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2021 | OriginalPaper | Buchkapitel

Modelling Human Intelligence Using Mixed Model Approach

verfasst von : Thanigaivasan Gokul, Mamandur Rangaswamy Srinivasan, Michele Gallo

Erschienen in: Data Science and Social Research II

Verlag: Springer International Publishing

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Abstract

In many psychometric studies, the observations may be often on longitudinal outcomes pertaining to General (G) and Specific (S) factors of human intelligence along with other covariates. Modelling human intelligence under Generalized Linear Mixed Model (GLMM) framework received the attention of psychologists in understanding the variables associated with the outcomes. In this paper, we formulate (i) a suitable GLMM model for count data of human intelligence factors and (ii) further examine the association between the outcome variables of Spearman’s G and S factors of human intelligence using joint longitudinal modelling along with other covariates based on school lunch intervention data.

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Bilker, W. B., Hansen, J. A., Brensinger, C. M., Richard, J., Raquel, E., & Gur, R. C. (2012). Development of abbreviated nine-item forms of the Raven’s standard progressive matrices test. HHS Public Access, 19(3), 354–369.

Breslow, N. E., & Clayton, D. G. (1993). Approximate inference in generalized linear mixed models. Journal of the American Statistical Association, 88, 9–25.MATH

Cavanaugh, J. E., & Neath, A. A. (2019). The Akaike information criterion: Background, derivation, properties, application, interpretation, and refinements. Wiley Interdisciplinary Reviews: Computational Statistics, 1460, 1–11.MathSciNet

Chakraborty, H., Helms, R., Sen, P., & Cohen, M. (2003). Estimating correlation by using a general linear mixed model: Evaluation of the relationship between the concentration of HIV- RNA in blood and semen. Statistics in Medicine, 22, 1457–1464.CrossRef

Coxe, S., West, S. G., & Aiken, L. S. (2009). The analysis of count data: A gentle introduction to poisson regression and its alternatives. Journal of Personality Assessment, 91(2), 121–136.CrossRef

Coyle, T. R., Elpers, K. E., Gonzalez, M. C., Freeman, J., & Baggio, J. A. (2018). General intelligence (g), ACT scores, and theory of mind: (ACT)g predicts limited variance among theory of mind tests. Intelligence, 71, 85–91.CrossRef

Efendi, A., Molenberghs, G., Njagi, N. E., & Dendale, P. (2013). A joint model for longitudinal continuous and time-to-event outcomes with direct marginal interpretation. Biometrical Journal, 55(4), 572–588.MathSciNetCrossRef

Fallah, N., Mitnitski, A., & Rockwood, K. (2011). Applying neural network Poisson regression to predict cognitive score changes. Journal of Applied Statistics, 38(9), 2051–2062.MathSciNetCrossRef

Fieuws, S., & Verbeke, G. (2006). Pairwise fitting of mixed models for the joint modeling of multivariate longitudinal profiles. Biometrics, 62, 424–431.MathSciNetCrossRef

Goharpey, N., Crewther, D. P., & Crewther, S. G. (2013). Research in developmental disabilities problem solving ability in children with intellectual disability as measured by the Raven’s colored progressive matrices. Research in Developmental Disabilities, 34(12), 4366–4374.CrossRef

Graßhoff, U., Holling, H., & Schwabe, R. (2016). Optimal design for the rasch poisson-gamma model. In Kunert J., Müller C. H., & Atkinson A. C. (eds.), mODa 11—Advances in model-oriented design and analysis, pp. 133–141. Springer.

Greenhalgh, T., Kristjansson, E., & Robinson, V. (2007). Realist review to understand the efficacy of school feeding programmes. BMJ, 335(7625), 858.CrossRef

Gueorguieva, R. (2001). A multivariate generalized linear mixed model for joint modelling of clustered outcomes in the exponential family. Statistical Modeling, 1, 177–193.CrossRef

Horrocks, J., & Van Den Heuvel, M. J. (2009). Prediction of pregnancy: A joint model for longitudinal and binary data. Bayesian Analysis, 4, 523–538.MathSciNetCrossRef

Iddi, S., & Molenberghs, G. (2012). A joint marginalized multilevel model for continuous and binary longitudinal outcomes. Journal of Applied Statistics, 39(11), 2413–2430.MathSciNetCrossRef

Ivanova, A., Molenberghs, G., & Verbeke, G. (2016). Mixed models approaches for joint modelling of different types of responses. Journal of Biopharmaceutical Statistics, 26(4), 601–618.CrossRef

Kristjansson, B., Petticrew, M., MacDonald, B., Krasevec, J., Janzen, L., Greenhalgh, T., Wells, G, A., MacGowan, J., Farmer, A. P., Shea, B., Mayhew, A., Tugwell, P., & Welch, V. (2007). School feeding for improving the physical and psychosocial health of disadvantaged students. Cochrane Database of Systematic Reviews, 24(1), Art. No.: CD004676.

