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Social Sciences Research Methods Centre course timetable

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Wed 22 Nov – Mon 29 Jan 2018

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Monday 27 November 2017

10:00
Doing Multivariate Analysis (DMA-1) (3 of 4) Not bookable 10:00 - 12:00 8 Mill Lane, Lecture Room 1

This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself, and to interpret and write about your results intelligently.

Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software.

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

14:00
Doing Multivariate Analysis (DMA-1) (4 of 4) Not bookable 14:00 - 16:00 University Information Services, Titan Teaching Room 1, New Museums Site

This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself, and to interpret and write about your results intelligently.

Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software.

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

16:00
Workshop: Using Your Own Data [Places] 16:00 - 18:00 University Information Services, Titan Teaching Room 1, New Museums Site

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:

  • Read your own data into Stata
  • Label your variables and values
  • Deal with dirty data

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.

Tuesday 28 November 2017

14:00
Microsoft Access: Database Design and Use (2 of 2) In progress 14:00 - 18:00 University Information Services, Titan Teaching Room 1, New Museums Site

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:

  • The use of Access’s menus and tool bars
  • Viewing and browsing data tables
  • Creating quick forms formulating queries
  • Developing queries using Boolean operators
  • Performing simple statistical operations
  • Linking tables and working with linked tables
  • Querying multiple tables
  • Data transformation.

Wednesday 29 November 2017

10:00
Doing Multivariate Analysis (DMA-3) (3 of 4) Not bookable 10:00 - 12:00 8 Mill Lane, Lecture Room 4

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) (3 of 4) Not bookable 10:00 - 12:00 8 Mill Lane, Lecture Room 4

This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself, and to interpret and write about your results intelligently.

Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software.

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

14:00
Doing Multivariate Analysis (DMA-2) (4 of 4) Not bookable 14:00 - 16:00 University Information Services, Titan Teaching Room 1, New Museums Site

This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself, and to interpret and write about your results intelligently.

Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software.

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

16:00
Doing Multivariate Analysis (DMA-3) (4 of 4) Not bookable 16:00 - 18:00 University Information Services, Titan Teaching Room 1, New Museums Site

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.

Tuesday 16 January 2018

14:00
Introduction to R (Lent) (1 of 2) [Full] 14:00 - 18:00 University Information Services, Titan Teaching Room 1, New Museums Site

This module introduces the use of R, a free programming language originally developed for statistical data analysis. In this course, we will use R through R Studio, a user-friendly interface.

Students will learn:

  • Ways of reading spreadsheet data into R
  • The notion of data type
  • How to manipulate data in major data types
  • How to draw basic graphs and figures with ggplot2
  • How to summarise data using descriptive statistics
  • How to perform basic inferential statistics (e.g. the t-test).

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 using another software package (for example Stata or SPSS).

Experimental Methods (1 of 2) [Full] 14:00 - 16:00 Faculty of Music, CMS Computer Room

This course will constitute a practical introduction to experimental method and design suitable for students from any discipline who have had limited experience of empirical methods but who wish to be able to read and understand the experimental literature or to undertake their own experimental studies. The course includes:

  • A theoretical introduction to the concepts and practices involved in experimental research in the human sciences, including ethical considerations;
  • An introduction to experimental design and to appropriate analytic techniques;
  • A practical component that can be undertaken away from the laboratory; and
  • An introduction to issues involved in writing up results.

At the end of the module, students will be equipped with the fundamental knowledge required to design and evaluate an experiment.

Wednesday 17 January 2018

09:00
Foundations in Applied Statistics (FiAS Intensive) (1 of 2) Not bookable 09:00 - 13:00 8 Mill Lane, Lecture Room 5

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:

  • The key features of quantitative analysis, and how it differs from other types of empirical analysis
  • Basic concepts: what is a variable? what is the distribution of a variable? and how can we best represent a distribution graphically?
  • Features of statistical distributions: measures of central tendency and dispersion
  • The normal distribution
  • The basics of formal hypothesis testing
  • Why statistical testing works
  • Statistical methods used to test simple hypotheses
  • How to use Stata
14:00
Foundations in Applied Statistics (FiAS Intensive) (2 of 2) Not bookable 14:00 - 18:00 University Information Services, Titan Teaching Room 1, New Museums Site

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:

  • The key features of quantitative analysis, and how it differs from other types of empirical analysis
  • Basic concepts: what is a variable? what is the distribution of a variable? and how can we best represent a distribution graphically?
  • Features of statistical distributions: measures of central tendency and dispersion
  • The normal distribution
  • The basics of formal hypothesis testing
  • Why statistical testing works
  • Statistical methods used to test simple hypotheses
  • How to use Stata
Experimental Methods (2 of 2) [Full] 14:00 - 16:00 Faculty of Music, CMS Computer Room

This course will constitute a practical introduction to experimental method and design suitable for students from any discipline who have had limited experience of empirical methods but who wish to be able to read and understand the experimental literature or to undertake their own experimental studies. The course includes:

  • A theoretical introduction to the concepts and practices involved in experimental research in the human sciences, including ethical considerations;
  • An introduction to experimental design and to appropriate analytic techniques;
  • A practical component that can be undertaken away from the laboratory; and
  • An introduction to issues involved in writing up results.

