Social Sciences Research Methods Programme course timetable
January 2017
Tue 17 |
Introduction to R
Finished
This module introduces the use of R, a programming language originally developed for statistical data analysis. In this course, we will use R through R Studio, a user-friendly interface for R. Students will learn ways of reading spreadsheet data into R, the notion of data type, how to manipulate data in major data types, draw basic graphs, summarise data with descriptive statistics, and perform basic inferential statistics (e.g., t-test). This module is intended primarily for students who have no prior experience in programming. This course covers how to perform data analysis with R but does not introduce analytical techniques. |
Introduction to Stata (Series 2)
Finished
The course will provide students with an introduction to the popular and powerful statistics package Stata, a program commonly used in both social and natural sciences. |
|
Wed 18 |
Introduction to R
Finished
This module introduces the use of R, a programming language originally developed for statistical data analysis. In this course, we will use R through R Studio, a user-friendly interface for R. Students will learn ways of reading spreadsheet data into R, the notion of data type, how to manipulate data in major data types, draw basic graphs, summarise data with descriptive statistics, and perform basic inferential statistics (e.g., t-test). This module is intended primarily for students who have no prior experience in programming. This course covers how to perform data analysis with R but does not introduce analytical techniques. |
Introduction to Stata (Series 2)
Finished
The course will provide students with an introduction to the popular and powerful statistics package Stata, a program commonly used in both social and natural sciences. |
|
Mon 23 |
This foundational course is for graduate students who have no prior training in statistics. Topics covered include: the notion of variables and how they are measured; ways of describing the central tendency and the dispersion of a variable; and the principles of hypothesis testing and statistical significance. The course also introduces students to the software Stata. The day consists of a lecture, and a computer lab session with exercises in Stata. BookingsAll students wishing to book a place on this module must complete the SSRMC Skill Check before a place can be booked for them. Students that have already completed the Skill Check may have had a place booked for them by their Department. Students can check this by typing their CRSid into the search box at the very top right of this page, hitting the enter key then clicking on their name. This will show all module(s) that they are booked onto, as applicable. Students for whom this module is not compulsory can make a booking via the Basic Statistics Stream Booking Form on the SSRMC website. In cases where you have a problem or a clash, please contact the SSRMC Administrator who will try to help you. |
This foundational course is for graduate students who have no prior training in statistics. Topics covered include: the notion of variables and how they are measured; ways of describing the central tendency and the dispersion of a variable; and the principles of hypothesis testing and statistical significance. The course also introduces students to the software Stata. The day consists of a lecture, and a computer lab session with exercises in Stata. BookingsAll students wishing to book a place on this module must complete the SSRMC Skill Check before a place can be booked for them. Students that have already completed the Skill Check may have had a place booked for them by their Department. Students can check this by typing their CRSid into the search box at the very top right of this page, hitting the enter key then clicking on their name. This will show all module(s) that they are booked onto, as applicable. Students for whom this module is not compulsory can make a booking via the Basic Statistics Stream Booking Form on the SSRMC website. In cases where you have a problem or a clash, please contact the SSRMC Administrator who will try to help you. |
|
Comparative Historical Methods
Finished
Week 2 - The Janus-Faced nature of Nationalism This module will start by analyzing the so-called ‘Dark side’ of Nationalism often associated with xenophobia, ethnic cleansing and racism. In contrast, the Democratic side of Nationalism will be connected with the quest for recognition of national and ethnic minorities in the West. Key questions: What are the major strengths of Nationalism? What do we mean by Nationalism? In which circumstances can we refer to nationalism as an ideology of inclusion and exclusion? Week 3 - Globalization and National Identity Identity is a definition, an interpretation of the self that establishes what and where the person is both in social and psychological terms. We will explore the contrast between Individual and Collective forms of identity. Key theories of nationalism will be will be taken and discussed in class into account the relevance of Nationalism in modern History. Week 4 - The Rise of the Radical Right in Europe We are witnessing a widening gap between the elites and the unemployed. In this context, feelings of vulnerability, fear of immigrants and resentment towards both the state and society come to the fore. Inequality comes to the fore and, in this context, the Radical Right is able gain support. Key Questions to be debated in class:
|
|
Tue 24 |
This module introduces students to four of the most commonly used statistical tests in the social sciences: correlation, chi-square tests, t-tests, and analysis of variance (ANOVA). Building upon the univariate techniques introduced in the Foundations in Applied Statistics module, these sessions aim to provide students with a thorough understanding of statistical methods designed to test associations between two variables (bivariate statistics). Students will learn about the assumptions underlying each test, and will receive practical instruction on how to generate and interpret bivariate results using Stata. BookingsBefore a place can be booked for them, all students wishing to book a place on this module must have either:
OR
Students for whom this module is not compulsory can make a booking via the Basic Statistics Stream Booking Form on the SSRMC website. In cases where you have a problem or a clash, please contact the SSRMC Administrator who will try to help you. |
This module introduces students to four of the most commonly used statistical tests in the social sciences: correlation, chi-square tests, t-tests, and analysis of variance (ANOVA). Building upon the univariate techniques introduced in the Foundations in Applied Statistics module, these sessions aim to provide students with a thorough understanding of statistical methods designed to test associations between two variables (bivariate statistics). Students will learn about the assumptions underlying each test, and will receive practical instruction on how to generate and interpret bivariate results using Stata. BookingsBefore a place can be booked for them, all students wishing to book a place on this module must have either:
OR
Students for whom this module is not compulsory can make a booking via the Basic Statistics Stream Booking Form on the SSRMC website. In cases where you have a problem or a clash, please contact the SSRMC Administrator who will try to help you. |
|
Doing Qualitative Interviews
Finished
Face-to-face interviews are used to collect a wide range of information in the social sciences. They are appropriate for the gathering of information on individual and institutional patterns of behaviour; complex histories or processes; identities and cultural meanings; routines that are not written down; and life-history events. Face-to-face interviews thus comprise an appropriate method to generate information on individual behaviour, the reasons for certain patterns of acting and talking, and the type of connection people have with each other.
