Statistical and Data Management Training Courses available on an inhouse basis:
Introduction to Bayesian Statistics using Mplus

Who is the course aimed at?
This course is aimed at those with previous knowledge of Mplus who wish to use the software as a way into the world of Bayesian estimation. As such it is an ideal follow on course for individuals or groups who have previously taken our introductory course in Structural Equation Modelling, or as a the second day of a two-day inhouse course, with the first day being the introductory course in Structural Equation Modelling.

Course level:
A basic knowledge of the Mplus language (reading data in, running files, the BY, ON, WITH commands, and the use of @ and ( ) symbols for fixing and naming parameters), and how path analysis models and model improvement are tested e.g. basic fit indices, chi-square tests) is required. It is also highly desirable to have an understanding of basic and intermediate level statistical methods, specifically t-tests, anova, simple linear regression, logistic regression, and CFA, since we will cover using Bayesian estimation in all of these methods. No previous knowledge or experience with Bayesian estimation is assumed.

Course content and aims:
The course will cover the following topics:

• What is Bayes theorem and how can it help us?
• Bayesian estimation vs classical statistics/null hypothesis significance testing
• Selecting appropriate 'priors'
• Using Monte Carlo Markov Chains for model fitting
• Using Bayesian estimation to fit a simple linear regression model in Mplus
• Using Bayesian estimation to fit a path analysis model in Mplus, including estimating indirect effects
• Using Bayesian estimation to fit a CFA in Mplus

The course comprises of a mixture of short lectures on the basic theory behind Bayes theorem, Bayesian estimation, and Monte Carlo Markov Chain (MCMC) methods, teaching via examples worked through by the trainer on real data sets which participants can follow, and exercises to practice the skills just learned. You will also receive a 80-page coursebook containing all the notes and worked examples, providing an easy reference for the basics of performing Bayesian Estimation in Mplus, and as a reminder for the techniques you have learned.

Course timings: The course fits into a full day, starting at 9.15am and finishing at around 5.30pm, with a 45 minute lunch break, and short mid-morning and afternoon coffee breaks. Transport timings for my return journey permitting, I am always willing to stay on for a while after the end of the course to answer questions pertinent to participants' own data sets.

The teacher:
Dr Chris Stride has been using Mplus in his work as a statistician and data manager for the last decade. He has particular experience and expertise in teaching non-statisticians from the fields of psychology, HR, management and the social sciences. He runs inhouse courses at UK and European Universities and public sector institutions, and public training courses based in London, Sheffield and Berlin.

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