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.
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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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
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 Oct 22, 2021
2022
No classes scheduled at this time.
2023
Oct 27, 2023 to Nov 24, 2023
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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The majority of our instructors have more than five years of teaching experience online at the Institute.
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What type of courses does the Institute offer?
The Institute offers approximately 80 courses each year. Topics include basic survey courses for novices, a full sequence of introductory statistics courses, bridge courses to more advanced topics. Our courses cover a range of topics including biostatistics, research statistics, data mining, business analytics, survey statistics, and environmental statistics.
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Our courses have several for-credit options:
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The American Council on Education’s College Credit Recommendation Service (ACE CREDIT) has evaluated and recommended college credit for several courses offered by The Institute for Statistics Education at Statistics.com.
ACE CREDIT’s College and University Network includes 2,200 higher education institutions that consider ACE credit recommendations for transfer to degree programs. The decision to accept specific credit recommendations is up to each institution.
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How many courses are ACE college credit eligible?
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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Related Courses
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.
Topic: Statistics, Statistical Modeling | Skill: Intermediate | Credit Options: CEU
Class Start Dates: Oct 29, 2021, Oct 21, 2022, Oct 27, 2023
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.
Supplemental Information
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