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Variance Difference Between Maximum Likelihood Estimation Method and Expected a Posteriori Estimation Method Viewed from Number of Test Items

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Variance Difference Between Maximum Likelihood Estimation Method and Expected a Posteriori Estimation Method Viewed from Number of Test Items Vol. 11(16), pp. 1579-1589, 23 August, 2016 DOI: 10.5897/ERR2016.2807 Article Number: B2D124860158 ISSN 1990-3839 Educational Research and Reviews Copyright © 2016 Author(s) retain the copyright of this article http://www.academicjournals.org/ERR Full Length Research Paper Variance difference between maximum likelihood estimation method and expected A posteriori estimation method viewed from number of test items Jumailiyah Mahmud*, Muzayanah Sutikno and Dali S. Naga 1Institute of Teaching and Educational Sciences of Mataram, Indonesia. 2State University of Jakarta, Indonesia. 3 University of Tarumanegara, Indonesia. Received 8 April, 2016; Accepted 12 August, 2016 The aim of this study is to determine variance difference between maximum likelihood and expected A posteriori estimation methods viewed from number of test items of aptitude test. The variance presents an accuracy generated by both maximum likelihood and Bayes estimation methods. The test consists of three subtests, each with 40 multiple-choice items of 5 alternatives. The total items are 102 and 3159 respondents which were drawn using random matrix sampling technique, thus 73 items were generated which were qualified based on classical theory and IRT. The study examines 5 hypotheses. The results are variance of the estimation method using MLE is higher than the estimation method using EAP on the test consisting of 25 items with F= 1.602, variance of the estimation method using MLE is higher than the estimation method using EAP on the test consisting of 50 items with F= 1.332, variance of estimation with the test of 50 items is higher than the test of 25 items, and variance of estimation with the test of 50 items is higher than the test of 25 items on EAP method with F=1.329. All observed F values ≥ 1.00. 5 RMSE in items 10, 15, 20, and 25 are different in both MLE and EAP, with t = 3.060, 0,011, thereby meaning that statistical null hypothesis are rejected. The study concludes that variance of MLE method is higher than EAP, and the test with 50 items has higher variance than that with 25 items, the accuracy of EAP estimate higher than that of MLE in item 10, 15, 20, and 25. Key words: Variance, RMS of estimation, maximum likelihood, expected A posteriori. INTRODUCTION There are two types of psychological and educational weakness of the classical measurement theory, mainly in measurement theories; classical and modern. Such a its dependence among groups of test-takers and items. modern measurement theory is also known as item Dependence means that result of measurement depends response theory which is developed in response to the on groups of those who do the test. If such a test is *Corresponding author. E-mail: [email protected]. Authors agree that this article remain permanently open access under the terms of the Creative Commons Attribution License 4.0 International License 1580 Educ. Res. Rev. provided to groups of its takers who have high analysis is also done on individual basis. proficiency, level of difficulty of items assessed in the test Correct answer probability, item parameter and test is getting lower. On contrary, if it is given to those with takers’ characteristics are correlated in a logistic formula low capabilities, level of difficulty of the test items model. As a result, item curve characteristic is reflected becomes higher (Hambleton, 1991). It was found that the by logistic model used as a basis of calculation (Naga, classical test theory (CTT) had some limitations, 1992). Model description of a parameter in a curve of however, Item Response Theory (IRT) showed a variety item characteristic is level of difficulty of item itself (bi). of benefits such as: The higher level of difficulty a certain item has, the higher ability the test takers need to answer such item correctly. 1. Estimating item difficulty Thus, as shown in the location, the higher level of 2. More stable in terms of difficulty indeces difficulty the test item has, the righter position it will be. 3. More stable of internal consistency, and In a two-parameter logistic model, it shows that such a 4. Markedly reducing in error’s measurement (Magno, model calculates item level of difficulty (b ) and item 2009). i discrimination index (ai). The picturing in item arch curve IRT, as a model- and item-basedapproach, is obviously (ICC), item discrimination index is shown in the curve of considered succesful in its use in terms of research and item with slope or curve precipitousness. An item with practice applications. The main role of IRT model is to high precipitousness shows high discrimination index or estimate someone’s position on a latent dimension value of ai is high. In contrast, slope item curve shows (Reise and Revicki, 2014). In