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Social Sciences Research Methods Centre course timetable

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Tuesday 23 January

14:00
Doing Qualitative Interviews (1 of 3) [Places] 14:00 - 16:00 New Museums Site, Babbage Lecture Theatre

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:

1. Conducting qualitative interviews

2. Ethics and practical constraints

3. Practical session: interpretation and analysis

Introduction to R (Lent) (2 of 2) In progress 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) [Places] 16:00 - 17:30 Department of Genetics, Biffen Lecture Theatre

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

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) POSTPONED 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

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) POSTPONED 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

Tuesday 30 January

14:00
Doing Qualitative Interviews (2 of 3) [Places] 14:00 - 16:00 New Museums Site, Babbage Lecture Theatre

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:

1. Conducting qualitative interviews

2. Ethics and practical constraints

3. Practical session: interpretation and analysis

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

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:

  • How to open and manage a dataset in Stata
  • How to recode variables
  • How to select a sample for analysis
  • The commands needed to perform simple statistical analyses in Stata
  • Where to find additional resources to help you as you progress with Stata

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.

16:00
Conversation and Discourse Analysis (2 of 4) [Places] 16:00 - 17:30 Department of Genetics, Biffen Lecture Theatre

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 31 January

10:00
Further Topics in Multivariate Analysis (FTMA) (3 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) (4 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.

Monday 5 February

14:00
Survey Research and Design (1 of 4) [Places] 14:00 - 16:00 8 Mill Lane, Lecture Room 1

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 four two-hour sessions, each of which has two parts.

The first hour of each session will consist of a lecture. The four lectures cover: 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 aimed at all students taking the module, irrespective of whether they will conduct surveys themselves or are ‘passive’ users of survey results. Students who have attended these lectures will be able to evaluate research that uses surveys, in particular to understand issues concerning sample selection, response bias and data analysis; to appreciate and understand basic principles of questionnaire design; and to trace appropriate sources of data and appropriate exemplars of good survey practice.

The second hour of each session will be dedicated to practical exercises on how to design surveys. The focus will primarily be on 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 (and we may provide opportunities to field successful exercises as part of YouGov surveys), 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). These second parts of the sessions are aimed at students who are using, or plan to use, surveys in their research, although we welcome any students who would like to learn more about the practicalities of doing survey research.

Issues in Measurement: Validity and Reliability [Places] 14:00 - 16:00 8 Mill Lane, Lecture Room 10

This short two-hour course will provide an introduction to measurement issues in the social sciences. We design questions (or "survey instruments") to gain information on the concepts we are researching. Two prime considerations in whether an instrument is effective are validity (does our instrument actually measure what we want it to measure?) and reliability (does our instrument give consistent results across a range of different situations?)

Considerations of validity and reliability are important across many areas of social science, including the measurement of personality and mental health; attitudes; ability tests; political behaviour; cultural differences and similarities between various groups; and consumer behaviour.

The course will discuss what we mean by validity and reliability, the different ways we can think about the concepts, and different ways we can assess the quality of instruments using these criteria. We will also look at some statistical techniques for reliability and validity checks: Cronbach’s Alpha, Kappa coefficient, and Factor Analysis.

16:00
Public Policy Analysis (2 of 3) POSTPONED 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.

Tuesday 6 February

14:00
Doing Qualitative Interviews (3 of 3) [Places] 14:00 - 16:00 New Museums Site, Babbage Lecture Theatre

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:

1. Conducting qualitative interviews

2. Ethics and practical constraints

3. Practical session: interpretation and analysis

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

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:

  • How to open and manage a dataset in Stata
  • How to recode variables
  • How to select a sample for analysis
  • The commands needed to perform simple statistical analyses in Stata
  • Where to find additional resources to help you as you progress with Stata

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.