The first session provides an overview of interviewing as a social research method, then focuses on the processes of organising and conducting qualitative interviews. The second session explores the ethics and practical constraints of interviews as a research method, particularly relevant when attempting to engage with marginalised or stigmatised communities. The third session focuses on organisation and analysis after interviews, including interpretation through coding and close reading. This session involves practical examples from qualitative analysis software. The final session provides an opportunity for a hands-on session, to which students should bring their interview material (at whatever stage of the process: whether writing interview questions, coding or analysing data) in order to receive advice and support in taking the interview material/data to the next stage of the research process. |
|
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. Students who attend this course will be able to design their own evaluate research that uses surveys, in particular to understand issues concerning sample selection, response bias and data analysis; to appreciate and use basic principles of questionnaire design; and to trace appropriate sources of data and appropriate exemplars of good survey practice. |
|
Wed 25 |
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 sample data and questions will be provided for students who wish to take the material into practice. |
Mon 30 |
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. BookingsBefore a place can be booked for them, all students wishing to book a place on this module must have either:
OR
Students for whom this module is not compulsory can make a booking via the Basic Statistics Stream Booking Form on the SSRMC website. In cases where you have a problem or a clash, please contact the SSRMC Administrator who will try to help you. |
This module 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. BookingsBefore a place can be booked for them, all students wishing to book a place on this module must have either:
OR
Students for whom this module is not compulsory can make a booking via the Basic Statistics Stream Booking Form on the SSRMC website. In cases where you have a problem or a clash, please contact the SSRMC Administrator who will try to help you. |
|
Comparative Historical Methods
Finished
Week 2 - The Janus-Faced nature of Nationalism This module will start by analyzing the so-called ‘Dark side’ of Nationalism often associated with xenophobia, ethnic cleansing and racism. In contrast, the Democratic side of Nationalism will be connected with the quest for recognition of national and ethnic minorities in the West. Key questions: What are the major strengths of Nationalism? What do we mean by Nationalism? In which circumstances can we refer to nationalism as an ideology of inclusion and exclusion? Week 3 - Globalization and National Identity Identity is a definition, an interpretation of the self that establishes what and where the person is both in social and psychological terms. We will explore the contrast between Individual and Collective forms of identity. Key theories of nationalism will be will be taken and discussed in class into account the relevance of Nationalism in modern History. Week 4 - The Rise of the Radical Right in Europe We are witnessing a widening gap between the elites and the unemployed. In this context, feelings of vulnerability, fear of immigrants and resentment towards both the state and society come to the fore. Inequality comes to the fore and, in this context, the Radical Right is able gain support. Key Questions to be debated in class:
|
|
Tue 31 |
Doing Qualitative Interviews
Finished
Face-to-face interviews are used to collect a wide range of information in the social sciences. They are appropriate for the gathering of information on individual and institutional patterns of behaviour; complex histories or processes; identities and cultural meanings; routines that are not written down; and life-history events. Face-to-face interviews thus comprise an appropriate method to generate information on individual behaviour, the reasons for certain patterns of acting and talking, and the type of connection people have with each other.
The first session provides an overview of interviewing as a social research method, then focuses on the processes of organising and conducting qualitative interviews. The second session explores the ethics and practical constraints of interviews as a research method, particularly relevant when attempting to engage with marginalised or stigmatised communities. The third session focuses on organisation and analysis after interviews, including interpretation through coding and close reading. This session involves practical examples from qualitative analysis software. The final session provides an opportunity for a hands-on session, to which students should bring their interview material (at whatever stage of the process: whether writing interview questions, coding or analysing data) in order to receive advice and support in taking the interview material/data to the next stage of the research process. |
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. Students who attend this course will be able to design their own evaluate research that uses surveys, in particular to understand issues concerning sample selection, response bias and data analysis; to appreciate and use basic principles of questionnaire design; and to trace appropriate sources of data and appropriate exemplars of good survey practice. |
February 2017
Wed 1 |
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 sample data and questions will be provided for students who wish to take the material into practice. |
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. |
|
Mon 6 |
This module is an extension of the three previous modules in the Basic Statistics stream, covering the theory and practice of multivariate analysis. Students will gain deeper knowledge of interaction effects in regression models and its interpretation as well as introduction to ordered and categorical regression models. You will learn why and when to use interaction between explanatory variables, to do simple marginal effects of interaction variables, to understand the principles for employing multinomial and ordered categorical models, to perform simple models or these kind, and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind interaction effects, multinomial and ordered categorical models. The other half is lab-based, in which students will work through practical exercises using Stata statistical software. All students wishing to book a place on this module must have either:
OR
before a place can be booked for them.
|
This module is an extension of the three previous modules in the Basic Statistics stream, covering the theory and practice of multivariate analysis. Students will gain deeper knowledge of interaction effects in regression models and its interpretation as well as introduction to ordered and categorical regression models. You will learn why and when to use interaction between explanatory variables, to do simple marginal effects of interaction variables, to understand the principles for employing multinomial and ordered categorical models, to perform simple models or these kind, and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind interaction effects, multinomial and ordered categorical models. The other half is lab-based, in which students will work through practical exercises using Stata statistical software. All students wishing to book a place on this module must have either:
OR
before a place can be booked for them.
