(Note. I’ve made several similar posts with a similar message on several social media outlets over the last 1.5 years)
Yes. This may be seen as a brag post (I plead the fifth). But, I really want (need?) to share this recent publication (January 2023). Why? Because, after 40 years of scholarship, I consider this article (which is open access and can be downloaded and read freely) to be one of my 5 top peer-reviewed research publications. The article is part of a special issue (Assessment of Human Intelligence-State of the Art in the 2020s) of the Journal of Intelligence, edited by Alan Kaufman et al. Warning—it is a long article. The article is the result of collaboration with Joel Schneider, Scott Decker and Okan Bulut.
The content of the article pushes the “edge of the envelop” regarding intelligence theories and testing via the use of exploratory psychometric network analysis (PNA) within the context of network non-g (i.e., psychometric g) models of intelligence. This approach represents an emerging paradigm shift for thinking about intelligence theories and testing. As stated by Savi et al. (2021) "factor analysis models dominated the 20th century of intelligence research, but network models will dominate the 21st." I believe Savi et al. are more-or-less correct. I believe PNA and non-g network models can move intelligence theories and testing forward—as they have become stagnant via the repeated use of "common cause" descriptive and taxonomic-generating factor analysis methods. Used in isolation, factor analysis-based intelligence test and theory models constrain school psychologists and other assessment professionals from moving forward (as described in the paper). For far too long, especially in school psychology, we have been "stuck on g" factor analysis based models of test interpretation.
As stated in our article, "newer non-g emergent property theories of intelligence might lead to better intervention research for individuals who have been marginalized by society. Holden and Hart (2021) suggest that network-based non-g theories, particularly those that feature Gwm-AC mechanisms [the working memory-attentional control complex] (process overlap theory in particular) may hold promise as a vehicle for improving, and not harming, social justice and equity practices and valued outcomes for individuals in marginalized groups" (McGrew et al., 2023). Read the original Holden and Hart article if you are interested in the social justice implications of a new way of thinking about intelligence grounded in modern network non-g conceptualizations of intelligence.
Even if the methodological material is not your cup of tea, much of the McGrew et al. (2023) introduction is relevant to assessment practitioners. Also, several sections in the discussion deal with practical implications for understanding new insights into intelligence theories, broad cluster test interpretation in general, and some strengths and weaknesses of the WJ IV CHC test and cluster scores.
If you are not familiar with the Journal of Intelligence (JOI), I would suggest SPs take a look. It is not the Intelligence journal from ISIR. It is the "new kid on the block" and has quickly become a prestigious open access publication outlet with a top notch editorial board. Since it is open access, all articles can be downloaded, read, and shared freely—an awesome free source of emerging thinking in the field of intelligence. JOI is publishing interesting articles from a wide variety of perspectives by a diversity of scholars interested in intelligence, cognition, and related topics. It has become one of my favorite journals the past few years.
Finally, exploratory hierarchical psychometric network analysis methods (along with traditional structural analysis methods) were applied to the WJ V norm data—these results will be in the WJ V Technical Manual (LaForte, Dailey, McGrew, 2025).
My WJ IV conflict of interest (COI) is included in the linked PDF article. My WJ V COI and additional COI information can be found at the MindHub web portal.
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