Introduction to STATA - 23rd January 2013
The course provides an introductory training in STATA, a statistical package increasingly used for social research data analysis which has powerful data manipulation procedures and extensive and powerful statistical capabilities.

Introduction to Bayesian Analysis using WinBUGS - 24th and 25th January 2013
Use of Bayesian methods is becoming increasingly widespread within quantitative social and health sciences, particularly for analysing data with complex structure, such as hierarchical or multilevel data. However, very few applied researchers have any formal training in Bayesian methods. This two-day course aims to introduce quantitative researchers to the basic principles of Bayesian inference and simulation-based methods for estimating Bayesian models, and to highlight some of the potential benefits that a Bayesian approach can offer. There is a large practical component to this course with time for hands-on data analysis using examples drawn mainly from the social and health sciences. No previous experience of Bayesian methods or WinBUGS is necessary.

Introduction to R - 1st February 2013
This course is aimed at people who wish to familiarise themselves with the freely available statistical analysis software R. R is a command language that can be used to carry out standard statistical analyses but also has powerful facilities to enable users to create their own routines or implement methods designed by other researchers. The course will: introduce participants to the R environment; explain how to enter data and run simple descriptive statistical methods; describe how to run standard procedures; show how to run commands designed by other researchers and how to develop commands for non-standard analyses. For background materials, software and reading please go to http://www.r-project.org/

Latent Factor Analysis - 6th February 2013
This short course covers latent variables and factor analysis at an introductory and intermediate level. A latent variable is a thing (such as an attitude, an orientation, an experience or a level, e.g. the level of well-being) that has been measured using a set of related indicators. A set of three or more indicators can be considered the manifest variables, from which a single latent variable might be derived. Factor analysis is one way to derive a single factor from a set of variables, and is thus called a data reduction method. The course is suitable both for primary-data collection researchers (who may need to write a suitable questionnaire), and for those who want to analyse secondary data sets.

Understanding Statistics - 7th March 2013
This course is an opportunity for participants to ask the basic statistical questions they have always wanted to ask. It focuses on basic statistical concepts such as: the four levels of measurement, measures of central tendency (median, mean, and mode), measures of dispersion (percentiles, variance, standard deviation, and standard error), confidence intervals, hypothesis testing, design effects and the issue of causality. These skills allow participants to interpret and evaluate existing research findings within the remit of basic statistics. The course is composed of a combination of lectures and practicals. The course will provide participants with the expertise required to evaluate the meaning, robustness and generalisability of basic statistical research findings.

Introduction to Sampling - 8th March 2013
This course introduces participants to what survey sampling is, why it is important, and how it is implemented. The course focuses on the practical aspects as well as some of the mathematics. It is composed of a combination of lectures and practicals. Content includes: types of samples, how to construct a ‘sampling frame’, types of probability samples (e.g., simple random, systematic, stratified, multi-stage clustered, unequal probabilities of selection), what ‘sampling error’ is, the role of sampling error in confidence intervals, how to determine sample size and an introduction to the effects of different types of sample designs on confidence intervals.

Introduction to Structural Equation Modelling using Mplus - 13-15th March 2013
Structural Equation Models (SEM) amalgamate regression analysis, path analysis and factor analysis, allowing for more richly detailed statistical models to be specified and compared to data than by using these techniques individually. Historically, SEM models were confined to the analysis of continuous observed data, limiting their usefulness in applied social research, where many phenomena are inherently discrete or are measured only with coarse-grained instruments. Advances in recent years have made SEM methods for categorical data available to applied researchers. This course aims to train quantitative social scientists to use the Mplus programme in the application of structural equation modelling techniques to non-continuous observed data.

Advanced Generalised Linear Models - 20th-21st March 2013
This course will introduce the modelling of relationships between variables with an emphasis on practical considerations. It will include: an overview of statistical modelling, the exponential family of distributions, linear regression models, quantile regression, one and two-way contingency tables, measures of association, odds ratios and properties of odds ratios, logistic regression, Poisson regression and extensions to random effects. Participants should have an understanding of basic data analytical techniques and concepts such as: cross tabulations, significance testing, hypothesis testing and correlation. An understanding of linear regression is required as is some understanding of binary logistic regression.

Quantitative Models in Marketing and Management - 27th-29th March 2013
This 3-day course deals with the analysis of datasets common in marketing and management using generalized linear models which provide a theoretically-coherent method for data analysis. This course deals with the underlying theory of modelling and data analysis and applies it to a wide range of data and modelling situations.The course covers the following topics: R and the Rcmdr, measurement scales and coding, generalized linear models, OLS regression, proportional-odds logit models, multinomial logit models, Poisson regression, variable and test selection, multi-model presentation and inference, model diagnostics and transformation.

Web Survey Design - 25th April 2013
The course focuses on the design of Web survey instruments or questionnaires. There is a large body of research showing that design of Web surveys can have a big effect on the answers obtained. The course will cover all aspects of instrument design for Web surveys, including the appropriate use of widgets (e.g., radio buttons, check boxes) for Web surveys, general formatting and layout issues, movement through the instrument (action buttons, navigation, error messages), and so on. The course will draw on empirical results from experiments on alternative design approaches as well as practical experience in the design and implementation of Web surveys. The course will NOT address the technical aspects of Web survey implementation (such as hardware, software or programming), and will also NOT focus on question wording, sampling or recruitment issues. Participants are encouraged to bring their own examples to class for discussion.

Inference in Web Surveys - 26th April 2013
There are many different ways that samples can be obtained for online surveys. These include open invitation surveys of volunteers, intercept surveys, opt-in or access panels, Amazon’s Mechanical Turk, Google Consumer Surveys, list-based samples, and the like. In most cases, the goal is to make inference to some large population. The different approaches to selecting samples and inviting respondents to complete a survey vary in their inferential properties. Threats to inference include sampling error, coverage error, and nonresponse error. In addition to selection methods, a variety of adjustment methods, such as weighting, propensity score adjustment and matching, are being used to mitigate the risk of inferential errors. The course will focus on the assumptions behind the different approaches to inference in Web surveys, the benefits and risks inherent in the different approaches, and the appropriate use of a particular approach to sample selection in Web surveys.