Learn how to create and assess measurement models used to confirm the structure of a scale or questionnaire.
When working with data, we often want to create models to predict future events, but we also want an even deeper understanding of how our data is connected or structured. In this course, you will explore the connectedness of data using using structural equation modeling (SEM) with the R programming language using the lavaan package. SEM will introduce you to latent and manifest variables and how to create measurement models, assess measurement model accuracy, and fix poor fitting models. During the course, you will explore classic SEM datasets, such as the Holzinger and Swineford (1939) and Bollen (1989) datasets. You will also work through a multi-factor model case study using the Wechsler Adult Intelligence Scale. Following this course, you will be able to dive into your data and gain a much deeper understanding of how it all fits together.
When working with data, we often want to create models to predict future events, but we also want an even deeper understanding of how our data is connected or structured. In this course, you will explore the connectedness of data using using structural equation modeling (SEM) with the R programming language using the lavaan package. SEM will introduce you to latent and manifest variables and how to create measurement models, assess measurement model accuracy, and fix poor fitting models. During the course, you will explore classic SEM datasets, such as the Holzinger and Swineford (1939) and Bollen (1989) datasets. You will also work through a multi-factor model case study using the Wechsler Adult Intelligence Scale. Following this course, you will be able to dive into your data and gain a much deeper understanding of how it all fits together.