addition, estimation quality low discrimination index or value of ai is low. Here is a depends on accuracy criteria consisting mean square formula for two-parameter logistic model: error (MSE), bias and estimation variance. The results showed that parameter estimation is much more better by i=1,2,3, (1) implementing priory, particularly for two and three parameter models (Baker, 2004). Pi (θ) = probability of test takers having ability θ chosen The aim of educational measurement is to know level randomly that could answer item i correctly. of test takers’ ability that could be used in selections for θ = subject’s level of ability decision-makings. Results of such selections are used to bi = parameter of item level of difficulty for i identify whether or not candidates can be accepted in a ai = discrimination index for item i particular program. A decision to accept the candidates n = number of items in a test or not is often wrong which may bring negative e = numeral valuing 2.718 implications to further individuals’ developments. An D = scaling factor made in order that logistic function inappropriate decision is often caused by the use of closes to function of normal ogive (Hambleton et al., invalid and unreliable instruments or tests, results of 1991) measurements which are very different from the actual conditions so that it contains high uncertainty. In contrast, Estimation is means fathoming or apprising. Estimation a measurement is believed containing high accuracy if its contains the findings of appropriate values for parameters result has small RMS and variance. of equality using certain ways or methods (Makridakis et Perspective proposed by DeMars reveals assumptions al.,1999). Some differences, however, may be found on item response theory relating to unidimensional, local between them. Such differences are: independency and model specification accuracy (DeMars, 2010). Firstly, unidimensional is a test 1. Regression model is often applied in variables that measuring only a character or a particular test takers’ have linear correlation; while in parameter logistic model, ability. Items in a test, for example, just measure there is a correlation between test item and nonlinear test participants’ ability to count, not to assess their takers’ ability. proficiency in a language either. Statistically, 2. Independent variable in regression model is unidimensional can be calculated its Eigen score using observable. On contrary, independent variable of test factor analysis, indicated by a dominant one. Secondly, takers’ ability is unobservable in item response theory local independency is meant that the influence of test (Hambleton et al., 1991). Since actual scores of test item takers’ ability and test items is supposed to be constant; parameters and ability of test takers are unidentified, an test takers’ responses on the items are not statistically analysis and estimation for parameters of test takers’ connected. “This assumption can be accepted if test ability and item scores is required to do. The identification takers’ answer in a certain item do not influence answers of scores of parameters is known as parameter in other items. Test takers’ answers in some test items estimation. are expected to be unrelated” (Hambleton et al., 1991). A study by Borgatto and Pinheiro (2015) aims to Implication of such assumption causes is that the test can determine the impact of ability estimation on IRT with be analyzed item per item. For the test takers, an respect to the difficulty level of the test and to evaluate Mahmud et al. 1581 whether the error of estimation can be influenced by average score as a justifiable deviation. method of estimation used. There are 2 estimation Hambleton et al. (1991) explains that score of residual methods used, which are weighted likelihood estimator error of maximum likelihood estimation method is higher (WLE) and MLE. EAP method is divided according to its than that of expected a posteriori method. Hambleton et prior θ uniform and normal distribution, and this uniform al. (1991) clearly states that scores of residual error of distribution is compared with WLE. The standard MLE is higher than EAP. This indicates that the accuracy measurement to determine the accuracy of estimation of EAP estimation is better than MLE (Hambleton et al., method is Mean Squared Error (MSE). Simulation uses 4 1991). Swaminathan et al. (2003) argues that “Bayes tests based on the number of items which are 15, 30, 45 procedure generally can result to smaller variance and 60 items. The result of study showed that uniformed compared to that generated from maximum likelihood. EAP has better MSE than WLE with 45 items, and from Baker et al. (2004) points out an estimation bias resulted this result it was suggested to use more items in from both of the estimation methods; “there was little estimating ability parameter. difference between the MLE/EM estimates and those Accuracy is meant as the most criteria used to evaluate obtained via the Bayesian procedures”. This means that the works of models of estimating methods.
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