Structural equation modeling (SEM) with lavaan

Structural equation modeling (SEM) with lavaan, available at $54.99, has an average rating of 4.15, with 73 lectures, based on 363 reviews, and has 2848 subscribers.

You will learn about Specify and estimate parameters in a structural equation model using the R lavaan package and interpret and report on the SEM model results. Perform exploratory and confirmatory factors analyses (EFAs and CFAs) using their own datasets. Use a variety of multiple imputation techniques to "fill in," and correct for, missing data. Specify and estimate mediated and other indirect SEM effects using traditional parametric confidence intervals, as well as using bootstrapped and/or bias-corrected and accelerated non-parametric approaches. Specify and estimate the fit of multi-group SEM models, as well as determine levels of measurement invariance (metric, scalar, configural). Output beautiful multi-color plots of fitted SEM models for use in reports and publications. Understand how to set-up, specify, estimate and interpret a latent (growth) curve model, using alternate random intercept and slope model specifications. This course is ideal for individuals who are Course participants may be "brand-new" (inexperienced) to using both R software and/or SEM model estimation, or they may be more experienced in one or both techniques. or This course is very useful for graduate students, quantitative-analysis professionals, and/or for college and university faculty who analyze research data using path models characterized by latent variables. or This course is appropriate for anyone wishing to learn more about specifying, estimating and intrepreting covariance-based SEM models using the no-cost professional-grade SEM modeling features in the lavaan (and other) packages in R software. or The course is appropriate for anyone who wishes to learn how to use a no-cost, professional SEM software suite regarded as an alternative to MPlus. It is particularly useful for Course participants may be "brand-new" (inexperienced) to using both R software and/or SEM model estimation, or they may be more experienced in one or both techniques. or This course is very useful for graduate students, quantitative-analysis professionals, and/or for college and university faculty who analyze research data using path models characterized by latent variables. or This course is appropriate for anyone wishing to learn more about specifying, estimating and intrepreting covariance-based SEM models using the no-cost professional-grade SEM modeling features in the lavaan (and other) packages in R software. or The course is appropriate for anyone who wishes to learn how to use a no-cost, professional SEM software suite regarded as an alternative to MPlus.

Enroll now: Structural equation modeling (SEM) with lavaan

Summary

Title: Structural equation modeling (SEM) with lavaan

Price: $54.99

Average Rating: 4.15

Number of Lectures: 73

Number of Published Lectures: 73

Number of Curriculum Items: 73

Number of Published Curriculum Objects: 73

Original Price: $84.99

Quality Status: approved

Status: Live

What You Will Learn

Specify and estimate parameters in a structural equation model using the R lavaan package and interpret and report on the SEM model results.
Perform exploratory and confirmatory factors analyses (EFAs and CFAs) using their own datasets.
Use a variety of multiple imputation techniques to "fill in," and correct for, missing data.
Specify and estimate mediated and other indirect SEM effects using traditional parametric confidence intervals, as well as using bootstrapped and/or bias-corrected and accelerated non-parametric approaches.
Specify and estimate the fit of multi-group SEM models, as well as determine levels of measurement invariance (metric, scalar, configural).
Output beautiful multi-color plots of fitted SEM models for use in reports and publications.
Understand how to set-up, specify, estimate and interpret a latent (growth) curve model, using alternate random intercept and slope model specifications.

Who Should Attend

Course participants may be "brand-new" (inexperienced) to using both R software and/or SEM model estimation, or they may be more experienced in one or both techniques.
This course is very useful for graduate students, quantitative-analysis professionals, and/or for college and university faculty who analyze research data using path models characterized by latent variables.
This course is appropriate for anyone wishing to learn more about specifying, estimating and intrepreting covariance-based SEM models using the no-cost professional-grade SEM modeling features in the lavaan (and other) packages in R software.
The course is appropriate for anyone who wishes to learn how to use a no-cost, professional SEM software suite regarded as an alternative to MPlus.

Target Audiences

Course participants may be "brand-new" (inexperienced) to using both R software and/or SEM model estimation, or they may be more experienced in one or both techniques.
This course is very useful for graduate students, quantitative-analysis professionals, and/or for college and university faculty who analyze research data using path models characterized by latent variables.
This course is appropriate for anyone wishing to learn more about specifying, estimating and intrepreting covariance-based SEM models using the no-cost professional-grade SEM modeling features in the lavaan (and other) packages in R software.
The course is appropriate for anyone who wishes to learn how to use a no-cost, professional SEM software suite regarded as an alternative to MPlus.

This “hands-on” course teaches one how to use the R software lavaan package to specify, estimate the parameters of, and interpret covariance-based structural equation (SEM) models that use latent variables. “lavaan” (note the purposeful use of lowercase “L” in ‘lavaan’) is an acronym for latent variable analysis, and the name suggests the long-term goal of the developer, Yves Rosseel: “to provide a collection of tools that can be used to explore, estimate, and understand a wide family of latent variable models, including factor analysis, structural equation, longitudinal, multilevel, latent class, item response, and missing data models.” The course uses and executes many “live” examples (with included R scripts and datasets) using no-cost R and RStudio software to demonstrate and teach how to: (1) specify a SEM model in lavaan syntax; (2) fit and then evaluate your model; (3) perform a CFA; (4) impute and replace missing data; (5) estimate mediating and other indirect effects; (6) estimate and evaluate multigroup models, simultaneously establishing measurement invariance; and (7) specifying and estimating latent (growth) curve models, including the use of random (and latent) intercepts and slopes. The R lavaan package is world-class ‘professional-grade’ SEM software, used by thousands of SEM experts, graduate students, and college and university faculty around the world.

