Introduction to Structural Equation Modelling using Mplus

Description:

'Introduction to Structural Equation Modelling using Mplus'

Details of the course are given below: for further information and to book a place, go to http://www.figureitout.org.uk

If London is too far to travel and there are a number of people from your organisation looking for such training, note that this course, and further courses on using the SPSS syntax language, Multiple Regression and Multilevel Modelling, are also available on an inhouse basis in the UK and Europe; see http://www.figureitout.org.uk for details of our inhouse and bespoke training.

******* Course details are as follows: *******

-|- Introduction to Structural Equation Modelling using MPlus -|-

Friday 6th January 2012

at the Social Sciences Building, City University, St John Street, London

-|- Who is the course aimed at?
This course is aimed at two distinct groups; those seeking a beginners' course in Path Analysis techniques such as Structural Equation Modelling (SEM) and Confirmatory Factor Analysis (CFA); and those with some previous experience of Structural Equation Modelling who wish to learn to use the powerful and elegant MPlus software, which can be employed in an incredibly wide range of model fitting scenarios.

-|- Course level:
A basic knowledge of Exploratory Factor Analysis and the ideas behind multiple regression analysis will be advantageous to participants. No previous knowledge or experience with SEM or Mplus is assumed

-|- Course content and aims:
The course will cover the following topics:
- Introduction to Path Analysis - what is it and why do we need it?
- Assessing the fit of a Structural Equation Model
- Comparing competing Structural Equation Models
- Preparing and importing data into Mplus from SPSS
- Getting started with analysis in MPlus - the basics of the language
- Fitting a simple Confirmatory Factor Analysis in Mplus
- Fitting a simple Structural Equation Model in Mplus
- Testing for indirect effects in Structural Equation Models
- Fitting Structural Equation Models with missing data
- Including Categorical variables in Structural Equation Models
- Multiple group analysis

-|- Course Format:
The course comprises of a mixture of short lectures on the basic theory behind Structural Equation Models, 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 50-page coursebook containing all the notes and worked examples, providing an easy reference for the basics of performing SEM in MPLus, and as a reminder for the techniques you have learned.

-|- Course schedule:
The course will start at 9.30am, with a lunch break from 1pm-2pm, and short coffee breaks at 11.30am and 3.30pm. It will finish at around 5.15-5.30pm, though I will be willing to stay on for a while after this to answer questions pertinent to participants' own data sets or any other Structural Equation Modelling queries you may have.

For further information and to book a place, go to http://www.offbeat.group.shef.ac.uk/FIO/trainingcourses.htm

-|- The trainer:
Dr Chris Stride has been using SPSS and Mplus in his work as a statistician and data manager for the past decade. He has particular experience and expertise in teaching non-statisticians from the fields of psychology, HR, management and the social sciences. He is a Chartered Statistician and a member of the ASSESS (UK and European SPSS Users Group) Committee, and has been running regular statistical training courses at the University of Sheffield and on an inhouse basis to Universities and Public Sector organisations over the last five years.

Cost:

250.00 UK pounds / 175.00 UK pounds for students; further 50.00 pounds reduction if also booking Multilevel Modelling course for the previous day

Website and registration:

http://www.offbeat.group.shef.ac.uk/FIO/trainingcourses.htm

Region:

Greater London

Keywords:

Frameworks for Research and Research Designs, Descriptive Research, Exploratory Research, Explanatory Research and Causal analysis, Comparative and Cross National Research, Survey Research, Cross-Sectional Research, Longitudinal Research , Evaluation Research, Case Study, Pilot Study, Participatory Research, Behavioural Research, Secondary Analysis, Analysis of administrative data, Mixed Methods, Frameworks for Research and Research Designs (other), Hypothesis testing research, Data Collection, Survey and Questionnaire Design, Observation, Online Data Collection , Data Quality and Data Management , Quality in Quantitative Research, Measurement Error, Data Editing, Nonresponse , Statistical Disclosure Control, Quantitative Data Handling and Data Analysis, Statistical Theory and Methods of Inference, Small Area Estimation, Regression Methods, Multilevel Modelling , Longitudinal Data Analysis, Event History Analysis, Latent Variable Models, Structural equation models, Time Series Analysis, Quantitative Approaches (other), Mixed Methods Data Handling and Data Analysis, Mixed Methods Approaches (other), Quantitative Software, Mplus , Structural Equation Modelling , Structural Equation Modeling , SEM , CFA , Factor Analysis , Valid

Related publications and presentations:

Frameworks for Research and Research Designs
Descriptive Research
Exploratory Research
Explanatory Research and Causal analysis
Comparative and Cross National Research
Survey Research
Cross-Sectional Research
Longitudinal Research
Evaluation Research
Case Study
Pilot Study
Participatory Research
Behavioural Research
Secondary Analysis
Analysis of administrative data
Mixed Methods
Frameworks for Research and Research Designs (other)
Hypothesis testing research
Data Collection
Survey and Questionnaire Design
Observation
Online Data Collection
Data Quality and Data Management
Quality in Quantitative Research
Measurement Error
Data Editing
Nonresponse
Statistical Disclosure Control
Quantitative Data Handling and Data Analysis
Statistical Theory and Methods of Inference
Small Area Estimation
Regression Methods
Multilevel Modelling
Longitudinal Data Analysis
Event History Analysis
Latent Variable Models
Structural equation models
Time Series Analysis
Quantitative Approaches (other)
Mixed Methods Data Handling and Data Analysis
Mixed Methods Approaches (other)
Quantitative Software

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