|
|
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. |
|
This module is an extension of the three previous modules in the Basic Statistics stream, covering the theory and practice of multivariate analysis. Students will gain deeper knowledge of interaction effects in regression models and its interpretation as well as introduction to ordered and categorical regression models. You will learn why and when to use interaction between explanatory variables, to do simple marginal effects of interaction variables, to understand the principles for employing multinomial and ordered categorical models, to perform simple models or these kind, and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind interaction effects, multinomial and ordered categorical models. The other half is lab-based, in which students will work through practical exercises using Stata statistical software. All students wishing to book a place on this module must have either:
OR
before a place can be booked for them.
|
|
Meta Analysis
Finished
Students are introduced to meta-analysis, a powerful statistical technique allowing researchers to synthesize available evidence for a given research question using standardized (comparable) effect sizes across studies. The sessions teach students how to compute treatment effects, how to compute effect sizes based on correlational studies, how to address questions such as what is the association of bullying victimization with depression? The module will be useful for students who seek to draw statistical conclusions in a standardized manner from literature reviews they are conducting. |
|
This module is an extension of the three previous modules in the Basic Statistics stream, covering the theory and practice of multivariate analysis. Students will gain deeper knowledge of interaction effects in regression models and its interpretation as well as introduction to ordered and categorical regression models. You will learn why and when to use interaction between explanatory variables, to do simple marginal effects of interaction variables, to understand the principles for employing multinomial and ordered categorical models, to perform simple models or these kind, and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind interaction effects, multinomial and ordered categorical models. The other half is lab-based, in which students will work through practical exercises using Stata statistical software. All students wishing to book a place on this module must have either:
OR
before a place can be booked for them.
|
|
Tue 7 |
Doing Qualitative Interviews
Finished
Face-to-face interviews are used to collect a wide range of information in the social sciences. They are appropriate for the gathering of information on individual and institutional patterns of behaviour; complex histories or processes; identities and cultural meanings; routines that are not written down; and life-history events. Face-to-face interviews thus comprise an appropriate method to generate information on individual behaviour, the reasons for certain patterns of acting and talking, and the type of connection people have with each other.
The first session provides an overview of interviewing as a social research method, then focuses on the processes of organising and conducting qualitative interviews. The second session explores the ethics and practical constraints of interviews as a research method, particularly relevant when attempting to engage with marginalised or stigmatised communities. The third session focuses on organisation and analysis after interviews, including interpretation through coding and close reading. This session involves practical examples from qualitative analysis software. The final session provides an opportunity for a hands-on session, to which students should bring their interview material (at whatever stage of the process: whether writing interview questions, coding or analysing data) in order to receive advice and support in taking the interview material/data to the next stage of the research process. |
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. Students who attend this course will be able to design their own evaluate research that uses surveys, in particular to understand issues concerning sample selection, response bias and data analysis; to appreciate and use basic principles of questionnaire design; and to trace appropriate sources of data and appropriate exemplars of good survey practice. |
|
Wed 8 |
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 sample data and questions will be provided for students who wish to take the material into practice. |
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. |
|
Thu 9 |
This is an Open Access module, so please read the course description carefully before making a booking, and be advised that spaces may be limited. This workshop series aims to provide introductory training on Geographical Information Systems. Material covered includes the construction of geodatabases from a range of data sources, geovisualisation and mapping from geodatasets, raster-based modeling and presentation of maps and charts and other geodata outputs. Each session will start with an introductory lecture followed by practical exercises bases around tools in GIS software packages (mainly ArcGIS). |
Mon 13 |
This module is an extension of the three previous modules in the Basic Statistics stream, covering the theory and practice of multivariate analysis. Students will gain deeper knowledge of interaction effects in regression models and its interpretation as well as introduction to ordered and categorical regression models. You will learn why and when to use interaction between explanatory variables, to do simple marginal effects of interaction variables, to understand the principles for employing multinomial and ordered categorical models, to perform simple models or these kind, and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind interaction effects, multinomial and ordered categorical models. The other half is lab-based, in which students will work through practical exercises using Stata statistical software. All students wishing to book a place on this module must have either:
OR
before a place can be booked for them.
|
This module is an extension of the three previous modules in the Basic Statistics stream, covering the theory and practice of multivariate analysis. Students will gain deeper knowledge of interaction effects in regression models and its interpretation as well as introduction to ordered and categorical regression models. You will learn why and when to use interaction between explanatory variables, to do simple marginal effects of interaction variables, to understand the principles for employing multinomial and ordered categorical models, to perform simple models or these kind, and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind interaction effects, multinomial and ordered categorical models. The other half is lab-based, in which students will work through practical exercises using Stata statistical software. All students wishing to book a place on this module must have either:
OR
before a place can be booked for them.
|
|
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. |
|
This module is an extension of the three previous modules in the Basic Statistics stream, covering the theory and practice of multivariate analysis. Students will gain deeper knowledge of interaction effects in regression models and its interpretation as well as introduction to ordered and categorical regression models. You will learn why and when to use interaction between explanatory variables, to do simple marginal effects of interaction variables, to understand the principles for employing multinomial and ordered categorical models, to perform simple models or these kind, and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind interaction effects, multinomial and ordered categorical models. The other half is lab-based, in which students will work through practical exercises using Stata statistical software. All students wishing to book a place on this module must have either:
OR
before a place can be booked for them.
