A Tribute to Wayne Velicer
Wayne Velicer was a giant among quantitative psychologists and health researchers, among other groups. I was very fortunate to be able to call him a colleague and good friend. Sadly, he died too young, in October 2017.
The journal Multivariate Behavioral Research has just published online-before-print a tribute to Wayne. Here’s the reference:
(2020) A Tribute to the Mind, Methodology and Mentoring of Wayne Velicer, Multivariate Behavioral Research,
Here’s the abstract:
Wayne Velicer is remembered for a mind where mathematical concepts and calculations intrigued him, behavioral science beckoned him, and people fascinated him. Born in Green Bay, Wisconsin on March 4, 1944, he was raised on a farm, although early influences extended far beyond that beginning. His Mathematics BS and Psychology minor at Wisconsin State University in Oshkosh, and his PhD in Quantitative Psychology from Purdue led him to a fruitful and far-reaching career. He was honored several times as a high-impact author, was a renowned scholar in quantitative and health psychology, and had more than 300 scholarly publications and 54,000+ citations of his work, advancing the arenas of quantitative methodology and behavioral health. In his methodological work, Velicer sought out ways to measure, synthesize, categorize, and assess people and constructs across behaviors and time, largely through principal components analysis, time series, and cluster analysis. Further, he and several colleagues developed a method called Testing Theory-based Quantitative Predictions, successfully applied to predicting outcomes and effect sizes in smoking cessation, diet behavior, and sun protection, with the potential for wider applications. With $60,000,000 in external funding, Velicer also helped engage a large cadre of students and other colleagues to study methodological models for a myriad of health behaviors in a widely applied Transtheoretical Model of Change. Unwittingly, he has engendered indelible memories and gratitude to all who crossed his path. Although Wayne Velicer left this world on October 15, 2017 after battling an aggressive cancer, he is still very present among us.
I’m one of 17 of his colleagues who contributed a short tribute. Here’s mine:
Geoff Cumming wrote:
Way back, Wayne invited me to visit his lab. I gave a talk about the iniquities of NHST and p values, and the benefits of confidence intervals. At once it was clear that we shared many views. Wayne enthusiastically brainstormed about what estimation could do in his research. On the spot, he invited me to help develop a paper using confidence intervals to evaluate one of his multi-variable quantitative models. Over several years we exchanged drafts; it was exciting for me to work with such a creative, energetic, and distinguished colleague. The paper appeared in Applied Psychology: An International Review, and has provoked much comment.
My first experience of Wayne as food and wine buff, and wonderfully generous host, occurred when my wife and I visited R.I. while driving an old RV around the U.S. Thereafter, memorable meals—at venues selected by Wayne the expert—became, for me, highlights of American Psychological Association Conventions.
Wayne often mentioned that he loved visiting Australia, where he had close research colleagues, although we managed to meet up here only once. Besides his enduring friendship and expansive hospitality, I remember most warmly his ability to ruffle scientific feathers to such creative and positive effect.
Using CIs to Assess the Quantitative Predictions Made by a Multivariable Model
The paper I mentioned above (reference below) includes this figure:
This figure also appears in my first book, where I discussed this example on pp. 426-7.
Velicer and colleagues chose omega2 (vertical axis above) as the main ES, an estimate of the proportion of total variance in smoking status–a measure of a person’s position on the spectrum from regular smoker to successful quitter–attributable to each of a number of predictor variables (horizontal axis) of their Transtheoretical Model of behaviour change. The grey dots mark the model’s quantitative predictions; the short horizontal lines mark estimates from a large data set, with 95% CIs. The predictions fall within the CIs for 11 of the 15 variables, which we interpreted as strong support for most aspects of the model. Because the discrepancies between predictions and data are quantitative, we could examine each and decide whether to modify an aspect of the model, or await further empirical testing. We discussed our test of the Transtheoretical Model more broadly as an illustration of the value of CIs for model fitting, and looked forward to the development of many more quantitative models in psychology. A focus on CIs then allows, as above, the evaluation of such models against new sets of data.
Velicer, W. F., Cumming, G., Fava, J. L., Rossi, J. S., Prochaska, J. O., & Johnson, J. (2008). Theory testing using quantitative predictions of effect size. Applied Psychology: An International Review, 57, 589-608. doi: 10.1111/j.1464-0597.2008.00348.x
I salute the memory and intellectual legacy of Wayne.