Learning goals

In this course you will learn how to translate a social scientific theory into a statistical model, how to analyze your data with these models, and how to interpret and report your results following APA standards.

After completing the course, you will be able to:

  1. Translate a verbal theory into a conceptual model, and translate a conceptual model into a statistical model.
  2. Independently analyze data using the free, open-source statistical software R.
  3. Use a path model to represent the hypothesized causal relations among several variables, including relationships such as mediation and moderation.
  4. Apply a latent variable model to a real-life problem wherein the observed variables are only indirect indicators of an unobserved construct.
  5. Explain to a fellow student how structural equation modeling combines latent variable models with path models and the benefits of doing so.
  6. Reflect critically on the decisions involved in defining and estimating structural equation models.

Background knowledge

We assume you have basic knowledge about multivariate statistics before entering this course. You do not need any prior experience working with R. If you wish to refresh your knowledge, Andy Field’s Discovering Statistics using R is quite a popular and well-respected introductory stats book.

If you cannot access the Field book, many other introductory statistics textbooks cover these topics equally well. So, use whatever you have lying around from past statistics courses. You could also try one of the following open-access options: