Beyond IQ: Mining the “no-mans-land” between Intelligence and IQ: Journal of Intelligence special issue

I am pleased to see the Journal of Intelligence addressing the integration of non-cognitive variables (personality; self-beliefs; motivational constructs; often called the “no-mans land” between intelligence and personality— I believe this catchy phrase was first used by Stankov) with intellectual constructs to better understanding human performance. I have had a long-standing interest in such comprehensive models as reflected by my articulation of the Model of Academic Competence and Motivation (MACM) and repeated posting of “beyond IQ” information at my blogs.

Joel Schneider and I briefly touched in this topic in our soon to be published CHC intelligence theory update chapter. Below is the select text and some awesome figures crafted by Joel.

Our simplified conceptual structure of knowledge abilities is presented in Figure 3.10. At the center of overlapping knowledge domains is general knowledge—knowledge and skills considered important for any member of the population to know (e.g., literacy, numeracy, self-care, budgeting, civics, etiquette, and much more). The bulk of each knowledge domain is the province of specialists, but some portion is considered important for all members of society to know. Drawing inspiration from F. L. Schmidt (2011, 2014), we posit that interests and experience drive acquisition of domain-specific knowledge.

In Schmidt's model, individual differences in general knowledge are driven largely by individual differences in fluid intelligence and general interest in learning, also known as typical intellectual engagement (Goff & Ackerman, 1992). In contrast, individual differences in domain-specific knowledge are more driven by domain-specific in-terests, and also by the “tilt” of one's specific abilities (Coyle, Purcell, Snyder, & Richmond, 2014; Pässler, Beinicke, & Hell, 2015). In Figure 3.11, we present a simplified hypothetical synthesis of several ability models in which abilities, interests, and personality traits predict general and specific knowledge (Ackerman, 1996a, 1996b, 2000; Ackerman, Bowen, Beier, & Kanfer, 2001; Ackerman & Heggestad, 1997; Ackerman & Rolfhus, 1999; Fry & Hale, 1996; Goff & Ackerman, 1992; Kail, 2007; Kane et al., 2004; Rolfhus & Ackerman, 1999; Schmidt, 2011, 2014; Schneider et al., 2016; Schneider & Newman, 2015; Woodcock, 1993; Ziegler, Danay, Heene, Asendorpf, & Bühner, 2012).

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Can emotional intelligence (Gei) be trained: A meta-analysis

Can emotional intelligence be trained? A meta-analysis

Please cite this article as: Mattingly, V., Human Resource Management Review (2018), https://doi.org/10.1016/j.hrmr.2018.03.002

Victoria Mattingly, Kurt Kraiger

Keywords: Emotional intelligence, Training Meta-analysis

A B S T R A C T

Human resource practitioners place value on selecting and training a more emotionally in-telligent workforce. Despite this, research has yet to systematically investigate whether emo-tional intelligence can in fact be trained. This study addresses this question by conducting a meta-analysis to assess the effect of training on emotional intelligence, and whether effects are mod-erated by substantive and methodological moderators. We identified a total of 58 published and unpublished studies that included an emotional intelligence training program using either a pre-post or treatment-control design. We calculated Cohen's d to estimate the effect of formal training on emotional intelligence scores. The results showed a moderate positive effect for training, regardless of design. Effect sizes were larger for published studies than dissertations. Effect sizes were relatively robust over gender of participants, and type of EI measure (ability v. mix-edmodel). Further, our effect sizes are in line with other meta-analytic studies of competency-based training programs. Implications for practice and future research on EI training are discussed.

See prior Gei posts here and here.


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AJT CHC Intelligence Test launch in Jakarta -measure of 9 broad CHC abilities

Yesterday’s AJT CHC cognitive test launch in Jakarta was a big success. I was taken aback by the special “event” flavor. Extremely professional. As I’ve stated before, the AJT is based on an Indonesia norm sample of 4,800 and will be one if the most comprehensive intelligence tests in the world (on par with the WJ IV COG). It measures 9 broad CHC domains (Gf, Gc, Gwm, Ga, Gv, Gs, Gl, Gr, and some of Gp-separate from cognitive). This has been the most personally rewarding and important project I have worked on in my 40+ years in psychology and education. It is bringing the core concept of individual differences to the education system of the fourth largest country in the world.

George and Laurel Tahija (see picture below), and their YDB foundation, are the visionaries behind this project and other projects focused on trying to help unique learners in their country. In my five years on this project I can say that I’ve never worked with so many nice people . It was a grand effort by many. I am very impressed how together we built such a comprehensive and technically sound battery of tests from scratch. I have developed a fondness for Indonesia and the people of this wonderful country. The genuine warmth and enthusiasm of the participants was personally moving.