16:00
Conversation and Discourse Analysis (3 of 4) [Places] 16:00 - 17:30 Department of Genetics, Biffen Lecture Theatre

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 7 February

09:00
Time Series Analysis (Intensive) (1 of 2) [Full] 09:00 - 13:00 8 Mill Lane, Lecture Room 5

This module introduces the time series techniques relevant to forecasting in social science research and computer implementation of the methods. Background in basic statistical theory and regression methods is assumed. Topics covered include time series regression, Vector Error Correction and Vector Autoregressive Models, Time-varying Volatility, and ARCH models. The study of applied work is emphasized in this non-specialist module. Topics include:

  • Introduction to Time Series: Time series and cross-sectional data; Components of a time series, Forecasting methods overview; Measuring forecasting accuracy, Choosing a forecasting technique
  • Time Series Regression; Modelling linear and nonlinear trend; Detecting autocorrelation; Modelling seasonal variation by using dummy variables
  • Stationarity; Unit Root test; Cointegration
  • Vector Error Correlation and Vector Autoregressive models; Impulse responses and variance decompositions
  • Time-varying volatility and ARCH models; GARCH models
14:00
Time Series Analysis (Intensive) (2 of 2) [Full] 14:00 - 18:00 University Information Services, Titan Teaching Room 1, New Museums Site

This module introduces the time series techniques relevant to forecasting in social science research and computer implementation of the methods. Background in basic statistical theory and regression methods is assumed. Topics covered include time series regression, Vector Error Correction and Vector Autoregressive Models, Time-varying Volatility, and ARCH models. The study of applied work is emphasized in this non-specialist module. Topics include:

  • Introduction to Time Series: Time series and cross-sectional data; Components of a time series, Forecasting methods overview; Measuring forecasting accuracy, Choosing a forecasting technique
  • Time Series Regression; Modelling linear and nonlinear trend; Detecting autocorrelation; Modelling seasonal variation by using dummy variables
  • Stationarity; Unit Root test; Cointegration
  • Vector Error Correlation and Vector Autoregressive models; Impulse responses and variance decompositions
  • Time-varying volatility and ARCH models; GARCH models

Thursday 8 February

14:00
Geographical Information Systems (GIS) Workshop (1 of 4) [Full] 14:00 - 16:00 Department of Geography, Downing Site - Top Lab

This module is shared with Geography. Students from the Department of Geography MUST book places on this course via the Department; any bookings made by Geography students via the SSRMC portal will be cancelled.

This workshop series aims to provide introductory training on Geographical Information Systems. Material covered includes the construction of geodatabases from a range of data sources, geovisualisation and mapping from geodatasets, raster-based modeling and presentation of maps and charts and other geodata outputs. Each session will start with an introductory lecture followed by practical exercises using GIS software.

Monday 12 February

10:00
Factor Analysis (1 of 4) [Full] 10:00 - 12:00 8 Mill Lane, Lecture Room 5

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 (2 of 4) [Full] 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
Survey Research and Design (2 of 4) [Places] 14:00 - 16:00 8 Mill Lane, Lecture Room 1

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 four two-hour sessions, each of which has two parts.

The first hour of each session will consist of a lecture. The four lectures cover: 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 aimed at all students taking the module, irrespective of whether they will conduct surveys themselves or are ‘passive’ users of survey results. Students who have attended these lectures will be able to evaluate research that uses surveys, in particular to understand issues concerning sample selection, response bias and data analysis; to appreciate and understand basic principles of questionnaire design; and to trace appropriate sources of data and appropriate exemplars of good survey practice.

The second hour of each session will be dedicated to practical exercises on how to design surveys. The focus will primarily be on 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 (and we may provide opportunities to field successful exercises as part of YouGov surveys), 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). These second parts of the sessions are aimed at students who are using, or plan to use, surveys in their research, although we welcome any students who would like to learn more about the practicalities of doing survey research.

16:00
Public Policy Analysis (3 of 3) POSTPONED 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.

Meta Analysis (1 of 4) [Places] 16:00 - 18:00 University Information Services, Titan Teaching Room 1, 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.

Aims:
1. To understand and judge the results produced by a meta-analysis
2. To learn how to compute effects sizes based on dichotomous and continuous data
3. To become familiar with heterogeneity tests
4. To learn how to calculate and report subgroup analysis and meta-regression

Session 1: Computational formulas for effect sizes and their variance: fixed/random models
Session 2: Heterogeneity in effect sizes: Tau-squared, Tau, and I-squared
Session 3: Sub-group analysis and meta-regression
Session 4: Vote-counting; publication bias; criticism of meta-analysis