# Structural Equation Models, Path Analysis, Causal Modelling and Latent Variable Models Using R

## 16 September 2019 - 20 September 2019

£275.00 - £540.00### Event Navigation

## Course Overview:

This course provides a comprehensive introduction to a set of inter-related topics of widespread applicability in the social social sciences: structural equation modelling, path analysis, causal modelling, mediation analysis, latent variable modelling (including factor analysis and latent class analysis), Bayesian networks, graphical models, and other related topics. The course begins with a thorough review, both practical and theoretical, of regression modelling, particularly on general and generalized linear regression. We then turn to the topic of path analysis. At its simplest, path analysis can be seen as an extension of standard (e.g. linear) regression analysis to cases where there are more complex structural relationship between the predictor and outcome variables. More generally, and more usefully, we can view path analysis as specifying and modelling causal relationships between observed variables. In order to fully appreciate path analyses, and especially their role as causal models, we will introduce the concept of directed acyclic graphical models, also known as Bayesian networks, which are a powerful mathematical and conceptual tool for reasoning about causal relationships. We then thoroughly cover the topic of mediation analysis, which can seen as a special case, though still very widely applicable, of path analysis and causal models. We then turn to structural equation modelling, which can be seen as an extension of path analysis, particularly due to the inclusion of unobserved or latent variables. More generally, structural equation models allow for the specification and testing of more complex theoretical models of the observed data. In order to properly introduce structural equation models, we first explore latent variable models, particularly factor analysis and latent class models. In our coverage of structural equation models we deal with the general concepts of model identification, inference, and evaluation, and then explore special topics such as categorical, nonlinear, and non-normal structural equation models, multilevel structural equation models, and latent growth curve modelling.

### Intended Audience

This course is aimed at anyone who is interested to learn and apply this powerful and flexible set of statistical modelling methods that have widespread application across the social, medical, and biological sciences.

**Venue** – PS statistics head office, 53 Morrison Street, Glasgow, G5 8LB – Google map

**Availability** – 20 places

**Duration** – 5 days

**Contact hours** – Approx. 28 hours

**ECT’s** – Equal to 3 ECT’s

**Language** – English

### Packages

We offer **COURSE ONLY** and **ACCOMMODATION PACKAGES**;

•** COURSE ONLY** – Includes lunch and refreshments and welcome meal Monday evening.

• **ACCOMMODATION PACKAGE** (to be purchased in addition to the course only option) – Includes breakfast, lunch, refreshments and welcome dinner Monday evening. Self-catering facilities are available in the accommodation. Accommodation is multiple occupancy (max 3- 4 people) single sex en-suite rooms. Arrival Sunday 15th September (between 17:00-21:00) and departure Friday 20th September (accommodation must be vacated by 09:15).

To book ‘COURSE ONLY’ with the option to add the additional ‘ACCOMMODATION PACKAGE’ please scroll to the bottom of this page.

Other payment options are available please email oliverhooker@psstatistics.com

PLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@psstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees (and accommodation fees if booked through PS statistics) will be credited. However, PS statistics will not be held responsible/liable for any travel fees, accommodation costs or other expenses incurred to you as a result of the cancellation. Because of this PS statistics strongly recommends any travel and accommodation that is booked by you or your institute is refundable/flexible and to delay booking your travel and accommodation as close the course start date as economically viable.