I also would like to take this opportunity to thank James for all his help and work this summer and wish him the very best of luck. The atmosphere in his lecture is amazing as everyone is fully involved. He has all the traits that make a great tutor: energetic, enthusiasm, rich and robust knowledge and patience to explain. Sorry for the previous lecturers who taught me statistics but it's true that James is one of his kind. James is the best statistics lecturer I ever met in my life. It's difficult to find methods courses that are targeted at academics as well as students, so this was a wonderful and much-needed opportunity!
He explained things thoroughly and clearly. This course is great for students who do not have statistical training, and also good for those want a deeper understanding on this topic. James Abdey is an inspiration both in terms of teaching skills and also in terms of knowledge in statistics. The below schedule is subject to change.ĭr.
Please note: A full timetable will be provided at registration on Monday 14 August. Computer practical classes take place in the afternoon.Ī 2-hour final examination will take place on the afternoon of Friday 25 August 2017. Thursday - Categorical data analysis, correlationįriday - Multiple linear regression and model buildingĪll lectures take place from 10am-1pm. Wednesday - Hypothesis testing (one- and two-sample) Tuesday - Sampling distributions, confidence intervals Monday - Data visualisation, descriptive statistics The following teaching schedule is indicative only, and is subject to change. However, a “course pack” will be provided which will serve as background reading.
Discovering Statistics using IBM SPSS Statistics. (4th ed.). Other topics to be covered include interval estimation and hypothesis testing (for one and two samples), categorical data, correlation and regression, analysis of variance and several multivariate analysis techniques, such as factor analysis.įield, A. The course will begin with an overview of the SPSS environment, followed by data visualisation and descriptive statistics. Assumptions, merits and limitations of methods will be discussed. Topics covered in the course will be wide-ranging, such that participants will be exposed to a variety of statistical methods reflecting the different sorts of data which a researcher may be required to analyse.
SPSS is a popular choice of statistical software and is ideally suited for empirical research in the social sciences. The course will consist of daily lectures supported by computer-based practical classes which will allow course participants to practise implementing the lecture material hands-on in SPSS.
Professional rate: £2,800 Programme details
not knowing how to interpret output from software packages and what conclusions can be drawn.inexperience with using statistical software packages (specifically SPSS here).not knowing which techniques are appropriate for different types of data.a lack of understanding of various statistical methods.This applications-oriented course is designed for researchers who lack the confidence to perform data analysis independently due to: Working with datasets, the course will cover widely-used statistical methods including descriptive statistics, data visualisation, statistical inference, categorical data, correlation and regression, analysis of variance and multivariate analysis (such as factor analysis). Instead of being purely theory-oriented, emphasis will be more on the practical application of a variety of statistical techniques to supplied datasets. Statistical Methods for Social Research using SPSS takes a more applied approach to conventional statistics by focusing on encouraging participants to "get their hands dirty with data". This course concentrates on transforming participants into competent and confident users of SPSS to enable them to conduct independent data analysis for their own research needs. Many researchers in the social sciences use SPSS to perform data analysis, but often formal training in use of the software and how to interpret output is severely lacking. For those undertaking research in the social sciences, an ability to handle data is an essential skill.