At the end of the module, students will be equipped with the fundamental knowledge required to design and evaluate an experiment.

Monday 22 January 2018

09:00
Basic Quantitative Analysis (BQA Intensive) (1 of 2) Not bookable 09:00 - 13:00 8 Mill Lane, Lecture Room 5

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:

  • Cross-tabulations
  • Scatterplots
  • Covariance and correlation
  • Nonparametric methods
  • Two-sample t-tests
  • ANOVA
  • Ordinary Least Squares (OLS)

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.

14:00
Basic Quantitative Analysis (BQA Intensive) (2 of 2) Not bookable 14:00 - 18:00 University Information Services, Titan Teaching Room 1, New Museums Site

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:

  • Cross-tabulations
  • Scatterplots
  • Covariance and correlation
  • Nonparametric methods
  • Two-sample t-tests
  • ANOVA
  • Ordinary Least Squares (OLS)

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.

Tuesday 23 January 2018

14:00
Doing Qualitative Interviews (1 of 3) [Places] 14:00 - 16:00 8 Mill Lane, Lecture Room 1

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.

Topics:

  • Session 1: Conducting qualitative interviews
  • Session 2: Ethics and practical constraints
  • Session 3: Interpretation and analysis
  • Session 4: Practical: developing your own material
Introduction to R (Lent) (2 of 2) [Full] 14:00 - 18:00 University Information Services, Titan Teaching Room 1, New Museums Site

This module introduces the use of R, a free programming language originally developed for statistical data analysis. In this course, we will use R through R Studio, a user-friendly interface.

Students will learn:

  • Ways of reading spreadsheet data into R
  • The notion of data type
  • How to manipulate data in major data types
  • How to draw basic graphs and figures with ggplot2
  • How to summarise data using descriptive statistics
  • How to perform basic inferential statistics (e.g. the t-test).

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 using another software package (for example Stata or SPSS).

16:00
Conversation and Discourse Analysis (1 of 4) [Full] 16:00 - 17:30 8 Mill Lane, Lecture Room 6

The module will introduce students to the study of language use as a distinctive type of social practice. Attention will be focused primarily on the methodological and analytic principles of conversation analysis. (CA). However, it will explore the debates between CA and Critical Discourse Analysis (CDA), as a means of addressing the relationship between the study of language use and the study of other aspects of social life. It will also consider the roots of conversation analysis in the research initiatives of ethnomethodology, and the analysis of ordinary and institutional talk. It will finally consider the interface between CA and CDA.

Topics:

  • Session 1: The Roots of Conversation Analysis
  • Session 2: Ordinary Talk
  • Session 3: Institutional Talk
  • Session 4: Conversation Analysis and Critical Discourse Analysis

Wednesday 24 January 2018

09:00
Doing Multivariate Analysis (DMA Intensive) (1 of 2) Not bookable 09:00 - 13:00 8 Mill Lane, Lecture Room 5

This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself , and to interpret and write about your results intelligently.

Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software.

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

14:00
Doing Multivariate Analysis (DMA Intensive) (2 of 2) Not bookable 14:00 - 18:00 University Information Services, Titan Teaching Room 1, New Museums Site

This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself , and to interpret and write about your results intelligently.

Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software.

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

14:30
Research Ethics (Lent) [Full] 14:30 - 17:30 Institute of Criminology, Room B3

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 some small-group work.

Monday 29 January 2018

10:00
Further Topics in Multivariate Analysis (FTMA) (1 of 4) Not bookable 10:00 - 12:00 8 Mill Lane, Lecture Room 1

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

Topics covered include:

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

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

14:00
Further Topics in Multivariate Analysis (FTMA) (2 of 4) Not bookable 14:00 - 16:00 University Information Services, Titan Teaching Room 1, New Museums Site

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

Topics covered include:

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

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

16:00
Public Policy Analysis (1 of 3) [Places] 16:00 - 18:00 8 Mill Lane, Lecture Room 1

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

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

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

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

Stata and Data new [Places] 16:00 - 18:00 University Information Services, Titan Teaching Room 1, New Museums Site

This workshop will provide support for students who are working on their own projects, and who need a little extra help with their data analysis. Bring your data along to this session (bearing in mind considerations of data security) and our demonstrators will do their best to help you with:

  • Getting your data into shape
  • Writing and documenting syntax files
  • De-bugging syntax that doesn't work
  • Understanding your output
  • Your next steps, including choosing appropriate analytical techniques