Saturday, May 19, 2018

The Relation between Intelligence and Adaptive Behavior: A Meta-Analysis 

Very important meta-analysis of AB IQ relation. Primary finding on target with prior informal synthesis by McGrew (2015)

The Relation between Intelligence and Adaptive Behavior: A Meta-Analysis   
 
Ryan M. Alexander 
 
ABSTRACT 
 
Intelligence tests and adaptive behavior scales measure vital aspects of the multidimensional nature of human functioning. Assessment of each is a required component in the diagnosis or identification of intellectual disability, and both are frequently used conjointly in the assessment and identification of other developmental disabilities. The present study investigated the population correlation between intelligence and adaptive behavior using psychometric meta-analysis. The main analysis included 148 samples with 16,468 participants overall. Following correction for sampling error, measurement error, and range departure, analysis resulted in an estimated population correlation of ρ = .51. Moderator analyses indicated that the relation between intelligence and adaptive behavior tended to decrease as IQ increased, was strongest for very young children, and varied by disability type, adaptive measure respondent, and IQ measure used. Additionally, curvilinear regression analysis of adaptive behavior composite scores onto full scale IQ scores from datasets used to report the correlation between the Wechsler Intelligence Scales for Children- Fifth edition and Vineland-II scores in the WISC-V manuals indicated a curvilinear relation—adaptive behavior scores had little relation with IQ scores below 50 (WISC-V scores do not go below 45), from which there was positive relation up until an IQ of approximately 100, at which point and beyond the relation flattened out. Practical implications of varying correlation magnitudes between intelligence and adaptive behavior are discussed (viz., how the size of the correlation affects eligibility rates for intellectual disability).
 
Other Key Findings Reported
 
McGrew (2012) augmented Harrison's data-set and conducted an informal analysis including a total of 60 correlations, describing the distributional characteristics observed in the literature regarding the relation. He concluded that a reasonable estimate of the correlation is approximately .50, but made no attempt to explore factors potentially influencing the strength of the relation.
 
Results from the present study corroborate the conclusions of Harrison (1987) and McGrew (2012) that the IQ/adaptive behavior relation is moderate, indicating distinct yet related constructs. The results showed indeed that the correlation is likely to be stronger at lower IQ levels—a trend that spans the entire ID range, not just the severe range. The estimated true mean population is .51, and study artifacts such as sampling error, measurement error, and range departure resulted in somewhat attenuated findings in individual studies (a difference of about .05 between observed and estimated true correlations overall).
 
 
The present study found the estimated true population mean correlation to be .51, meaning that adaptive behavior and intelligence share 26% common variance. In practical terms, this magnitude of relation suggests that an individual's IQ score and adaptive behavior composite score will not always be commensurate and will frequently diverge, and not by a trivial amount. Using the formula Ŷ = Ȳ + ρ (X - X ̅ ), where Ŷ is the predicted adaptive behavior composite score, Ȳ  is the mean adaptive behavior score in the population, ρ  is the correlation between adaptive behavior and intelligence, X is the observed IQ score for an individual, and X ̅ is the mean IQ score, and accounting for regression to the mean, the predicted adaptive behavior composite score corresponding to an IQ score of 70, given a correlation of .51, would be 85 —a score that is a full standard deviation above an adaptive behavior composite score of 70, the cut score recommended by some entities to meet ID eligibility requirements. With a correlation of .51, and accounting for regression to the mean, an IQ score of 41 would be needed in order to have a predicted adaptive behavior composite score of 70. Considering that approximately 85% of individuals with ID have reported IQ scores between 55 and 70±5 (Heflinger et al., 1987; Reschly, 1981), the eligibility implications, especially for those with less severe intellectual impairment, are alarming. In fact, derived from calculations by Lohman and Korb (2006), only 17% of individuals obtaining an IQ score of 70 or below would be expected to also obtain an adaptive behavior composite score of 70 or below when the correlation between the two is .50. 
 
