Structural Equation Modeling (SEM) Using R

Aim of Course:

Structural Equation Modeling (SEM) is a modeling technique that allows you to create a deeper understanding of how your data is structured. You will learn how to create structural equation models using the lavaan package in R. We will cover SEM terminology, such as latent and manifest variables, how to create a measurement and structural models, and assess that model for accuracy. In this course, you will apply your knowledge to real datasets to design, build, assess, and update a structural equation model. By the end of the course, you will be able to analyze path models, conduct a confirmatory factor analysis, and diagram your model using the semPlot package.

This course may be taken individually (one-off) or as part of a certificate program.
Course Program:

WEEK 1:  Terms and Concepts in SEM

  • Terminology about models: latent, manifest, exogenous, and endogenous variables
  • Understanding model diagrams: squares, circles, and paths
  • Hypothesis testing in SEM
  • Specification, identification, and degrees of freedom
  • Estimation and other considerations

WEEK 2:  Your First Model and Fit Indices

  • lavaan syntax: understanding how to create models
  • Path models: regression on regression
  • Fit indices: goodness of fit and residual statistics
  • Interpreting lavaan output

WEEK 3:  Measurement Models

  • Creating a measurement model: applications to confirmatory factor analysis
  • Reflective versus formative modeling approaches
  • Latent variables
  • Scaling
  • Creating diagrams with semPaths

WEEK 4:  Full Structural Equation Models

  • Combine path and measurement models
  • Heywood cases
  • Modification indices
  • Model comparison 

Homework in this course consists of short answer problems and includes exercises that require the use of computer software.  In addition to assigned readings, this course also has an end-of-course project, short narrated software demos, example software codes, and supplemental readings available online.

Structural Equation Modeling (SEM) Using R

Who Should Take This Course:
Researches and analysts who want to go beyond simple models and incorporate multi-directionality, multiple causes and latent variables, using R.
Level:
intermediate-advanced
Prerequisite:
You should have some familiarity with statistical modeling (e.g. regression) and the basics of educational measurement and assessment.  You should also be comfortable working in R.
Course Text:
All necessary course materials will be made available online.  If you would like a text, a good optional choice is Latent Variable Modeling Using R by A. Beaujean.
Software:
This courses uses the lavaan and SEMPlot packages in R.
Instructor(s):

Dates:

November 01, 2019 to November 29, 2019

Structural Equation Modeling (SEM) Using R

Instructor(s):

Dates:
November 01, 2019 to November 29, 2019

Course Fee: $589

Do you meet course prerequisites? What about book & software? (Click here to learn more)

We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

Group rates: Email jdobbins "at" statistics.com to get information on group rates. 

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