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Social Sciences Research Methods Programme course timetable

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Tue 3 Mar – Tue 13 Oct

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Tuesday 3 March

14:00
Survey Research and Design (3 of 3) Finished 14:00 - 17:00 University Information Services, Titan Teaching Room 2, New Museums Site

The module aims to provide students with an introduction to and overview of survey methods and its uses and limitations. It will introduce students both to some of the main theoretical issues involved in survey research (such as survey sampling, non-response and question wording) and to practicalities of the design and analysis of surveys. The module consists of three three-hour sessions, split between lectures and practical exercises.

At the start of the module, the theoretical aspects of designing surveys will feature more, and topics covered include: the background to and history of survey research (with examples mostly drawn from political polling); an overview of the issues involved in analysing data from surveys conducted by others and some practical advice on how to evaluate such data; issues of sampling, non-response and different ways of doing surveys; issues related to questionnaire design (question wording, answer options, etc.) and ethical considerations. These lectures are relevant for all students taking the module, irrespective of whether they will conduct surveys themselves or are 'passive' users of survey results.

As the module progresses the practical aspects of designing surveys will feature more, particularly issues directly related to questionnaires (and less on issues of sampling), such as the wording of questions, the order of questions, and the use of different answer options. Most of the exercises will be provided by the instructors, but there will also be opportunities for students to bring in examples of surveys they would like to develop for their own research (and participants in the sessions may be asked to answer each other's surveys as a pilot test). We encourage all students registered for the module to attend the more practical sessions, but it will be of most direct relevance to those who are using, or plan to use, surveys in their research.

Wednesday 4 March

12:00
A Critical Analysis of Null Hypothesis Testing and its Alternatives (Including Bayesian Analysis) (1 of 2) Finished 12:00 - 17:00 Department of Psychology, Psychology Lecture Theatre

This course will provide a detailed critique of the methods and philosophy of the Null Hypothesis Significance Testing (NHST) approach to statistics which is currently dominant in social and biomedical science. We will briefly contrast NHST with alternatives, especially with Bayesian methods. We will use some computer code (Matlab and R) to demonstrate some issues. However, we will focus on the big picture rather on the implementation of specific procedures.

14:00
Causal Inference in the Social Sciences Finished 14:00 - 16:00 8 Mill Lane, Lecture Room 6

The challenge of causal inference is ubiquitous in social science. Nearly every research project fundamentally is about causes and effects.

This introductory session will:

  • 1. Introduce three main approaches to elucidate causal relationship: structural equation models, causal directed acyclic graphs, and the counterfactual/potential outcome framework;
  • 2. Explain the common challenges in empirical research;
  • 3. Talk through some principles and intuition of several research designs that can help researchers make stronger claims for causality.

The emphasis is on setting out applications of each approach, along with pros and cons, so that participants understand when a particular design may be more or less suitable to a research problem.

Monday 9 March

09:00
Meta Analysis (1 of 2) Finished 09:00 - 13:00 University Information Services, Titan Teaching Room 2, New Museums Site

In this module students will be introduced to meta-analysis, a powerful statistical technique allowing researchers to synthesize the available evidence for a given research question using standardized (comparable) effect sizes across studies. The sessions teach students how to compute treatment effects, how to compute effect sizes based on correlational studies, how to address questions such as what is the association of bullying victimization with depression? The module will be useful for students who seek to draw statistical conclusions in a standardized manner from literature reviews they are conducting.

11:00
Factor Analysis (3 of 6) Finished 11:00 - 13:00 8 Mill Lane, Lecture Room 4

This module introduces the statistical techniques of Exploratory and Confirmatory Factor Analyses. Exploratory Factor Analysis (EFA) is used to uncover the latent structure (dimensions) of a set of variables. It reduces the attribute space from a larger number of variables to a smaller number of factors. Confirmatory Factor Analysis (CFA) examines whether collected data correspond to a model of what the data are meant to measure. STATA will be introduced as a powerful tool to conduct confirmatory factor analysis. A brief introduction will be given to confirmatory factor analysis and structural equation modelling.

