• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1295-1309
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• 2016

This paper is to reproduce the result of Kim et al. (2014) by deriving a benchmark dose lower bound (BMDL) of lead based on the 2005 cohort data set of Children's Health and Environmental Research (CHEER) data set. The ADHD rating scales in the 2005 cohort were not consistent along the three follow-ups since two different ADHD rating scales were used in the cohort. We first unified the ADHD rating scales in the 2005 cohort by deriving a conversion formula using a penalized linear spline. We then constructed two linear mixed models for the 2005 cohort which reflected the longitudinal characteristics of the data set. The first model introduced the random intercept and the random slope terms and the second model assumed the first order autoregressive structure of the error term. Using these two models, we derived the BMDLs of lead and reconfirmed the "regression to the mean" nature of the ADHD score discovered by Kim et al. (2014). We also noticed that there was a definite difference between the sampling distributions of the two cohorts. As a result, taking this difference into account, we were able to obtain the consistent result with Kim et al. (2014).

• Journal of Gifted/Talented Education
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• v.15 no.1
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• pp.67-84
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• 2005

In this study we investigated the adequacy of tools for distinction gifted students through the comparison these mutual relation on the basis of data, like paper test, the depths interview score, and the rest data((TTCT: Torrance Tests of Creative Thinking, IQ test, FASP: Find A Shape Puzzle, V.T: Visualization Tests and Exp: experimental ability test), and analysis data of EEG test for examining the adequacy of tools for identification gifted students. So, we developed Brain Wave gifted Index(G-Index) for finding another distinction ability as using brain waves data. The standard of index development use gifted brain characteristic in closed-eyes rest state which is judged like that characteristic of distinction between gifted and normal students is the most clear and consistence. That is, the degree of unified pattern between each object and gifted PCA pattern was defined by Pearson method which added spatial mutual index to weight concept. This refer to mean number of spatial PCA pattern. Searching for the possibility of distinction gifted gave distinction effect in 76%. The result of regression analysis on the basis of mutual relation between the rest data is . The probability formula for distinct gifted group is as follow. $$P=\frac 1{1+e^{-[-0.018(TTCT)+0.057(IQ)+1.916(FASP)+0.682(V.T)+0.088(Exp.)+0.034(G-Index)-57.510]}}$$ The result of this calculation showed that probability for distinct in gifted group was very good(95.0%). On the basis of upper result, tools for identification gifted students should be estimated as using many-sided estimation data whatever possible. And following study about development, and operation of tools for distinction suitable to gifted student in science should be progressed.