S4E04 Partial Least Squares: Straight Outta Uppsala


In this week’s episode Patrick and Greg talk about partial least squares, a technique that resembles structural equation modeling but with a lot of flexibility, including but not limited to its ability to accommodate both reflective and formative constructs. Along the way they also mention dark Red Bubble, Sheeps Kin, snorting SEM, the Coors Light beer bong, Hotelling’s ghost, Larry the Cable Guy, the Marvel Metaverse, the Evil Eye, Badluck Schleprock, fika, Abba, and pieces left on the table.

Lightly Edited Transcript

We provide a lightly-edited and obviously imperfect audio transcript of the episode available here. This is not an exact representation of the audio, but does provide a searchable document with identified speakers and associated time stamps.

Related Episodes

S4E01 Ordinary Least Squares: Back Where It All Began

S4E02 Underachievers, Overachievers, & Maximum Likelihood Estimation

S4E03 Two-Stage Least Squares Strikes Back

S2E18 Regression — Like That Old High School Friend You’ve Outgrown

Suggested Readings

Abdi, H. (2010). Partial least squares regression and projection on latent structure regression (PLS Regression). Wiley interdisciplinary reviews: computational statistics2, 97-106.

Esposito Vinzi V, Chin WW, Henseler J, Wang H, eds. Handbook of Partial Least Squares Concepts, Methods and Applications in  Marketing and Related Fields. New York: Springer Verlag; 2009.

Haenlein, M., & Kaplan, A. M. (2004). A beginner’s guide to partial least squares analysis. Understanding Statistics3, 283-297.

Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.

Henseler, J. (2017). Partial least squares path modeling. In Advanced methods for modeling markets (pp. 361-381). Springer, Cham.

Lowry, P. B., & Gaskin, J. (2014). Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it. IEEE transactions on professional communication57(2), 123-146.

Purwanto, A., & Sudargini, Y. (2021). Partial least squares structural equation modeling (PLS-SEM) analysis for social and management research: a literature review. Journal of Industrial Engineering & Management Research2(4), 114-123.

Rigdon, E.E. (2013). Partial least squares path modeling. Structural Equation Modeling: A Second Course (2nd ed., G.R. Hancock & R.O. Mueller, Eds.), pages 81–116. Information Age Publishing

Rigdon, E. E., Ringle, C. M., & Sarstedt, M. (2010). Structural modeling of heterogeneous data with partial least squares. Review of marketing research.

Wold, H. O. (1982). Soft modeling: The basic design and some extensions. In K.G. Jöreskog & H. Wold (Eds.), Systems under indirect observation: Causality, structure, prediction (Part II) (pp. 1–54). Amsterdam: North-Holland.

Wold, H. O. (1985). Partial least squares. In S. Kotz & N. L. Johnson (Eds.), Encyclopedia of statistical sciences, vol. 6 (pp. 581–591).New York, NY: Wiley.

Wold, H. O. (1988). Predictor specification. In S. Kotz & N. L. Johnson (Eds.), Encyclopedia of statistical sciences, vol. 8 (pp. 587–599).New York, NY: Wiley.


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