  • Session 1: Exploratory Factor Analysis Introduction
  • Session 2: Factor Analysis Applications
  • Session 3: CFA and Path Analysis with STATA
  • Session 4: Introduction to SEM and programming
14:00
Public Policy Analysis (3 of 3) Finished 14:00 - 16:00 8 Mill Lane, Lecture Room 3

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

Meta Analysis (2 of 2) Finished 14:00 - 18:00 University Information Services, Titan Teaching Room 2, New Museums Site

In this module students will be introduced to meta-analysis, a powerful statistical technique allowing researchers to synthesize the available evidence for a given research question using standardized (comparable) effect sizes across studies. The sessions teach students how to compute treatment effects, how to compute effect sizes based on correlational studies, how to address questions such as what is the association of bullying victimization with depression? The module will be useful for students who seek to draw statistical conclusions in a standardized manner from literature reviews they are conducting.

Factor Analysis (4 of 6) Finished 14:00 - 16:00 University Information Services, Titan Teaching Room 1, New Museums Site

This module introduces the statistical techniques of Exploratory and Confirmatory Factor Analyses. Exploratory Factor Analysis (EFA) is used to uncover the latent structure (dimensions) of a set of variables. It reduces the attribute space from a larger number of variables to a smaller number of factors. Confirmatory Factor Analysis (CFA) examines whether collected data correspond to a model of what the data are meant to measure. STATA will be introduced as a powerful tool to conduct confirmatory factor analysis. A brief introduction will be given to confirmatory factor analysis and structural equation modelling.

  • Session 1: Exploratory Factor Analysis Introduction
  • Session 2: Factor Analysis Applications
  • Session 3: CFA and Path Analysis with STATA
  • Session 4: Introduction to SEM and programming

Tuesday 10 March

14:00
Secondary Data Analysis Finished 14:00 - 18:00 University Information Services, Titan Teaching Room 1, New Museums Site

Using secondary data (that is, data collected by someone else, usually a government agency or large research organisation) has a number of advantages in social science research: sample sizes are usually larger than can be achieved by primary data collection, samples are more nearly representative of the populations they are drawn from, and using secondary data for a research project often represents significant savings in time and money. This short course, taught by Dr Deborah Wiltshire of the UK Data Archive, will discuss the advantages and limitations of using secondary data for research in the social sciences, and will introduce students to the wide range of available secondary data sources. The course is based in a computer lab; students will learn how to search online for suitable secondary data by browsing the database of the UK Data Archive.

Wednesday 11 March

09:30
Multilevel Modelling (1 of 2) Finished 09:30 - 13:00 8 Mill Lane, Lecture Room 6

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

  • Introduction to Stata/MLM theory
  • Applications I - Random intercept models
  • Applications II - Random slope models
  • Applications III - Revision session/growth models
12:00
A Critical Analysis of Null Hypothesis Testing and its Alternatives (Including Bayesian Analysis) (2 of 2) Finished 12:00 - 17:00 Department of Psychology, Psychology Lecture Theatre

This course will provide a detailed critique of the methods and philosophy of the Null Hypothesis Significance Testing (NHST) approach to statistics which is currently dominant in social and biomedical science. We will briefly contrast NHST with alternatives, especially with Bayesian methods. We will use some computer code (Matlab and R) to demonstrate some issues. However, we will focus on the big picture rather on the implementation of specific procedures.

14:00
Multilevel Modelling (2 of 2) Finished 14:00 - 18:00 University Information Services, Titan Teaching Room 1, New Museums Site

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

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

Monday 16 March

10:00
Evaluation Methods (1 of 4) CANCELLED 10:00 - 12:45 8 Mill Lane, Lecture Room 6

This course aims to provide students with a range of specific technical skills that will enable them to undertake impact evaluation of policy. Too often policy is implemented but not fully evaluated. Without evaluation we cannot then tell what the short or longer term impact of a particular policy has been. On this course, students will learn the skills needed to evaluate particular policies and will have the opportunity to do some hands on data manipulation. A particular feature of this course is that it provides these skills in a real world context of policy evaluation. It also focuses primarily not on experimental evaluation (Random Control Trials) but rather quasi-experimental methodologies that can be used where an experiment is not desirable or feasible.

