Structural Equation Modeling (SEM) Using R

Structural Equation Modeling (SEM) Using R

This course will teach you how to implement structural equation models (SEM) using R.

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Overview

Structural Equation Modeling (SEM) allows you to go beyond simple single-outcome models, and deal with multiple outcomes and multi-directional causation. 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 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.

Expert Instructors

Learning Outcomes

You will learn how to:

  • Identify latent, manifest, exogenous and endogenous variables
  • Fit SEM models with the R package lavaan
  • Produce path diagrams of SEM models with semPlot
  • Use confirmatory factor analysis

Who Should Take This Course

Researches and analysts who want to go beyond simple models and incorporate multi-directionality, multiple outcomes and latent variables, using R.

Instructors

Dr. Erin Bunchanan

Dr. Erin Buchanan

Dr. Erin Buchanan is a Professor at Harrisburg University of Science and Technology where she teaches a variety of statistics courses, data science skills, and natural language processing. Her research focuses on applied statistics, the use and misuse of statistics, and computational linguistics. She runs a statistics YouTube channel and StatsTools.com for everyone to improve their skills.

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Course Syllabus

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

Class Dates

2021

Oct 29, 2021 to Nov 26, 2021

2022

Oct 28, 2022 to Nov 25, 2022

2023

No classes scheduled at this time.

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Prerequisites

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.

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Start Dates & Application Deadlines

The chart below details available entry terms for the Computational Data Analytics Certificate of Graduate Study program as well as corresponding application deadlines. Submitting the Application Form is only the first step to beginning the admission process. All of the required materials listed above must be received on or before the application completion deadline for your desired entry term to be considered for admission to that term. We encourage you to complete the application form and begin submitting your materials at least one month before the deadline indicated.

Term Start date Application Deadline
Spring 2020 01/21/2020 11/01/2019
Summer 2020 05/19/2020 04/01/2020
Fall 2020 09/01/2020 07/01/2020

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Our courses have several for-credit options:

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If your school will not accept the ACE Credit Recommendation, but will accept our Record of Completion  noting Continuing Education Units (CEU’s) be sure to download a copy of your Record at the end of the course for your files.  If you need your final marks sent to a specific person, you may write to the Registrar (ourcourses@statistics.com) to request this.  Include the full name, title and email address of the person who is to receive this information, plus your name and course title. An email will be sent, reporting your final marks. If you require a more formal notification, we can provide a transcript, which can be sent to your school. Please pay the transcript preparation fee here, then send an email to the Registrar, requesting the same.

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The Institute for Statistics Education offers 29 courses that are eligible for college credit through the American Council on Education (ACE).

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This course will teach you the fundamental concepts and theory of Structural Equation Modeling, including model specification, model identification, model estimation, model testing, and model modification.
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Class Start Dates: Oct 29, 2021, Oct 21, 2022, Oct 27, 2023

Additional Course Information

Organization of Course

This course takes place online at The Institute for 4 weeks. During each course week, you participate at times of your own choosing – there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirements

About 15 hours per week, at times of your choosing.

Homework

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.

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.

Course Fee & Information

Enrollment
Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date unless you specify otherwise.

Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.

Group Rates
Contact us to get information on group rates.

Discounts
Academic affiliation?  In most courses you are eligible for a discount at checkout.

New to Statistics.com?  Click here for a special introductory discount code.  

Invoice or Purchase Order
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment.

Options for Credit and Recognition

This course is eligible for the following credit and recognition options:

No Credit
You may take this course without pursuing credit or a record of completion.

Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.

CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.

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Structural Equation Modeling (SEM) Using R
$589 | Enroll Now
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