practical sessions, but it will be of most direct relevance to those who are using, or plan to use, surveys in their research.

In this module students will be introduced to meta-analysis, a powerful statistical technique allowing researchers to synthesize the 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 course will introduce students to 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 led 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.

• To understand that the emphasis on statistical significance testing has obscured the goals of analysing data for many social scientists.
• To discuss other ways in which the significance testing paradigm has perverted scientific research, such as through the replication crisis and fraud.
• To understand the role of graphics in EDA

This module is an introduction to ethnographic fieldwork and analysis and 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.

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.

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: Ethnographies in Confinement
The practice of ethnography varies greatly depending on its setting. This session will consider the value, practice, epistemology and ethics of ethnographic research conducted in organisations, particularly those, such as prisons and psychiatric institutions, which confine people. How can we ensure access, and what are the political and ethical ramifications of doing so? How can we ethically conduct research in an institution in which people are held against their will? What are the epistemological issues when ‘free’ researchers conduct research in spaces of confinement?

Session 3: Ethnographies of Freedom
Building on the previous week’s session, this session this session will consider how the practice of ethnography differs when it is conducted in more permeable institutions. There are many advantages to conducting research where the setting is less boundaried – access is less complex, and consent can feel harder to gauge – but other issues are raised. What is the role of the ethnographer in something that looks like everyday life? What does it mean to leave the field? What is the difference between ‘research’ and ‘friendship’? And what actually is the site of study?

Session 4: 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?

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.

• Introduction to PDA: Concepts and uses
• Manipulating and describing panel data
• An overview of random effects, fixed effects and ‘hybrid’ panel models
• Panel models for dichotomous outcomes

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.

• Introduction to PDA: Concepts and uses
• Manipulating and describing panel data
• An overview of random effects, fixed effects and ‘hybrid’ panel models
• Panel models for dichotomous outcomes

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 is an introductory course for students whose research involves collecting, storing or analysing data using networked digital devices. Unless your research data is only collected using pen and paper or tape recorders and is written up on a manual typewriter, this course will be relevant to you. If you are planning to collect data online through either public or private communications, or you intend to share or publish data collected by other means it will be essential.

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 some sample data and questions will be provided for students who wish to take the material into practice.

In this module students will be introduced to meta-analysis, a powerful statistical technique allowing researchers to synthesize the 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 course will provide a detailed critique of the methods and philosophy of the Null Hypothesis Significance Testing (NHST) approach to statistics which is currently dominant in social and biomedical science. We will briefly contrast NHST with alternatives, especially with Bayesian methods. We will use some computer code (Matlab and R) to demonstrate some issues. However, we will focus on the big picture rather on the implementation of specific procedures.

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 three three-hour sessions, split between lectures and practical exercises.

At the start of the module, the theoretical aspects of designing surveys will feature more, and topics covered include: 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.

As the module progresses the practical aspects of designing surveys will feature more, particularly 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, 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 the more practical sessions, but it will be of most direct relevance to those who are using, or plan to use, surveys in their research.

This module is an introduction to ethnographic fieldwork and analysis and 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.

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.

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: Ethnographies in Confinement
The practice of ethnography varies greatly depending on its setting. This session will consider the value, practice, epistemology and ethics of ethnographic research conducted in organisations, particularly those, such as prisons and psychiatric institutions, which confine people. How can we ensure access, and what are the political and ethical ramifications of doing so? How can we ethically conduct research in an institution in which people are held against their will? What are the epistemological issues when ‘free’ researchers conduct research in spaces of confinement?

Session 3: Ethnographies of Freedom
Building on the previous week’s session, this session this session will consider how the practice of ethnography differs when it is conducted in more permeable institutions. There are many advantages to conducting research where the setting is less boundaried – access is less complex, and consent can feel harder to gauge – but other issues are raised. What is the role of the ethnographer in something that looks like everyday life? What does it mean to leave the field? What is the difference between ‘research’ and ‘friendship’? And what actually is the site of study?

Session 4: 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?

This intensive one-day 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 very recent papers, as well as 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.

This intensive one-day 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 very recent papers, as well as 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.