|
|
Meta Analysis
Finished
Students are introduced to meta-analysis, a powerful statistical technique allowing researchers to synthesize available evidence for a given research question using standardized (comparable) effect sizes across studies. The sessions teach students how to compute treatment effects, how to compute effect sizes based on correlational studies, how to address questions such as what is the association of bullying victimization with depression? The module will be useful for students who seek to draw statistical conclusions in a standardized manner from literature reviews they are conducting. |
|
This module is an extension of the three previous modules in the Basic Statistics stream, covering the theory and practice of multivariate analysis. Students will gain deeper knowledge of interaction effects in regression models and its interpretation as well as introduction to ordered and categorical regression models. You will learn why and when to use interaction between explanatory variables, to do simple marginal effects of interaction variables, to understand the principles for employing multinomial and ordered categorical models, to perform simple models or these kind, and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind interaction effects, multinomial and ordered categorical models. The other half is lab-based, in which students will work through practical exercises using Stata statistical software. All students wishing to book a place on this module must have either:
OR
before a place can be booked for them.
|
|
Tue 14 |
Doing Qualitative Interviews
Finished
Face-to-face interviews are used to collect a wide range of information in the social sciences. They are appropriate for the gathering of information on individual and institutional patterns of behaviour; complex histories or processes; identities and cultural meanings; routines that are not written down; and life-history events. Face-to-face interviews thus comprise an appropriate method to generate information on individual behaviour, the reasons for certain patterns of acting and talking, and the type of connection people have with each other.
The first session provides an overview of interviewing as a social research method, then focuses on the processes of organising and conducting qualitative interviews. The second session explores the ethics and practical constraints of interviews as a research method, particularly relevant when attempting to engage with marginalised or stigmatised communities. The third session focuses on organisation and analysis after interviews, including interpretation through coding and close reading. This session involves practical examples from qualitative analysis software. The final session provides an opportunity for a hands-on session, to which students should bring their interview material (at whatever stage of the process: whether writing interview questions, coding or analysing data) in order to receive advice and support in taking the interview material/data to the next stage of the research process. |
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. Students who attend this course will be able to design their own evaluate research that uses surveys, in particular to understand issues concerning sample selection, response bias and data analysis; to appreciate and use basic principles of questionnaire design; and to trace appropriate sources of data and appropriate exemplars of good survey practice. |
|
Wed 15 |
Panel Data Analysis (Intensive)
Finished
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. |
Panel Data Analysis (Intensive)
Finished
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. |
|
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. |
|
Thu 16 |
This is an Open Access module, so please read the course description carefully before making a booking, and be advised that spaces may be limited. This workshop series aims to provide introductory training on Geographical Information Systems. Material covered includes the construction of geodatabases from a range of data sources, geovisualisation and mapping from geodatasets, raster-based modeling and presentation of maps and charts and other geodata outputs. Each session will start with an introductory lecture followed by practical exercises bases around tools in GIS software packages (mainly ArcGIS). |
Mon 20 |
Factor Analysis
Finished
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. |
Factor Analysis
Finished
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. |
|
Meta Analysis
Finished
Students are introduced to meta-analysis, a powerful statistical technique allowing researchers to synthesize available evidence for a given research question using standardized (comparable) effect sizes across studies. The sessions teach students how to compute treatment effects, how to compute effect sizes based on correlational studies, how to address questions such as what is the association of bullying victimization with depression? The module will be useful for students who seek to draw statistical conclusions in a standardized manner from literature reviews they are conducting. |
|
Tue 21 |
Factor Analysis
Finished
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. |
Factor Analysis
Finished
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. |
|
Agent-based Modelling with Netlogo
Finished
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. |
|
Wed 22 |
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. |
Thu 23 |
This is an Open Access module, so please read the course description carefully before making a booking, and be advised that spaces may be limited. This workshop series aims to provide introductory training on Geographical Information Systems. Material covered includes the construction of geodatabases from a range of data sources, geovisualisation and mapping from geodatasets, raster-based modeling and presentation of maps and charts and other geodata outputs. Each session will start with an introductory lecture followed by practical exercises bases around tools in GIS software packages (mainly ArcGIS). |
Mon 27 |
Meta Analysis
Finished
Students are introduced to meta-analysis, a powerful statistical technique allowing researchers to synthesize available evidence for a given research question using standardized (comparable) effect sizes across studies. The sessions teach students how to compute treatment effects, how to compute effect sizes based on correlational studies, how to address questions such as what is the association of bullying victimization with depression? The module will be useful for students who seek to draw statistical conclusions in a standardized manner from literature reviews they are conducting. |
Tue 28 |
Agent-based Modelling with Netlogo
Finished
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. |
March 2017
Wed 1 |
Multilevel Modelling
Finished
Students are introduced to multilevel modelling techniques (a.k.a. hierarchical linear modelling). MLM allows one to analyse how contexts influence outcomes ie do schools/neighbourhoods influence behaviour. Stata will be used during this module. No prior knowledge of Stata will be assumed. |
Multilevel Modelling
Finished
Students are introduced to multilevel modelling techniques (a.k.a. hierarchical linear modelling). MLM allows one to analyse how contexts influence outcomes ie do schools/neighbourhoods influence behaviour. Stata will be used during this module. No prior knowledge of Stata will be assumed. |
|
Thu 2 |
This is an Open Access module, so please read the course description carefully before making a booking, and be advised that spaces may be limited. This workshop series aims to provide introductory training on Geographical Information Systems. Material covered includes the construction of geodatabases from a range of data sources, geovisualisation and mapping from geodatasets, raster-based modeling and presentation of maps and charts and other geodata outputs. Each session will start with an introductory lecture followed by practical exercises bases around tools in GIS software packages (mainly ArcGIS). |
Mon 6 |