 
The purpose of this study was to investigate the relation between IQ and adaptive behavior and variables moderating the relation using psychometric meta-analysis. The findings contributed in several ways to the current literature with regard to IQ and adaptive behavior. First, the estimated true mean population correlation between intelligence and adaptive behavior following correction for sampling error, measurement error, and range departure is moderate, indicating that intelligence and adaptive behavior are distinct, yet related, constructs. Second, IQ level has a moderating effect on the relation between IQ and adaptive behavior. The correlation is likely to be stronger at lower IQ levels, and weaker as IQ increases. Third, while not linear, age has an effect on the IQ/adaptive behavior relation. The population correlation is highest for very young children, and lowest for children between the ages of five and 12. Fourth, the magnitude of IQ/adaptive behavior correlations varies by disability type. The correlation is weakest for those without disability, and strongest for very young children with developmental delays. IQ/adaptive behavior correlations for those with ID are comparable to those with autism when not matched on IQ level. Fifth, the IQ/adaptive correlation when parents/caregivers serve as adaptive behavior respondents is comparable to when teachers act as respondents, but direct assessment of adaptive behavior results in a stronger correlation. Sixth, an individual's race does not significantly alter the correlation between IQ and adaptive behavior, but future research should evaluate the influence of race of the rater on adaptive behavior ratings. Seventh, the correlation between IQ and adaptive behavior varies depending on IQ measure used—the population correlation when Stanford-Binet scales are employed is significantly higher than when Wechsler scales are employed. And eighth, the correlation between IQ and adaptive behavior is not significantly different between adaptive behavior composite scores obtained from the Vineland, SIB, and ABAS families of adaptive behavior measures, which are among those that have been deemed appropriate for disability identification. Limitations of this study notwithstanding, it is the first to employ meta-analysis procedures and techniques to examine the correlation between intelligence and adaptive behavior and how moderators alter this relation. The results of this study provide information that can help guide practitioners, researchers, and policy makers with regard to the diagnosis or identification of intellectual and developmental disabilities.


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Tuesday, May 8, 2018

NEUROSCIENCE & SOCIETY: Ethics, Law, and Technology Confrence - Neuroethics & Law Blog

NEUROSCIENCE & SOCIETY: Ethics, Law, and Technology Confrence - Neuroethics & Law Blog

NEUROSCIENCE & SOCIETY: Ethics, Law, and Technology Confrence

NEUROSCIENCE & SOCIETY: Ethics, Law, and Technology
24-25 August 2018
Sydney, NSW, Australia

Advances in brain scanning and intervention technologies are transforming our ability to observe, explain, and influence human thought and behaviour. Potential applications of such technologies (e.g. brain-based pain detection in civil lawsuits, medications to help criminal offenders become less impulsive, prediction of future behaviour through neuroimaging) and their ethical, clinical, legal, and societal implications, fuel important debates in neuroethics. However, many factors beyond the brain – factors targeted by different emerging technologies – also influence human thought and behaviour. Sequencing the human genome and gene-editing technologies like CRISPR Cas-9 offer novel ways to explain and influence human thought and behaviour. Analysis of data about our offline and online lives (e.g. from fitness trackers, how we interact with our smartphone apps, and our social media posts and profiles) also provide striking insights into our psychology. Such intimate information can be used to predict and influence our behaviour, including through bespoke advertising for goods and services that more effectively exploits our psychology and political campaigns that sway election results. Although such methods often border on manipulation, they are both difficult to detect and potentially impossible to resist. The use of such information to guide the design of online environments, artifacts, and smart cities lies at the less nefarious – and potentially even socially useful and morally praiseworthy – end of the spectrum vis à vis the potential applications of such emerging "moral technologies".

At this year's Neuroscience & Society conference we will investigate the ethical, clinical, legal, and societal implications of a wide range of moral technologies that target factors beyond, as well as within, the brain, in order to observe, explain, and influence human thought and behaviour. Topics will include, but are not limited to:

  • cognitive and moral enhancement
  • neurolaw and neuro-evidence
  • brain-computer interfaces
  • neuro-advertising
  • neuromorphic engineering and computing
  • mental privacy and surveillance
  • social media and behaviour prediction/influence
  • implicit bias and priming
  • technological influences on human behaviour
  • nudging, environment and technology design, and human behaviour
  • artificial intelligence and machine learning
  • technology and the self
  • (neuro)technology and society

We invite abstracts from scholars, scientists, technology designers, policy-makers, practitioners, clinicians and graduate students, interested in presenting talks or posters on any of the above or related topics.

Abstracts of 300 words should be emailed to Cynthia Forlini <cynthia.forlini@sydney.edu.au> in Microsoft Word format by Thursday, 31 May 2018. Submissions will be peer reviewed, and authors of successful submissions will be notified via email by Friday, 15 June 2018.

In addition to keynote presentations (to be announced shortly), contributed talks, and a poster session, the conference program will also include three sessions on the following topics:

  • highlights from- and information about enhancements to the Australian Neurolaw Database
  • book symposium on Neuro-Interventions and The Law: Regulating Human Mental Capacity
  • panel on the topic of remorse
For enquiries about matters other than abstract submission, please email Adrian Carter <adrian.carter@monash.edu.au> or Jeanette Kennett <jeanette.kennett@mq.edu.au>
Neuroscience & Society is supported by the ARC Centre of Excellence for Integrative Brain Function Neuroethics Program, and the Centre for Agency Values and Ethics at Macquarie University.