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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.
What are the requirements?
- Students will be required to install no-cost R and RStudio software (instructions are provided).
- Students who are new to R software will need to need to use and practice with the “introduction to R” scripts and exercises that are provided with the course’s videos and materials.
What am I going to get from this course?
- 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 is the target audience?
- 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.
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