About the Author

Hi! I am John Sakaluk (pronounced: “Sack-uh-luck”, like a bag of luck)!

A wild John appeared!

I use he/him prounouns, and I live/work in Canada on the traditional lands of the Anishinaabek, Haudenosaunee, Lūnaapéewak, and Chonnonton Nations

What’s most relevant, for you, is that:

• I am an Assistant Professor in the Department of Psychology at Western University (formerly/occassionally still refered to as the University of Western Ontario) in London (not that London), in Southwestern Ontario, Canada.
• My research program spans the domains of sexuality, relationships, and psychological interventions. Most of what I’m known for would be considered applied quantitative psychology work, in which I use (or promote) emerging or improved analytic techniques, usually either for the purpose of psychometrics or research synthesis
• I was a postdoctoral fellow in Dr. Emily Impett’s lab, in which she and her students often conduct dyadic data analyses
• Thanks to Emily’s support, I took a week-long dyadic workshop from Dr. Dave Kenny and Dr. Tessa West. This workshop was mostly taught through the lens of MLM (and in SPSS), but was nonetheless was a useful training exercise that got me thinking a lot about dyadic data and why it is analyzed the way it is (and isn’t)
• My “expertise” in dyadic data analysis is quite a recent phenomenon–emerging years after said workshop. During the submission of an article for a special issue in Personal Relationships (see Sakaluk, Fisher, and Kilshaw (2021)), the reviewers encouraged us to develop some R functionality to help with the computation of dyadic-versions of invariance testing effect size indexes (from Nye and Drasgow (2011)). Working through this request unlocked some programming barriers in my brain, and a few bursts of impromptu coding later, I had laid the foundations for the dySEM package for R (see here for link to CRAN site). After dySEM kicked around for awhile, and I gave a few smaller talks on dyadic SEM, I was invited to give the closing keynote at the 2023 Close Relationships Preconference of the Society for Personality and Social Psychology (REF OR SLIDES), during which I mostly evangilized abou the benefits of dyadic SEM. That talk–which was received with a strange amount of controversy–seems to have catalyzed things for this portion of my research program, as an invited review paper on dyadic SEM (REF) and a dySEM workshop (REF OR SLIDES) have quickly followed.
• Though my “expertise” in dyadic data analysis is a recent phemonenon, I have extensive training and experience with SEM and R. My first foray with factor analysis was as a 4th year undergrad working with Dr. Robin Milhausen; I borrowed her copy of Multivariate Statistics (Tabachnick and Fidell (2012)) and my mind has been lost to latent variable modeling ever since. Afterwards, as a graduate student at the University of Kansas, I took multiple courses on structural equation modeling with latent variables, including from Drs. Carol Wood, Mijke Rhemtulla, Alexander Schoemann, and Todd Little. This included learning how to apply SEM in contexts with dependent data ¹, which is largely what has enabled me ot move into dyadic SEM even though I have not often analyzed primary dyadic data. It was around my 3rd year of grad school that I began routinely using the psych and lavaan packages for latent variable modeling in R.
• Since then, I have routinely used and taught R and SEM–10 years and counting–including to some leading dyadic data analysis folks in the field.
• Most recently, I have added the label of R developer to my set of experiences, having taken the plunge of R package creation and maintenance.

Outside of my work-related bona fides, I am known for:

• hailing from North Bay, ON
• making good food and drinks
• having a peculiar brand of academic-speak in both writing and talking²
• code-switching between sounding very American (I’m a very enthusiastic “y’all”-er), or very Canadian (think Letterkenny)
• using moderately? profane language
• playing basketball (I play a [very watered down] Shawn Marion-like game)
• having brutal handwriting
• typing very loudly (I snap my spacebar particularly hard, for some reason)
• having a very wonderful, cheerful partner, and a very large, goob-tastic dog

Little, Todd D. 2013. Longitudinal Structural Equation Modeling. New York, NY: Guilford Press.

Nye, Christopher D., and Fritz Drasgow. 2011. “Effect Size Indices for Analyses of Measurement Equivalence: Understanding the Practical Importance of Differences Between Groups.” Journal of Applied Psychology 96 (5): 966–80. https://doi.org/10.1037/a0022955.

Sakaluk, John K., Alexandra N. Fisher, and Robyn E. Kilshaw. 2021. “Dyadic Measurement Invariance and Its Importance for Replicability in Romantic Relationship Science.” Personal Relationships 28 (1): 190–226. https://doi.org/10.1111/pere.12341.

Tabachnick, Barbara G., and Linda S. Fidell. 2012. Using Multivariate Statistics. 6th ed. New York, NY: Pearson.

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1. Todd was just finishing his now go-to book on Longitudinal SEM (Little (2013)) when I took his Longitudinal SEM class. Doing the proof-reading and code-testing for one of his book’s chapters was a legitimate final project option for that course–a possibility I look back on with some sardonic amusement, now that I am writing a book of my own and have my own graduate statistics classes.

2. Emily’s lab used to call this “Johnspeak”; you can credit (or blame) my late Grandmother, who was a powerfully idiomatic speaker