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Showing courses 1-25 of 31
Courses per page: 10 | 25 | 50 | 100

Agent-based Modelling with Netlogo Tue 20 Feb 2018   14:00 [Places]

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.

Basic Quantitative Analysis (BQA Intensive) Mon 22 Jan 2018   09:00 Not bookable

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.

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.

Topics:

  • Key concepts, from correlates to causes
  • Quasi-experimental Studies and Causal Inference
  • Propensity Score Matching and Causal Inference
  • Fixed-effects Regression Models and Causal Inference
Conversation and Discourse Analysis Tue 23 Jan 2018   16:00 [Full]

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
Critical Approaches to Discourse Analysis Tue 20 Feb 2018   13:30 [Places]

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.

Session 1: The origins of critical discourse analysis (the Frankfurt school, Foucault, post-structuralism, feminism); how theoretical backgrounds shape research design
Session 2: 'Doing' discourse analysis: analysing methods and approaches

Doing Multivariate Analysis (DMA Intensive) Wed 24 Jan 2018   09:00   [More dates...] Not bookable

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.

3 other events...

Date Availability
Mon 20 Nov 2017 10:00 Not bookable
Wed 22 Nov 2017 10:00 Not bookable
Wed 22 Nov 2017 10:00 Not bookable
Doing Qualitative Interviews Tue 23 Jan 2018   14:00 [Places]

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
Ethnographic Methods Tue 20 Feb 2018   15:30 [Places]

This module is an introduction to ethnographic fieldwork and analysis.

The ethnographic method was originally developed in the field of social anthropology, but has grown in popularity across several disciplines, including sociology, geography, criminology, education and organization studies.

Ethnographic research is a largely qualitative method, based upon participant observation among small samples of people for extended periods. A community of research participants might be defined on the basis of ethnicity, geography, language, social class, or on the basis of membership of a group or organization. An ethnographer aims to engage closely with the culture and experiences of their research participants, to produce a holistic analysis of their fieldsite.

This module is intended for students in fields other than anthropology. It provides an introduction to contemporary debates in ethnography, and an outline of how selected methods may be used in ethnographic study.

Session 1: The Ethnographic Method What is ethnography? Can ethnographic research and writing be objective? How does one conduct ethnographic research responsibly and ethically?

Session 2: Photography and Audio Recording in Ethnographic Work What kinds of audiovisual equipment, and practices of photography and sound recording, can be used to support an ethnographer’s research process? What kinds of the epistemological, theoretical, social, and ethical considerations tend to arise around possible use of these technologies in anthropological fieldwork and analysis?

Experimental Methods Tue 16 Jan 2018   14:00 [Full]

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.

This course will introduce students to 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 led 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.

  • To understand that the emphasis on statistical significance testing has obscured the goals of analysing data for many social scientists.
  • To discuss other ways in which the significance testing paradigm has perverted scientific research, such as through the replication crisis and fraud.
  • To understand the role of graphics in EDA
Factor Analysis Mon 12 Feb 2018   10:00 [Places]

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
Foundations in Applied Statistics (FiAS Intensive) Wed 17 Jan 2018   09:00 Not bookable

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
Further Topics in Multivariate Analysis (FTMA) Mon 29 Jan 2018   10:00 Not bookable

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.

Geographical Information Systems (GIS) Workshop Thu 8 Feb 2018   14:00 [Full]

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.

Introduction to R (Lent) Tue 16 Jan 2018   14:00 [Full]

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).

Introduction to Stata (Lent) Tue 30 Jan 2018   14:00 [Places]

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.

Issues in Measurement: Validity and Reliability Mon 5 Feb 2018   14:00 [Places]

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.

Meta Analysis Mon 12 Feb 2018   16:00 [Places]

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

Microsoft Access: Database Design and Use Tue 21 Nov 2017   14:00 In progress

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.
Multilevel Modelling Wed 21 Feb 2018   09:00 [Places]

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
Panel Data Analysis (Intensive) Wed 14 Mar 2018   09:00 [Places]

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.

  • Introduction to PDA: Concepts and uses
  • Manipulating and describing panel data
  • An overview of random effects, fixed effects and ‘hybrid’ panel models
  • Panel models for dichotomous outcomes
Public Policy Analysis Mon 29 Jan 2018   16:00 [Places]

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.

Research Ethics (Lent) Wed 24 Jan 2018   14:30 [Full]

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.

Secondary Data Analysis new Tue 6 Mar 2018   14:00 [Places]

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.

Social Network Analysis Wed 14 Feb 2018   09:00 [Full]

This introductory course is for graduate students who have no prior training in social network analysis (SNA). The course overviews the literature on SNA, and teaches how to handle databases, run network statistics, and visualise graphs.

Topics covered

  • An overview of themes in the literature on SNA
  • Searching, producing, and formating relational data
  • Basic network statistics using R and Ucinet
  • Visualisation of graphs
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