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, moving average, exponential smoothing and decomposition. The study of applied work is emphasized in this non-specialist module. |
Tue 7 |
The challenge of causal inference is ubiquitous in social science. Nearly every research project fundamentally is about causes and effects. This course will introduce graduate students to core issues about causal inference in quantitative social research, focusing especially on how one can move from demonstrating correlation to causation. The first lecture will define key concepts of correlates, risk factors, causes, mediators and moderators. The second lecture will discuss quasi-experimental research designs (studies without random assignment), and issues of “validity” in drawing causal conclusions. The third and fourth sessions will be lectures and practicals introducing two key analytic methods (propensity score matching and fixed effects regression models) that can be used to help identify causes. The course will focus on studies in which individual people are the basic unit of analyses, particularly longitudinal studies which follow the same people over multiple waves of assessment. |
The challenge of causal inference is ubiquitous in social science. Nearly every research project fundamentally is about causes and effects. This course will introduce graduate students to core issues about causal inference in quantitative social research, focusing especially on how one can move from demonstrating correlation to causation. The first lecture will define key concepts of correlates, risk factors, causes, mediators and moderators. The second lecture will discuss quasi-experimental research designs (studies without random assignment), and issues of “validity” in drawing causal conclusions. The third and fourth sessions will be lectures and practicals introducing two key analytic methods (propensity score matching and fixed effects regression models) that can be used to help identify causes. The course will focus on studies in which individual people are the basic unit of analyses, particularly longitudinal studies which follow the same people over multiple waves of assessment. |
|
Wed 8 |
This course will show, in a very practical way, 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 lead 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. |
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, moving average, exponential smoothing and decomposition. The study of applied work is emphasized in this non-specialist module. |
|
Research Ethics (Series 2)
Finished
Ethics is becoming an increasingly important issue for all researchers and the aim of these three sessions is to demonstrate the practical value of thinking seriously and systematically about what constitutes ethical conduct in social science research. The sessions will involve some small-group work. |
October 2017
Wed 4 |
SSRMC Student Induction Lecture
Finished
This event details how the SSRMC works, more about the modules we offer, and everything you need to know about making a booking. NB. ALL STUDENTS WISHING TO TAKE SSRMC COURSES THIS YEAR ARE EXPECTED TO ATTEND THIS INDUCTION SESSION |
Mon 9 |
This module is shared with Psychology. Students from the Department of Psychology MUST book places on this course via the Department; any bookings made by Psychology students via the SSRMC portal will be cancelled. The course focuses on practical hands-on variable handling and programming implementation using rather than on theory. This course is intended for those who have never programmed before, including those who only call/run Matlab scripts but are not familiar with how code works and how matrices are handled in Matlab. (Note that calling a couple of scripts is not 'real' programming.) MATLAB (C) is a powerful scientific programming environment optimal for data analysis and engineering solutions. More information on the programme and its uses can be found here: https://www.mathworks.com/products/matlab.html More information on the course can be found, here: http://www.psychol.cam.ac.uk/grads/grads/pg-prog/programming#section-0 |
This module is shared with Psychology. Students from the Department of Psychology MUST book places on this course via the Department; any bookings made by Psychology students via the SSRMC portal will be cancelled. The course focuses on practical hands-on variable handling and programming implementation using rather than on theory. This course is intended for those who have never programmed before, including those who only call/run Matlab scripts but are not familiar with how code works and how matrices are handled in Matlab. (Note that calling a couple of scripts is not 'real' programming.) MATLAB (C) is a powerful scientific programming environment optimal for data analysis and engineering solutions. More information on the programme and its uses can be found here: https://www.mathworks.com/products/matlab.html More information on the course can be found, here: http://www.psychol.cam.ac.uk/grads/grads/pg-prog/programming#section-0 |
|
Tue 10 |
This module is shared with Psychology. Students from the Department of Psychology MUST book places on this course via the Department; any bookings made by Psychology students via the SSRMC portal will be cancelled. The course focuses on practical hands-on variable handling and programming implementation using rather than on theory. This course is intended for those who have never programmed before, including those who only call/run Matlab scripts but are not familiar with how code works and how matrices are handled in Matlab. (Note that calling a couple of scripts is not 'real' programming.) MATLAB (C) is a powerful scientific programming environment optimal for data analysis and engineering solutions. More information on the programme and its uses can be found here: https://www.mathworks.com/products/matlab.html More information on the course can be found, here: http://www.psychol.cam.ac.uk/grads/grads/pg-prog/programming#section-0 |
This module is shared with Psychology. Students from the Department of Psychology MUST book places on this course via the Department; any bookings made by Psychology students via the SSRMC portal will be cancelled. The course focuses on practical hands-on variable handling and programming implementation using rather than on theory. This course is intended for those who have never programmed before, including those who only call/run Matlab scripts but are not familiar with how code works and how matrices are handled in Matlab. (Note that calling a couple of scripts is not 'real' programming.) MATLAB (C) is a powerful scientific programming environment optimal for data analysis and engineering solutions. More information on the programme and its uses can be found here: https://www.mathworks.com/products/matlab.html More information on the course can be found, here: http://www.psychol.cam.ac.uk/grads/grads/pg-prog/programming#section-0 |
|
Comparative Historical Methods
Finished
These four sessions will introduce students to comparative historical research methods, emphasizing their qualitative dimensions. In the first session, we will analyze some contemporary classics within this genre. In the second and third sessions, we will review and distinguish among a variety of intellectual justifications for this genre as a methodology. In the final session, we will focus on a "state of the art" defence of qualitative and comparative-historical research, both in theory and practice. Aims:
Topics:
|
|
Wed 11 |
This course will introduce students to the general philosophical debates concerning scientific methodology, assessing their ramifications for the conduct of qualitative social research. It will enable students to critically evaluate major programmes in the philosophy of sciences, considering whether there are important analytic differences between the social and natural sciences; and whether qualitative methods themselves comprise a unified approach to the study of social reality. Topics:
|
Mon 16 |
Research Ethics (Michaelmas)
Finished