Topics:

  • Regression-based techniques
  • Evaluation framework and concepts
  • The limitations of regression based approaches and RCTs
  • Before/After, Difference in Difference (DID) methods
  • Computer exercise on difference in difference methods
  • Instrumental variables techniques
  • Regression discontinuity design.
13:45
Evaluation Methods (2 of 4) CANCELLED 13:45 - 17:00 University Information Services, Titan Teaching Room 1, New Museums Site

This course aims to provide students with a range of specific technical skills that will enable them to undertake impact evaluation of policy. Too often policy is implemented but not fully evaluated. Without evaluation we cannot then tell what the short or longer term impact of a particular policy has been. On this course, students will learn the skills needed to evaluate particular policies and will have the opportunity to do some hands on data manipulation. A particular feature of this course is that it provides these skills in a real world context of policy evaluation. It also focuses primarily not on experimental evaluation (Random Control Trials) but rather quasi-experimental methodologies that can be used where an experiment is not desirable or feasible.

Topics:

  • Regression-based techniques
  • Evaluation framework and concepts
  • The limitations of regression based approaches and RCTs
  • Before/After, Difference in Difference (DID) methods
  • Computer exercise on difference in difference methods
  • Instrumental variables techniques
  • Regression discontinuity design.

Tuesday 17 March

10:00
Evaluation Methods (3 of 4) CANCELLED 10:00 - 12:45 8 Mill Lane, Lecture Room 6

This course aims to provide students with a range of specific technical skills that will enable them to undertake impact evaluation of policy. Too often policy is implemented but not fully evaluated. Without evaluation we cannot then tell what the short or longer term impact of a particular policy has been. On this course, students will learn the skills needed to evaluate particular policies and will have the opportunity to do some hands on data manipulation. A particular feature of this course is that it provides these skills in a real world context of policy evaluation. It also focuses primarily not on experimental evaluation (Random Control Trials) but rather quasi-experimental methodologies that can be used where an experiment is not desirable or feasible.

Topics:

  • Regression-based techniques
  • Evaluation framework and concepts
  • The limitations of regression based approaches and RCTs
  • Before/After, Difference in Difference (DID) methods
  • Computer exercise on difference in difference methods
  • Instrumental variables techniques
  • Regression discontinuity design.
13:30
Evaluation Methods (4 of 4) CANCELLED 13:30 - 16:00 University Information Services, Titan Teaching Room 1, New Museums Site

This course aims to provide students with a range of specific technical skills that will enable them to undertake impact evaluation of policy. Too often policy is implemented but not fully evaluated. Without evaluation we cannot then tell what the short or longer term impact of a particular policy has been. On this course, students will learn the skills needed to evaluate particular policies and will have the opportunity to do some hands on data manipulation. A particular feature of this course is that it provides these skills in a real world context of policy evaluation. It also focuses primarily not on experimental evaluation (Random Control Trials) but rather quasi-experimental methodologies that can be used where an experiment is not desirable or feasible.

Topics:

  • Regression-based techniques
  • Evaluation framework and concepts
  • The limitations of regression based approaches and RCTs
  • Before/After, Difference in Difference (DID) methods
  • Computer exercise on difference in difference methods
  • Instrumental variables techniques
  • Regression discontinuity design.

