Literature DB >> 19633945

Combining nonlinear biometric and psychometric models of cognitive abilities.

Elliot M Tucker-Drob1, K Paige Harden, Eric Turkheimer.   

Abstract

It is well-established that genetic factors account for large proportions of individual differences in multiple cognitive abilities. It is also well-established that individual differences in performance on many different cognitive ability measures are strongly correlated. Recent empirical investigations, however, have suggested two interesting qualifications to these well-established findings: Genetic variance in cognitive abilities is higher in richer home environments (gene-by-environment interaction), and common variance in different cognitive abilities is lower at higher levels of overall ability (nonlinear factor structure). Although they have been investigated independently, these two phenomena may interact, because richer environments are routinely associated with higher ability levels. Using simulation we demonstrate how un-modeled nonlinear factor structure can obscure interpretation of gene-by-environment interaction. We then reanalyze data from the National Collaborative Perinatal Project, previously used by Turkheimer et al. (2003; Psychol Science), with a two-step method to model both phenomena.

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Year:  2009        PMID: 19633945      PMCID: PMC2903767          DOI: 10.1007/s10519-009-9288-6

Source DB:  PubMed          Journal:  Behav Genet        ISSN: 0001-8244            Impact factor:   2.805


  17 in total

1.  Comparative longitudinal structural analyses of the growth and decline of multiple intellectual abilities over the life span.

Authors:  John J McArdle; Emilio Ferrer-Caja; Fumiaki Hamagami; Richard W Woodcock
Journal:  Dev Psychol       Date:  2002-01

2.  Executive functioning as a potential mediator of age-related cognitive decline in normal adults.

Authors:  Timothy A Salthouse; Thomas M Atkinson; Diane E Berish
Journal:  J Exp Psychol Gen       Date:  2003-12

3.  Variance components models for gene-environment interaction in twin analysis.

Authors:  Shaun Purcell
Journal:  Twin Res       Date:  2002-12

4.  Markov Chain Monte Carlo approaches to analysis of genetic and environmental components of human developmental change and G x E interaction.

Authors:  Lindon Eaves; Alaattin Erkanli
Journal:  Behav Genet       Date:  2003-05       Impact factor: 2.805

5.  Transformations in the couplings among intellectual abilities and constituent cognitive processes across the life span.

Authors:  Shu-Chen Li; Ulman Lindenberger; Bernhard Hommel; Gisa Aschersleben; Wolfgang Prinz; Paul B Baltes
Journal:  Psychol Sci       Date:  2004-03

6.  Working memory and intelligence: the same or different constructs?

Authors:  Phillip L Ackerman; Margaret E Beier; Mary O Boyle
Journal:  Psychol Bull       Date:  2005-01       Impact factor: 17.737

7.  The role of nonlinear factor-to-indicator relationships in tests of measurement equivalence.

Authors:  Daniel J Bauer
Journal:  Psychol Methods       Date:  2005-09

8.  Adult age trends in the relations among cognitive abilities.

Authors:  Elliot M Tucker-Drob; Timothy A Salthouse
Journal:  Psychol Aging       Date:  2008-06

9.  Assessing spurious "moderator effects": Illustrated substantively with the hypothesized ("synergistic") relation between spatial and mathematical ability.

Authors:  D Lubinski; L G Humphreys
Journal:  Psychol Bull       Date:  1990-05       Impact factor: 17.737

10.  The effect of assumptions about parental assortative mating and genotype-income correlation on estimates of genotype-environment interaction in the National Merit Twin Study.

Authors:  John C Loehlin; K Paige Harden; Eric Turkheimer
Journal:  Behav Genet       Date:  2008-12-27       Impact factor: 2.805

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  5 in total

1.  Detecting specific genotype by environment interactions using marginal maximum likelihood estimation in the classical twin design.

Authors:  Dylan Molenaar; Sophie van der Sluis; Dorret I Boomsma; Conor V Dolan
Journal:  Behav Genet       Date:  2011-12-07       Impact factor: 2.805

2.  Intellectual interest mediates gene × socioeconomic status interaction on adolescent academic achievement.

Authors:  Elliot M Tucker-Drob; K Paige Harden
Journal:  Child Dev       Date:  2012-01-30

3.  The Economics of Human Development and Social Mobility.

Authors:  James J Heckman; Stefano Mosso
Journal:  Annu Rev Econom       Date:  2014-08

4.  Replication of a gene-environment interaction Via Multimodel inference: additive-genetic variance in adolescents' general cognitive ability increases with family-of-origin socioeconomic status.

Authors:  Robert M Kirkpatrick; Matt McGue; William G Iacono
Journal:  Behav Genet       Date:  2014-12-25       Impact factor: 2.805

5.  Mathematical Ability and Socio-Economic Background: IRT Modeling to Estimate Genotype by Environment Interaction.

Authors:  Inga Schwabe; Dorret I Boomsma; Stéphanie M van den Berg
Journal:  Twin Res Hum Genet       Date:  2017-11-06       Impact factor: 1.587

  5 in total

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