For more information check out these two links (one; two)

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The importance of differential psychology for school learning: 90% of school achievement variance is due to student characteristics

This is why the study of individual differences/differential psychology is so important. If you don’t want to read the article you can watch a video of Dr. Detterman where he summarizes his thinking and this paper.

Education and Intelligence: Pity the Poor Teacher because Student Characteristics are more Significant than Teachers or Schools. Article link.

Douglas K. Detterman

Case Western Reserve University (USA)

Abstract

Education has not changed from the beginning of recorded history. The problem is that focus has been on schools and teachers and not students. Here is a simple thought experiment with two conditions: 1) 50 teachers are assigned by their teaching quality to randomly composed classes of 20 students, 2) 50 classes of 20 each are composed by selecting the most able students to fill each class in order and teachers are assigned randomly to classes. In condition 1, teaching ability of each teacher and in condition 2, mean ability level of students in each class is correlated with average gain over the course of instruction. Educational gain will be best predicted by student abilities (up to r = 0.95) and much less by teachers' skill (up to r = 0.32). I argue that seemingly immutable education will not change until we fully understand students and particularly human intelligence. Over the last 50 years in developed countries, evidence has accumulated that only about 10% of school achievement can be attributed to schools and teachers while the remaining 90% is due to characteristics associated with students. Teachers account for from 1% to 7% of total variance at every level of education. For students, intelligence accounts for much of the 90% of variance associated with learning gains. This evidence is reviewed


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CHC intelligence theory update: Live chat or later YouTube viewing from #pscyhedpodcast this Sunday evening

I am looking forward to talking about the Cattell-Horn-Carroll (CHC) model of intelligence on the #psychedpodcast this sunday evening.

I will present material largely based on the forthcoming CHC chapter coauthored with Dr. Joel Schneider.  Tune it....it shall be fun. Or, watch the discussion later on YouTube, and eventually as an audio podcast on iTunes

BB (blatant brag): McGrew CHC 2009 article in Intelligence #1 (2008-2015) and top #10 all time

This was a pleasant surprise. I knew my 2009 Intelligence article was cited frequently but I never knew it was number one from 2008-2015 and it made the top 10 all time list for the journal Intelligence. I believe this is a reflection of the impact the CHC taxonomy has had. This should make my mom proud. Here is a link to the original article.

Bibliometric analysis across eight years 2008–2015 of Intelligence articles: An updating of Wicherts (2009). Article link.

Bryan J. Pesta

Abstract

I update and expand upon Wicherts' (2009) editorial in Intelligence. He reported citation counts of papers pub-lished in this journal from 1977 to 2007. All these papers are now at least a decade old, and many more new articles have been published since Wichert's analysis. An updated study is needed to help (1) quantify the journal's more recent impact on the scientific study of intelligence, and (2) alert researchers and educators to highly-cited articles; especially newer ones. Thus, I conducted a bibliometric analysis of all articles published here from 2008 to 2015. Data sources included both the Web of Science (WOS), and Google Scholar (GS). The eight-year set comprised 619 articles, published by 1897 authors. The average article had 17.0 (WOS), and 32.9 (GS) citations overall (2.75, and 5.33 citations per year, respectively). These metrics compare favorably with those from other psychology journals. In addition, a list of the most prolific authors is provided. Also reported is a list showing many articles in this set with counts greater than one hundred, and an updated top 25 list for the history of this journal.


“Also noteworthy is that nine of the articles in the old list (not shown here) dropped off the new list. Of their replacements, only three of the nine were published within the last decade: Deary, Strand, Smith, and Fernandes (2007); McGrew (2009), and Strenze (2007). The McGrew (2009) paper is again notable. It is the only article in my newer set (2008–2015) to make the all-time list. The paper ranks ninth on the all-time list with 281 citations, just eight years after being published.”


More recent Google Scholar citation info indicates that the article is still going strong from 2016-2017.

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Research Byte: Improving time processing ability in children with disabilities

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An interesting study demonstrating that it may be possible to improve the time processing abilities of children with various disabilities. Given that time processing abilities have been implicated in certain key cognitive functions (working memory, attentional control, executive functions) this study is intriguing. I am particularly interested in learning more about the time processing ability measures and the potential to use them in future intelligence test batteries...as well as where such temporal abilities fit in the CHC model of cognitive abilities.

 

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