Course Curriculum

Chapter 1: Introduction to R and to SEM using the lavaan package

Lecture 1: Introduction to Course and to R (slides)

Lecture 2: Introduction to Path Modeling and SEM (slides, part 1)

Lecture 3: Introduction to Path Modeling and SEM (slides, part 2)

Lecture 4: Input and Output into R

Lecture 5: Useful Data Summary Statistics

Lecture 6: JSS Reading and Exercise #1

Lecture 7: What is lavaan (up to syntax) ?

Lecture 8: Estimate an Example Confirmatory Factor Analysis (CFA)

Lecture 9: Other Useful lavaan Fitted Results Functions

Chapter 2: Confirmatory Factor Analysis (CFA) with lavaan

Lecture 1: Exercise Solutions from Section 1

Lecture 2: SEM Review (slides, part 1)

Lecture 3: SEM Review (slides, part 2)

Lecture 4: SEM Review (slides, part 3)

Lecture 5: Run CFA in R Script (part 1)

Lecture 6: Run CFA in R Script (part 2)

Lecture 7: Run CFA in R Script (part 3)

Lecture 8: Run CFA in R Script (part 4)

Lecture 9: CFA Exercise

Chapter 3: Full SEM Models

Lecture 1: Solution to CFA Exercise from Section 2 (part 1)

Lecture 2: Solution to CFA Exercise from Section 2 (part 2)

Lecture 3: Full SEM Political Democracy Model Example (part 1)

Lecture 4: Full SEM Political Democracy Model Example (part 2)

Lecture 5: Full SEM Political Democracy Model Example (part 3)

Lecture 6: Full SEM Quantitative Attitudes Example (part 1)

Lecture 7: Full SEM Quantitative Attitudes Example (part 2)

Lecture 8: Setting Inequalities Full SEM Exercise

Chapter 4: Factor Analysis

Lecture 1: What is Factor Analysis ?

Lecture 2: Set Up Data for Factor Analysis

Lecture 3: Begin Performing Exploratory Factor Analysis (EFA)

Lecture 4: Continue Performing Various EFAs

Lecture 5: Perform a Confirmatory Factor Analysis (CFA)

Chapter 5: Missing Data and Imputation

Lecture 1: Introduction to Missing Data and Imputation

Lecture 2: Solution to Setting Inequalities Exercise from Section 3 (part 1)

Lecture 3: Solution to Setting Inequalities Exercise from Section 3 (part 2)

Lecture 4: Solution to Setting Inequalities Exercise from Section 3 (part 3)

Lecture 5: Missing Data Problems and Issues (part 1)

Lecture 6: Missing Data Problems and Issues (part 2)

Lecture 7: Missing Data Imputation R Scripts Examples (part 1)

Lecture 8: Missing Data Imputation R Scripts Examples (part 2)

Lecture 9: More R Scripts with Modification Indices and FIML

Lecture 10: Missing Data Exercise and an Audience Question

Chapter 6: Mediation and Indirect Effects

Lecture 1: Introduction to Mediation and Indirect Effects

Lecture 2: Solution to Missing Data Exercise from Section 5 (part 1)

Lecture 3: Solution to Missing Data Exercise from Section 5 (part 2)

Lecture 4: Mediation Concepts (slides, part 1)

Lecture 5: Mediation Concepts (slides, part 2)

Lecture 6: First R Script Mediation Example

Lecture 7: Second R Script Mediation Example (part 1)

Lecture 8: Second R Script Mediation Example (part 2)

Lecture 9: Mediation Exercise

Lecture 10: More on the Complexity of Mediation

Chapter 7: Estimating Group Effects

Lecture 1: Introduction to Estimating Group Effects and Moderation

Lecture 2: Solution to Mediation Exercise from Section 6

Lecture 3: Introduction to Meanstructures; Group Effects Slides

Lecture 4: Group Analysis Functions in lavaan

Lecture 5: Groups R Script CFA Example

Lecture 6: Constraining Parameters Across Groups

Lecture 7: Multi-Group Analysis Measurement Invariance Example (part 1)

Lecture 8: Multi-Group Analysis Measurement Invariance Example (part 2)

Chapter 8: Latent (Growth) Curve Models

Lecture 1: Introduction to Latent (Growth) Curve Models

Lecture 2: First LCM Example (part 1)

Lecture 3: Add Covariates to First LCM Example

Lecture 4: Crime Data LCM Example (part 1)

Lecture 5: Crime Data LCM Example (part 2)

Lecture 6: Crime Data LCM Example (part 3)

Lecture 7: Crime Data LCM Example (part 4)

Lecture 8: Latent Curve Models Review

Lecture 9: Review LCM Crime Models 1 and 2

Lecture 10: Review LCM Crime Models 3, 4 and 5

Lecture 11: Adding Covariates and MIMIC

Lecture 12: More on Covariates and Interactions

Lecture 13: Alternative Specifications of Latent Intercepts and Slopes

Lecture 14: Alternative Specification of Multigroup Models (exercise solution)

Instructors

Geoffrey Hubona, Ph.D.
Associate Professor of MIS and Data Analytics

Rating Distribution

1 stars: 18 votes
2 stars: 35 votes
3 stars: 74 votes
4 stars: 118 votes
5 stars: 118 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

Menu