Ethics is becoming an increasingly important issue for all researchers and the aim of this session is to demonstrate the practical value of thinking seriously and systematically about what constitutes ethical conduct in social science research. The session will involve a lecture component and some small-group work. Aims: Topics:
|
Tue 17 |
Comparative Historical Methods
Finished
These four sessions will introduce students to comparative historical research methods, emphasizing their qualitative dimensions. In the first session, we will analyze some contemporary classics within this genre. In the second and third sessions, we will review and distinguish among a variety of intellectual justifications for this genre as a methodology. In the final session, we will focus on a "state of the art" defence of qualitative and comparative-historical research, both in theory and practice. Aims:
Topics:
|
Wed 18 |
This course will introduce students to the general philosophical debates concerning scientific methodology, assessing their ramifications for the conduct of qualitative social research. It will enable students to critically evaluate major programmes in the philosophy of sciences, considering whether there are important analytic differences between the social and natural sciences; and whether qualitative methods themselves comprise a unified approach to the study of social reality. Topics:
|
Mon 23 |
This is an introductory course for students who have little or no prior training in statistics. The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to analyze real data using the statistical package Stata. You will learn:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
This is an introductory course for students who have little or no prior training in statistics. The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to analyze real data using the statistical package Stata. You will learn:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
|
This is an introductory course for students who have little or no prior training in statistics. The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to analyze real data using the statistical package Stata. You will learn:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
|
This is an introductory course for students who have little or no prior training in statistics. The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to analyze real data using the statistical package Stata. You will learn:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
|
Reading and Understanding Statistics
Finished
This module is for students who don’t plan to use quantitative methods in their own research, but who need to be able to read and understand published research using quantitative methods. You will learn how to interpret graphs, frequency tables and multivariate regression results, and to ask intelligent questions about sampling, methods and statistical inference. The module is aimed at complete beginners, with no prior knowledge of statistics or quantitative methods. |
|
Tue 24 |
Mixed Methods
Finished
Neither quantitative nor qualitative data analysis has all the answers in social science research: qualitative research has depth and nuance but is not generalisable beyond the sample on which it is based, while quantitative research is generalisable but may lack depth. A mixed methods approach, which uses evidence from both qualitative and quantitative approaches to shed light on a single research question, has the potential to gain the advantages of both approaches. However, genuine mixed methods work is not always easy. This short course will introduce students to the rationale behind the use of mixed methods approaches, and how to design mixed methods projects for best results. |
Comparative Historical Methods
Finished
These four sessions will introduce students to comparative historical research methods, emphasizing their qualitative dimensions. In the first session, we will analyze some contemporary classics within this genre. In the second and third sessions, we will review and distinguish among a variety of intellectual justifications for this genre as a methodology. In the final session, we will focus on a "state of the art" defence of qualitative and comparative-historical research, both in theory and practice. Aims:
Topics:
|
|
Wed 25 |
This is an introductory course for students who have little or no prior training in statistics. The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to analyze real data using the statistical package Stata. You will learn:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
This is an introductory course for students who have little or no prior training in statistics. The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to analyze real data using the statistical package Stata. You will learn:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
|
This is an introductory course for students who have little or no prior training in statistics. The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to analyze real data using the statistical package Stata. You will learn:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
|
Psychometrics
Finished
An introduction to the design, validation and implementation of tests and questionnaires in social science research, using both Classical Test Theory (CTT) and modern psychometric methods such as Item Response Theory (IRT). This course aims to enable students to: be able to construct and validate a test or questionnaire; understand the strengths, weaknesses and limitations of existing tests and questionnaires; appreciate the impact and potential of modern psychometric methods in the internet age. Week 1: Introduction to psychometrics Week 2: Testing in the online environment Week 3: Modern Psychometrics Week 4: Implementing adaptive tests online |
|
This is an introductory course for students who have little or no prior training in statistics. The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to analyze real data using the statistical package Stata. You will learn:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
|
Mon 30 |
This is an introductory course for students who have little or no prior training in statistics. The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to analyze real data using the statistical package Stata. You will learn:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
This is an introductory course for students who have little or no prior training in statistics. The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to analyze real data using the statistical package Stata. You will learn:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
|
This is an introductory course for students who have little or no prior training in statistics. The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to analyze real data using the statistical package Stata. You will learn:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
|
This is an introductory course for students who have little or no prior training in statistics. The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to analyze real data using the statistical package Stata. You will learn:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
|
Reading and Understanding Statistics
Finished
This module is for students who don’t plan to use quantitative methods in their own research, but who need to be able to read and understand published research using quantitative methods. You will learn how to interpret graphs, frequency tables and multivariate regression results, and to ask intelligent questions about sampling, methods and statistical inference. The module is aimed at complete beginners, with no prior knowledge of statistics or quantitative methods. |
|
Tue 31 |
Comparative Historical Methods
Finished
These four sessions will introduce students to comparative historical research methods, emphasizing their qualitative dimensions. In the first session, we will analyze some contemporary classics within this genre. In the second and third sessions, we will review and distinguish among a variety of intellectual justifications for this genre as a methodology. In the final session, we will focus on a "state of the art" defence of qualitative and comparative-historical research, both in theory and practice. Aims:
