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İngiliz Dili ve Edebiyatı Bölümü Yayın Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/419

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Browsing İngiliz Dili ve Edebiyatı Bölümü Yayın Koleksiyonu by Scopus Q "Q1"

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    Effects of calibration sample size and item bank size on abilityestimation in computerized adaptive testing
    (EDAM, 2015) Şahin, Alper; Weiss, DJ.
    This study aimed to investigate the effects of calibration sample size and item bank size on examinee ability estimation in computerized adaptive testing (CAT). For this purpose, a 500-item bank pre-calibrated using the three-parameter logistic model with 10,000 examinees was simulated. Calibration samples of varying sizes (150, 250, 350, 500, 750, 1,000, 2,000, 3,000, and 5,000) were selected from the parent sample, and item banks that represented small (100) and medium size (200 and 300) banks were drawn from the 500-item bank. Items in these banks were recalibrated using the drawn samples, and their estimated parameters were used in post-hoc simulations to re-estimate ability parameters for the simulated 10,000 examinees. The findings showed that ability estimates in CAT are robust against fluctuations in item parameter estimation and that accurate ability parameter estimates can be obtained with a calibration sample of 150 examinees. Moreover, a 200-item bank pre-calibrated with as few as 150 examinees can be used for some purposes in CAT as long as it has sufficient information at targeted ability levels.
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    Citation - WoS: 2
    Citation - Scopus: 3
    New Spectral Approaches To the Simultaneous Quantitative Resolution of a Combined Veterinary Formulation by Ann and Pca-Ann Methods
    (Walter de Gruyter & Co, 2011) Baleanu, Dumitru; Sen Koktas, Nigar; Dinc, Erdal
    The simultaneous spectral prediction of levamisole (LEV) and triclabendazole (TRI) in combined veterinary formulation was performed by the new chemometric methods, artificial neural network (ANN) and principal component analysis-artificial neural network (PCA-ANN). Despite the overlapping spectra of LEV and TRI in the same wavelength region, the proposed methods do not use any separation procedure for the analysis of the related compounds. Good precision and accuracy were observed for the applications of the proposed artificial neural network models to an independent binary mixture set consisting of the active compounds. These methods were successfully applied for the chemometric quantitation of a veterinary formulation of LEV and TRI.