Laird, N., & Ware, J. (1982). Random-effects models for longitudinal data. Biometrics, 38, 963–974.CrossRef

Li, N., Elashofi, R. M., Li, G., & Saver, J. (2010). Joint modeling of longitudinal ordinal data and competing risks survival times and analysis of the NINDS rt-PA stroke trial. Statistics in Medicine, 29, 546–557.MathSciNetCrossRef

Martínez-Plumed, F., Ferri, C., Hernández-Orallo, J., & Ramírez-Quintana, M. J. (2017). A computational analysis of general intelligence tests for evaluating cognitive development. Cognitive Systems Research, 43, 100–118.CrossRef

Mohanraj, J., & Srinivasan, M. R. (2015). Selection of the best covariance structure in longitudinal data based on bootstrap. Assam Statistical Review, 29(1), 1–29.

Molenberghs, G., & Verbeke, G. (2005). Models for discrete longitudinal data. New York: Springer.MATH

Muniz, M., Gomes, C. M. A., & Pasian, S. R. (2016). Factor structure of Raven’s coloured progressive matrices. Psico-USF, 21(2), 259–272.CrossRef

Murphy, S. P., & Allen, L. H. (2003). Nutritional importance of animal source foods. The Journal of Nutrition, 133(11), 3932S-3935S.CrossRef

Neumann, C. G., Bwibo, N. O., Murphy, S. P., Sigman, M., Whaley, S., Allen, L. H., et al. (2003). Animal source foods improve dietary quality, micronutrient status, growth and cognitive function in Kenyan school children: Background, study design and baseline findings. The Journal of Nutrition, 133, 3941S-3949S.CrossRef

Njagi, N. E., Molenberghs, G., Verbeke, G., Kenward, M. G., Dendale, P., & Willekens, K. (2013). A exible joint-modelling framework for longitudinal and time-to-event data with overdispersion. Statistical Methods in Medical Research, 25(4), 1661–1676.CrossRef

Palestro, J. J., Bahg, G., Sederberg, P. B., Lu, Z.-L., Steyvers, M., & Turner, B. M. (2018). A tutorial on joint models of neural and behavioral measures of cognition. Journal of Mathematical Psychology, 84, 20–48.MathSciNetCrossRef

Pope, L., Roche, E., Morgan, C. B., & Kolodinsky, J. (2018). Sampling tomorrow lunch today: Examining the effect of sampling a vegetable-focused entrée on school lunch participation, a pilot study. Preventive Medicine Reports, 12, 152–157.CrossRef

Sakamoto, W. (2019). Inference on variance components near boundary in linear mixed effect models. WIREs Computational Statistics, 1466, 1–10.

Siekmann, J. H., Allen, L. H., Bwibo, N. O., Demment, M. W., Murphy, S. P., & Neumann, C. G. (2003). Kenyan school children have multiple micronutrient deficiencies, but increased plasma vitamin B-12 is the only detectable micronutrient response to meat or milk supplementation. The Journal of Nutrition, 133(11), 3972–3980.CrossRef

Thifiebaut, R., Jacqmin-Gadda, H., Chene, G., Leport, C., & Commenges, D. (2002). Bivariate linear mixed models using SAS PROC MIXED. Computer Methods and Programs in Biomedicine, 69, 249–256.CrossRef

Tourva, A., Spanoudis, G., & Demetriou, A. (2016). Intelligence cognitive correlates of developing intelligence: The contribution of working memory, processing speed and attention. Intelligence, 54, 136–146.CrossRef

Tsiatis, A., & Davidian, M. (2004). Joint modeling of longitudinal and time-to-event data: An overview. Statistica Sinica, 14, 809–834.MathSciNetMATH

Walingo, M. K., & Musamali, B. (2008). Nutrient intake and nutritional status indicators of participant and nonparticipant pupils of a parent-supported school lunch program in Kenya. Journal of Nutrition Education and Behavior, 40(5), 298–304.CrossRef

Titel: Modelling Human Intelligence Using Mixed Model Approach
verfasst von: Thanigaivasan Gokul
Mamandur Rangaswamy Srinivasan
Michele Gallo
Verlag: Springer International Publishing
Buch: Data Science and Social Research II
Print ISBN: 978-3-030-51221-7

Electronic ISBN: 978-3-030-51222-4

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-51222-4_16

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