Topics:
|
November 2017
Wed 1 |
This is an introductory course for students who have little or no prior training in statistics. The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to analyze real data using the statistical package Stata. You will learn:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
This is an introductory course for students who have little or no prior training in statistics. The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to analyze real data using the statistical package Stata. You will learn:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
|
This is an introductory course for students who have little or no prior training in statistics. The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to analyze real data using the statistical package Stata. You will learn:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
|
Psychometrics
Finished
An introduction to the design, validation and implementation of tests and questionnaires in social science research, using both Classical Test Theory (CTT) and modern psychometric methods such as Item Response Theory (IRT). This course aims to enable students to: be able to construct and validate a test or questionnaire; understand the strengths, weaknesses and limitations of existing tests and questionnaires; appreciate the impact and potential of modern psychometric methods in the internet age. Week 1: Introduction to psychometrics Week 2: Testing in the online environment Week 3: Modern Psychometrics Week 4: Implementing adaptive tests online |
|
This is an introductory course for students who have little or no prior training in statistics. The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to analyze real data using the statistical package Stata. You will learn:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
|
Mon 6 |
Researching Organisations
Finished
This course provides an introduction to some of the methodological issues involved in researching organisations. Drawing on examples of studies carried out in a wide range of different types of organisation, the aim will be to explore practical strategies to overcome some of problems that are typically encountered in undertaking such studies. Topics covered include:
|
Basic Quantitative Analysis (BQA-2)
Finished
This module follows on from Foundations in Applied statistics, and will teach you the basics of common bivariate techniques (that is, techniques that examine the associations between two variables). The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to apply these techniques to the analysis of real data. Techniques to be covered include:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
|
Basic Quantitative Analysis (BQA-1)
Finished
This module follows on from Foundations in Applied statistics, and will teach you the basics of common bivariate techniques (that is, techniques that examine the associations between two variables). The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to apply these techniques to the analysis of real data. Techniques to be covered include:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
|
Basic Quantitative Analysis (BQA-1)
Finished
This module follows on from Foundations in Applied statistics, and will teach you the basics of common bivariate techniques (that is, techniques that examine the associations between two variables). The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to apply these techniques to the analysis of real data. Techniques to be covered include:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
|
Reading and Understanding Statistics
Finished
This module is for students who don’t plan to use quantitative methods in their own research, but who need to be able to read and understand published research using quantitative methods. You will learn how to interpret graphs, frequency tables and multivariate regression results, and to ask intelligent questions about sampling, methods and statistical inference. The module is aimed at complete beginners, with no prior knowledge of statistics or quantitative methods. |
|
Basic Quantitative Analysis (BQA-2)
Finished
This module follows on from Foundations in Applied statistics, and will teach you the basics of common bivariate techniques (that is, techniques that examine the associations between two variables). The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to apply these techniques to the analysis of real data. Techniques to be covered include:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
|
Tue 7 |
Introduction to Stata (Michaelmas)
Finished
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:
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. |
Wed 8 |
Basic Quantitative Analysis (BQA-3)
Finished
This module follows on from Foundations in Applied statistics, and will teach you the basics of common bivariate techniques (that is, techniques that examine the associations between two variables). The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to apply these techniques to the analysis of real data. Techniques to be covered include:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
Basic Quantitative Analysis (BQA-4)
Finished
This module follows on from Foundations in Applied statistics, and will teach you the basics of common bivariate techniques (that is, techniques that examine the associations between two variables). The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to apply these techniques to the analysis of real data. Techniques to be covered include:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
|
Basic Quantitative Analysis (BQA-3)
Finished
This module follows on from Foundations in Applied statistics, and will teach you the basics of common bivariate techniques (that is, techniques that examine the associations between two variables). The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to apply these techniques to the analysis of real data. Techniques to be covered include:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
|
Psychometrics
Finished
An introduction to the design, validation and implementation of tests and questionnaires in social science research, using both Classical Test Theory (CTT) and modern psychometric methods such as Item Response Theory (IRT). This course aims to enable students to: be able to construct and validate a test or questionnaire; understand the strengths, weaknesses and limitations of existing tests and questionnaires; appreciate the impact and potential of modern psychometric methods in the internet age. Week 1: Introduction to psychometrics Week 2: Testing in the online environment Week 3: Modern Psychometrics Week 4: Implementing adaptive tests online |
|
Basic Quantitative Analysis (BQA-4)
Finished
This module follows on from Foundations in Applied statistics, and will teach you the basics of common bivariate techniques (that is, techniques that examine the associations between two variables). The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to apply these techniques to the analysis of real data. Techniques to be covered include:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
|
Mon 13 |
Researching Organisations
Finished
This course provides an introduction to some of the methodological issues involved in researching organisations. Drawing on examples of studies carried out in a wide range of different types of organisation, the aim will be to explore practical strategies to overcome some of problems that are typically encountered in undertaking such studies. Topics covered include:
|
Basic Quantitative Analysis (BQA-2)
Finished
This module follows on from Foundations in Applied statistics, and will teach you the basics of common bivariate techniques (that is, techniques that examine the associations between two variables). The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to apply these techniques to the analysis of real data. Techniques to be covered include:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