Wednesday 18 March

11:00
Factor Analysis (5 of 6) Finished 11:00 - 13:00 8 Mill Lane, Lecture Room 2

This module introduces the statistical techniques of Exploratory and Confirmatory Factor Analyses. Exploratory Factor Analysis (EFA) is used to uncover the latent structure (dimensions) of a set of variables. It reduces the attribute space from a larger number of variables to a smaller number of factors. Confirmatory Factor Analysis (CFA) examines whether collected data correspond to a model of what the data are meant to measure. STATA will be introduced as a powerful tool to conduct confirmatory factor analysis. A brief introduction will be given to confirmatory factor analysis and structural equation modelling.

  • Session 1: Exploratory Factor Analysis Introduction
  • Session 2: Factor Analysis Applications
  • Session 3: CFA and Path Analysis with STATA
  • Session 4: Introduction to SEM and programming
14:00
Factor Analysis (6 of 6) Finished 14:00 - 16:00 University Information Services, Titan Teaching Room 2, New Museums Site

This module introduces the statistical techniques of Exploratory and Confirmatory Factor Analyses. Exploratory Factor Analysis (EFA) is used to uncover the latent structure (dimensions) of a set of variables. It reduces the attribute space from a larger number of variables to a smaller number of factors. Confirmatory Factor Analysis (CFA) examines whether collected data correspond to a model of what the data are meant to measure. STATA will be introduced as a powerful tool to conduct confirmatory factor analysis. A brief introduction will be given to confirmatory factor analysis and structural equation modelling.

  • Session 1: Exploratory Factor Analysis Introduction
  • Session 2: Factor Analysis Applications
  • Session 3: CFA and Path Analysis with STATA
  • Session 4: Introduction to SEM and programming

Wednesday 8 April

09:00
Introduction to Python new (1 of 2) CANCELLED 09:00 - 14:00 SSRMP Zoom

This module introduces the use of Python, a free programming language originally developed for statistical data analysis. Students will learn:

  • Ways of reading data into Python
  • How to manipulate data in major data types
  • How to draw basic graphs and figures with Python
  • How to summarise data using descriptive statistics
  • How to perform basic inferential statistics


This module is suitable for students who have no prior experience in programming, but participants will be assumed to have a good working knowledge of basic statistical techniques.

16:00
Introduction to Python new (2 of 2) CANCELLED 16:00 - 17:00 SSRMP Zoom

This module introduces the use of Python, a free programming language originally developed for statistical data analysis. Students will learn:

  • Ways of reading data into Python
  • How to manipulate data in major data types
  • How to draw basic graphs and figures with Python
  • How to summarise data using descriptive statistics
  • How to perform basic inferential statistics


This module is suitable for students who have no prior experience in programming, but participants will be assumed to have a good working knowledge of basic statistical techniques.

Monday 12 October

10:00
Practical introduction to MATLAB Programming (1 of 4) Finished 10:00 - 12:00 Taught Online

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 SSRMP 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

More information on the course can be found here

14:00
Practical introduction to MATLAB Programming (2 of 4) Finished 14:00 - 16:00 Taught Online

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 SSRMP 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

More information on the course can be found here

Introduction to Empirical Research Finished 14:00 - 16:00 SSRMP Zoom

This module is for anyone considering studying on an SSRMP module but not sure which one/s to choose. It provides an overview of the research process and issues in research design. Through reflection on a broad overview of empirical research, the module aims to encourage students to consider where they may wish to develop their research skills and knowledge. The module will signpost the different modules, both quantitative and qualitative, offered by SSRMP and encourage students to consider what modules might be appropriate for their research and career development.

You will learn:

  • The research process and the different stages it might consist of
  • Issues related to research design
  • To consider what data you will need to address your research aims
  • To consider the best methods to collect and analyse your data
  • What modules are offered by SSRMP and how they might be appropriate to your needs

Tuesday 13 October

10:00
Practical introduction to MATLAB Programming (3 of 4) Finished 10:00 - 12:00 Taught Online

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 SSRMP 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

More information on the course can be found here

14:00
Practical introduction to MATLAB Programming (4 of 4) Finished 14:00 - 16:00 Taught Online

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 SSRMP 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

More information on the course can be found here