Gv (visual-spatial) abilities are important: STEM related research

Despite the finding that Gv tests on major individually administered IQ tests consistently fail to demonstrate strong correlations with standardized achievement tests, clinical experience and other research continues to indicate that strong Gv abilities are related to performance in complex higher-level areas of science, technology, engineering and mathematics.  We in applied IQ test development just need to develop better measures of these specific Gv abilities or recognize that our current dependent achievement test variables fail to tap these domains of expertise.  More from Lubinski on this topic.

Lubinski, D. (2010). Spatial ability and STEM: A sleeping giant for talent identification and development.
Personality and Individual Differences, 49(4), 344-351. (click here to read more)

Spatial ability is a powerful systematic source of individual differences that has been neglected in complex learning and work settings; it has also been neglected in modeling the development of expertise and creative accomplishments. Nevertheless, over 50 years of longitudinal research documents the important role that spatial ability plays in educational and occupational settings wherein sophisticated reasoning with figures, patterns, and shapes is essential. Given the contemporary push for developing STEM (science, technology, engineering, and mathematics) talent in the information age, an opportunity is available to highlight the psychological significance of spatial ability. Doing so is likely to inform research on aptitude-by-treatment interactions and Underwood’s (1975) idea to utilize individual differences as a crucible for theory construction. Incorporating spatial ability in talent identification procedures for advanced learning opportunities uncovers an under-utilized pool of talent for meeting the complex needs of an ever-growing technological world; furthermore, selecting students for advanced learning opportunities in STEM without considering spatial ability might be iatrogenic.
Article Outline

1. Spatial ability and STEM: decades of longitudinal research
2. Intellectually precocious youth
3. Discussion
4. Broader Psychological Implications

Technorati Tags: Psychology, school psychology, developmental psychology, educational psychology, forensic psychology, neuropsychology, special education, intelligence, cognitive abilities, cognition, intelligence theories, CHC theory, CHC, Cattell-Horn-Carroll, intelligence, cognition, IQ, IQ tests, Gv, visual-spatial, visual processing, STEM, science, engineering, technology, mathematics

Quantoids corner: Intro to hierarchical linear modeling (HLM)

I LOVE it when more applied journals publish articles where complex statistical methods are presented to a less statistically oriented audience, as I often find these "quanatoid explanations for dummies" an excellent introduction to complex statistical methods.  Today I discovered that Gifted Child Quarterly has published a brief two-part series of articles that provide a nice introduction to HLM.  I've never run HLM models, so I found the introduction very helpful.  So much so that I might run some HLM on some appropriate datasets I have just to see it work.

Below are the two articles.  Enjoy.  Kudos to GCQ and Dr. McCoach.

McCoach, D. B. & Adelson, J. L.  Dealing with dependence (Part 1):  Understanding the effects of clustered data.  Gifted Child Quarterly, 54(2), 152-155.

This article provides a conceptual introduction to the issues surrounding the analysis of clustered (nested) data. We define the intraclass correlation coefficient (ICC) and the design effect, and we explain their effect on the standard error. When the ICC is greater than 0, then the design effect is greater than 1. In such a scenario, the standard error produced under the assumption of independence is underestimated. This increases the Type I error rate. We provide a short illustration of the effect of non-independence on the standard error. We show that after accounting for the design effect, our decision about the statistical significance of the test statistic changes. When we fail to account for the clustered nature of the data, we conclude that the difference between the two groups is statistically significant. However, once we adjust the standard error for the design effect, the difference is no longer statistically significant.

McCoach, D. B. (2010). Dealing With Dependence (Part II): A Gentle Introduction to Hierarchical Linear
Modeling. Gifted Child Quarterly, 54(3), 252-256.

In education, most naturally occurring data are clustered within contexts. Students are clustered within classrooms, classrooms are clustered within schools, and schools are clustered within districts. When people are clustered within naturally occurring organizational units such as schools, classrooms, or districts, the responses of people from the same cluster are likely to exhibit some degree of relatedness with each other. The use of hierarchical linear modeling allows researchers to adjust for and model this non-independence. Furthermore, it may be of great substantive interest to try to understand the degree to which people from the same cluster are similar to each other and then to try to identify variables that help us to understand differences both within and across clusters. In HLM, we endeavor to understand and explain between- and within-cluster variability of an outcome variable of interest. We can also use predictors at both the individual level (level 1), and the contextual level (level 2) to explain the variance in the dependent variable. This article presents a simple example using a real data set and walk through the interpretation of a simple hierarchical linear model to illustrate the utility of the technique.

Technorati Tags: Psychology, school psychology, developmental psychology, educational psychology, forensic psychology, neuropsychology, special education, intelligence, cognitive abilities, cognition, intelligence theories, statistics, HLM, hierarchical linear modeling, gifted, Gifted Child Quarterly, quatoids corner