|
Basic Quantitative Analysis (BQA-1)
Finished
This module follows on from Foundations in Applied statistics, and will teach you the basics of common bivariate techniques (that is, techniques that examine the associations between two variables). The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to apply these techniques to the analysis of real data. Techniques to be covered include:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
|
Basic Quantitative Analysis (BQA-1)
Finished
This module follows on from Foundations in Applied statistics, and will teach you the basics of common bivariate techniques (that is, techniques that examine the associations between two variables). The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to apply these techniques to the analysis of real data. Techniques to be covered include:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
|
Reading and Understanding Statistics
Finished
This module is for students who don’t plan to use quantitative methods in their own research, but who need to be able to read and understand published research using quantitative methods. You will learn how to interpret graphs, frequency tables and multivariate regression results, and to ask intelligent questions about sampling, methods and statistical inference. The module is aimed at complete beginners, with no prior knowledge of statistics or quantitative methods. |
|
Basic Quantitative Analysis (BQA-2)
Finished
This module follows on from Foundations in Applied statistics, and will teach you the basics of common bivariate techniques (that is, techniques that examine the associations between two variables). The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to apply these techniques to the analysis of real data. Techniques to be covered include:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
|
Tue 14 |
Introduction to Stata (Michaelmas)
Finished
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:
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. |
Wed 15 |
Basic Quantitative Analysis (BQA-3)
Finished
This module follows on from Foundations in Applied statistics, and will teach you the basics of common bivariate techniques (that is, techniques that examine the associations between two variables). The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to apply these techniques to the analysis of real data. Techniques to be covered include:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
Basic Quantitative Analysis (BQA-4)
Finished
This module follows on from Foundations in Applied statistics, and will teach you the basics of common bivariate techniques (that is, techniques that examine the associations between two variables). The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to apply these techniques to the analysis of real data. Techniques to be covered include:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
|
Basic Quantitative Analysis (BQA-3)
Finished
This module follows on from Foundations in Applied statistics, and will teach you the basics of common bivariate techniques (that is, techniques that examine the associations between two variables). The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to apply these techniques to the analysis of real data. Techniques to be covered include:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
|
Psychometrics
Finished
An introduction to the design, validation and implementation of tests and questionnaires in social science research, using both Classical Test Theory (CTT) and modern psychometric methods such as Item Response Theory (IRT). This course aims to enable students to: be able to construct and validate a test or questionnaire; understand the strengths, weaknesses and limitations of existing tests and questionnaires; appreciate the impact and potential of modern psychometric methods in the internet age. Week 1: Introduction to psychometrics Week 2: Testing in the online environment Week 3: Modern Psychometrics Week 4: Implementing adaptive tests online |
|
Basic Quantitative Analysis (BQA-4)
Finished
This module follows on from Foundations in Applied statistics, and will teach you the basics of common bivariate techniques (that is, techniques that examine the associations between two variables). The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to apply these techniques to the analysis of real data. Techniques to be covered include:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
|
Mon 20 |
Researching Organisations
Finished
This course provides an introduction to some of the methodological issues involved in researching organisations. Drawing on examples of studies carried out in a wide range of different types of organisation, the aim will be to explore practical strategies to overcome some of problems that are typically encountered in undertaking such studies. Topics covered include:
|
Doing Multivariate Analysis (DMA-1)
Finished
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. |
|
Doing Multivariate Analysis (DMA-1)
Finished
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. |
|
Power Analysis
Finished
This two-hour short course will introduce students to the concept of power analysis (also known as power calculations), providing an easy and intuitive rationale behind the technique, as well as hands-on practice in how to perform power analysis in Stata. Power analysis is an important skill for anyone doing statistical research; it is particularly useful when writing a grant proposal, and is sometimes required by funders. It involves calculating the number of observations required to undertake a given statistical analysis. If a sample is too small, significant associations may not be detectable, even though they may be present in the population from which the sample is drawn. Power analysis is useful when:
|
|
Tue 21 |
These two sessions will provide a basic introduction to the management and analysis of relational databases, using Microsoft Access and a set of historical datasets. The workshops will introduce participants to the following:
|
Wed 22 |
Doing Multivariate Analysis (DMA-3)
Finished
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. |
Doing Multivariate Analysis (DMA-2)
Finished
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. |
|
Doing Multivariate Analysis (DMA-2)
Finished
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. |
|
Doing Multivariate Analysis (DMA-3)
Finished
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. |
|
Mon 27 |
Doing Multivariate Analysis (DMA-1)
Finished
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. |
Doing Multivariate Analysis (DMA-1)
Finished
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. |
|
Workshop: Using Your Own Data
POSTPONED
All the SSRMC's statistics courses are hands-on: you'll learn how to analyse real data, using state-of-the-art statistical analysis packages. But sometimes things aren't so straightforward when it comes to using your own data: the data may not be in Stata format; it may be a funny "shape"; there may be no variable or value labels; or it may be very dirty. If you have completed your basic stats training and need a helping hand getting started with your own data, this workshop will help you to:
The workshop, based in a computer lab, is entirely devoted to helping students get started with their own data - there is no lecture component. You will need to bring your own data along. |
|
Tue 28 |
These two sessions will provide a basic introduction to the management and analysis of relational databases, using Microsoft Access and a set of historical datasets. The workshops will introduce participants to the following:
|
Wed 29 |
Doing Multivariate Analysis (DMA-3)
Finished
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. |
Doing Multivariate Analysis (DMA-2)
Finished
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. |
|
Doing Multivariate Analysis (DMA-2)
Finished
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. |
|
Doing Multivariate Analysis (DMA-3)
Finished
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. |