Welcome to CARMA’s Sixth Webcast Lecture

Dr. Jose Cortina, Virginia Commonwealth University

Testing Interactions with Latent Variables

12:00 pm – 1:30 pm ET

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Dr. Jose Cortina is a professor at Virginia Commonwealth University practicing in the area of Management and Entrepreneurship. His main scopes of expertise range from Research Methods to Meta-Science and finally to Working with Big Data. In 2017, Dr. Cortina was awarded the ORM Award for Best Paper published in 2017. His recent published intellectual contributions are as follows:

  • Tonidandel, S., King, E., Cortina, J. (2015). Big data at work: The data science revolution and organizational psychology. Routledge.
  • Cortina, J. (2020). Explaining interaction effects: A commentary. In Research Methods in International Business. Springer.
  • King, E. B., Tonidandel, S., Cortina, J., Fink, A. A. (2016). Building understanding of the data science revolution and IO psychology. Routledge New York, NY.
  • Cortina, J., Kohler, T., Keeler, K., Pugh, S. D. (in press). Situation strength as a basis for interactions in psychological models. Psychological Methods.
  • Dormann, C., Guthier, C., Cortina, J. (2020). Introducing Continuous Time Meta-Analysis (CoTiMA). Organizational Research Methods.
  • Keeler, K., Cortina, J. (2020). Working to the beat: A self-regulatory framework linking music characteristics to job performance.. (vol. in press, pp.43). Academy of Management Review.


We all know what latent variable models are. We use them all the time. Well, not all the time. In our paper, we show that investigators who start their work with latent variable models usually abandon such models when testing for interactions. For example, they might use SEM to show that latent-X and latent-Z both predict latent-Y, but then they use moderated regression with scale scores to test the X by Z interaction. Given that latent variable models are even more useful for multiplicative models than they are for additive models, this doesn’t make a lot of sense. By contrast, we show that papers that test only additive models tend to stick with SEM throughout. The reason for this paradox is almost certainly discomfort with methods for testing latent interactions. In an attempt to address this issue, we update the review of procedures contained in Cortina, Chen, and Dunlap (2001) and develop new R code for implementing some of the best procedures for testing latent interactions.

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Quick Chat with Dr. Jose Cortina

Upcoming CARMA Events

  • Jan. 21, 2022 – PhD Prep Group, Dr. Allison Gabriel, Dr. John Paul Stephens, Dr. Aaron Hill – A Method Lens on Developing a Research Pipeline and Research Identity
  • Feb. 4, 2022 – Webcast Lecture, Dr. Elaine Hollensbe – Rigor/Trustworthiness in Qualitative Research
  • Feb.4 2022 – Topic Interest Group