In this in-depth tutorial, we take you through Structural Equation Modeling (SEM) with Mediation and Higher-Order Constructs in R Studio—a powerful statistical approach used to model complex relationships between observed and latent variables. Ideal for research scholars, this video will guide you through SEM's essential concepts, applications, and step-by-step instructions on implementing SEM with mediation and higher-order constructs in R.
In This Video, You’ll Learn:
What is Structural Equation Modeling (SEM)? – Get introduced to SEM, its benefits in research, and how it differs from traditional regression models by modeling both direct and indirect relationships.
Understanding Key SEM Concepts:
Observed vs. Latent Variables – Learn the difference between observed variables (measured directly) and latent constructs (inferred indirectly).
Path Analysis & Model Specification – Discover how path analysis in SEM allows for specifying complex relationships between multiple variables.
Mediation Analysis – Understand the concept of mediation, where an intermediary variable transmits the effect of an independent variable on a dependent variable.
Higher-Order Constructs – Explore the use of higher-order constructs (i.e., factors composed of multiple sub-dimensions), crucial for multi-dimensional constructs often found in behavioral and social sciences.
Preparing Data for SEM in R Studio:
Data Cleaning and Preparation – Learn how to load, clean, and structure your dataset in R Studio, ensuring it meets SEM’s requirements.
Handling Missing Data – Strategies for addressing missing values in SEM to maintain the integrity and reliability of your model.
Conducting SEM with Mediation and Higher-Order Constructs in R Studio:
Defining the Model Structure – Understand how to define and code a model structure in R Studio for both mediation and higher-order constructs using the lavaan package.
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