Multiple abstract variance analysis

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Short description: Statistical technique

Multiple abstract variance analysis (MAVA), is a statistical technique used to estimate the proportion of variance in a phenotypic trait due to genetic and environmental factors. It was developed by psychologist Raymond B. Cattell in order to enable the analysis of data from multiple independent sources to estimate the causes of trait variation. Cattell originally described the technique in a 1960 paper.[1][2] MAVA aims to estimate the relative genetic and environmental contributions to trait variation by comparing variances between families to those within families on the trait under study. As such, it is considered a "more systematic and comprehensive approach" than the classical correlation method of heritability estimation.[3] MAVA later formed the basis of Cattell's 16PF Questionnaire.[4]

MAVA has been criticized for inconsistencies between some of the mathematical equations used in the method,[5] and for introducing "...numerous new theoretical constructs without any clear empirical basis".[6] Critics of the method have also noted that it requires the introduction of additional parameters whenever a new type of genetic relationship is considered, which precludes a complete analysis of the causes of trait variation.[7]

References

  1. ↑ Vogler, George P.; Fulker, David W. (2013-11-11). "Human Behavior Genetics". in Nesselroade, John R. (in en). Handbook of Multivariate Experimental Psychology. Springer Science & Business Media. p. 480. ISBN 9781461308935. https://books.google.com/books?id=0kPuBwAAQBAJ. 
  2. ↑ Cattell, Raymond B. (1960). "The multiple abstract variance analysis equations and solutions: For nature-nurture research on continuous variables." (in en). Psychological Review 67 (6): 353–372. doi:10.1037/h0043487. ISSN 1939-1471. PMID 13691636. 
  3. ↑ Dempsey, P. J.; Townsend, G. C. (June 2001). "Genetic and environmental contributions to variation in human tooth size". Heredity 86 (6): 685–693. doi:10.1046/j.1365-2540.2001.00878.x. ISSN 0018-067X. PMID 11595049. 
  4. ↑ Wilson, Philip K. (2010-10-23). "The Cattell Controversy: Race, Science, and Ideology (review)" (in en). Journal of the History of Medicine and Allied Sciences 65 (4): 587–589. doi:10.1093/jhmas/jrq032. ISSN 1468-4373. https://muse.jhu.edu/article/399428. 
  5. ↑ Loehlin, John C. (1965). "Some methodological problems in Cattell's Multiple Abstract Variance Analysis.". Psychological Review 72 (2): 156–161. doi:10.1037/h0021706. ISSN 0033-295X. PMID 14282673. 
  6. ↑ Sullivan, Patrick F.; Eaves, Lindon J. (2002). "Evaluation of Analyses of Univariate Discrete Twin Data" (in en). Behavior Genetics 32 (3): 221–227. doi:10.1023/a:1016025229858. ISSN 0001-8244. PMID 12141783. 
  7. ↑ Eaves, L. J.; Last, Krystyna; Martin, N. G.; Jinks, J. L. (May 1977). "A progressive approach to non-additivity and genotype-environmental covariance in the analysis of human differences" (in en). British Journal of Mathematical and Statistical Psychology 30 (1): 1–42. doi:10.1111/j.2044-8317.1977.tb00722.x. ISSN 0007-1102. 

Further reading

  • Jinks, J. L.; Fulker, D. W. (1970). "Comparison of the biometrical genetical, MAVA, and classical approaches to the analysis of the human behavior." (in en). Psychological Bulletin 73 (5): 311–349. doi:10.1037/h0029135. ISSN 1939-1455. PMID 5528333. 



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