SSRMC Training Programme 2019-20
(Wed 9 Oct 2019 - Wed 8 Apr 2020)
[ Back to start of the programme ]
Tuesday 11 February 2020
| 14:00 |
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:
To get the most out of the course, you should also expect to spend some time between sessions building your own statistical models. |
|
Qualitative interviews are often used in the social sciences to learn more about the world and can be particularly appropriate for people we might class as vulnerable. The course will try to achieve two things. First, it will have a strong practical arc, guiding students through the complete process of designing and delivering interviews and what to do with the data when you have it. It is particularly important, therefore, that students come to the course prepared with a research question in mind (it does not have to be your actual dissertation topic). Second, we will repeatedly think carefully about the challenges of interviewing with populations that are deemed vulnerable (especially prisoners, women in the criminal justice system, and people living with trauma). We will explore how, in all stages of the research cycle, questions of ethics and the importance of understanding ‘whole people’ remain pertinent. In the first session we will think about how to frame a study and research question, and how to design an interview schedule that allows you to access your question sensibly and creatively! We will also think about the challenges of interviewing those with trauma, in particular, as a case study. In the second session we will think through the challenges of actually undertaking interviews in the field. Many hints and tip will be shared, and students will be encouraged to undertake a short mock interview. In the third session we explore various ways in which to approach a mass of interview data and different approaches towards analysis. In the final session, we burrow down into analysis and talk about how to write up your research. In both of the final sessions students will be asked to engage with real interview transcripts that have been anonymised. |
|
|
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:
To get the most out of the course, you should also expect to spend some time between sessions building your own statistical models. |
|
| 15:30 |
Ethnographic Methods
Finished
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
Session 2: Photography and Audio Recording in Ethnographic Work Session 3: Relationships in the Field Session 4: Defining the Fieldsite PLEASE NOTE: Update on additional teaching - we have now scheduled the two additional sessions on 18 and 25 February. Further information on their content will follow. |
| 16:00 |
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:
To get the most out of the course, you should also expect to spend some time between sessions building your own statistical models. |
|
Conversation and Discourse Analysis
Finished
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:
|
Wednesday 12 February 2020
| 14:00 |
Atlas.ti
Finished
These two sessions will provide a basic introduction to the management and analysis of qualitative data using Atlas.ti. The sessions will introduce participants to the following:
Please note: Atlas.ti for Mac will not be covered. |
Friday 14 February 2020
| 09:00 |
This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself , and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software. To get the most out of the course, you should also expect to spend some time between sessions having fun by building your own statistical models. |
| 11:00 |
This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself , and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software. To get the most out of the course, you should also expect to spend some time between sessions having fun by building your own statistical models. |
| 14:00 |
This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself , and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software. To get the most out of the course, you should also expect to spend some time between sessions having fun by building your own statistical models. |
| 16:00 |
This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself , and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software. To get the most out of the course, you should also expect to spend some time between sessions having fun by building your own statistical models. |
Monday 17 February 2020
| 15:00 |
Survey Research and Design
Finished
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. |
Tuesday 18 February 2020
| 14:00 |
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:
To get the most out of the course, you should also expect to spend some time between sessions building your own statistical models. |
|
Qualitative interviews are often used in the social sciences to learn more about the world and can be particularly appropriate for people we might class as vulnerable. The course will try to achieve two things. First, it will have a strong practical arc, guiding students through the complete process of designing and delivering interviews and what to do with the data when you have it. It is particularly important, therefore, that students come to the course prepared with a research question in mind (it does not have to be your actual dissertation topic). Second, we will repeatedly think carefully about the challenges of interviewing with populations that are deemed vulnerable (especially prisoners, women in the criminal justice system, and people living with trauma). We will explore how, in all stages of the research cycle, questions of ethics and the importance of understanding ‘whole people’ remain pertinent. In the first session we will think about how to frame a study and research question, and how to design an interview schedule that allows you to access your question sensibly and creatively! We will also think about the challenges of interviewing those with trauma, in particular, as a case study. In the second session we will think through the challenges of actually undertaking interviews in the field. Many hints and tip will be shared, and students will be encouraged to undertake a short mock interview. In the third session we explore various ways in which to approach a mass of interview data and different approaches towards analysis. In the final session, we burrow down into analysis and talk about how to write up your research. In both of the final sessions students will be asked to engage with real interview transcripts that have been anonymised. |
|
|
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:
To get the most out of the course, you should also expect to spend some time between sessions building your own statistical models. |
|
| 15:30 |
Ethnographic Methods
Finished
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
Session 2: Photography and Audio Recording in Ethnographic Work Session 3: Relationships in the Field Session 4: Defining the Fieldsite PLEASE NOTE: Update on additional teaching - we have now scheduled the two additional sessions on 18 and 25 February. Further information on their content will follow. |
| 16:00 |
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:
To get the most out of the course, you should also expect to spend some time between sessions building your own statistical models. |
Wednesday 19 February 2020
| 09:00 |
Propensity Score Matching
Finished
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. |
|
Time Series Analysis (Intensive)
Finished
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:
|
|
| 14:00 |
Propensity Score Matching
Finished
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. |
|
Time Series Analysis (Intensive)
Finished
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:
|
Monday 24 February 2020
| 13:00 |
Weighting and Imputation
Finished
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. |
| 14:00 |
Public Policy Analysis
Finished
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. |
| 15:00 |
Survey Research and Design
Finished
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. |
Tuesday 25 February 2020
| 14:00 |
Qualitative interviews are often used in the social sciences to learn more about the world and can be particularly appropriate for people we might class as vulnerable. The course will try to achieve two things. First, it will have a strong practical arc, guiding students through the complete process of designing and delivering interviews and what to do with the data when you have it. It is particularly important, therefore, that students come to the course prepared with a research question in mind (it does not have to be your actual dissertation topic). Second, we will repeatedly think carefully about the challenges of interviewing with populations that are deemed vulnerable (especially prisoners, women in the criminal justice system, and people living with trauma). We will explore how, in all stages of the research cycle, questions of ethics and the importance of understanding ‘whole people’ remain pertinent. In the first session we will think about how to frame a study and research question, and how to design an interview schedule that allows you to access your question sensibly and creatively! We will also think about the challenges of interviewing those with trauma, in particular, as a case study. In the second session we will think through the challenges of actually undertaking interviews in the field. Many hints and tip will be shared, and students will be encouraged to undertake a short mock interview. In the third session we explore various ways in which to approach a mass of interview data and different approaches towards analysis. In the final session, we burrow down into analysis and talk about how to write up your research. In both of the final sessions students will be asked to engage with real interview transcripts that have been anonymised. |
|
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:
To get the most out of the course, you should also expect to spend some time between sessions building your own statistical models. |