Abstract
With the development in computing technology, item response theory (IRT) develops rapidly, and has become a user friendly application in psychometrics world. Limitation in classical theory is one aspect that encourages the use of IRT. In this study, the basic concept of IRT will be discussed. In addition, it will briefly review the ability parameter estimation, particularly maximum likelihood estimation (MLE) and expected a posteriori (EAP). This review aims to describe the fundamental understanding of IRT, MLE and EAP which likely facilitates evaluators in the psychometrics to recognize the characteristics of test participants. Key words: Expected A Posteriori, Item Response Theory, Maximum Likelihood Estimation
Highlights
Over the last decade, item response theory (IRT) has increasingly been popular
This review aims to describe the fundamental understanding of IRT, maximum likelihood estimation (MLE) and expected a posteriori (EAP) which likely facilitates evaluators in the psychometrics to recognize the characteristics of test participants
IRT is developed to address the limitation in classic measurement theory, its shortcoming that is dependent between test participant group and items in nature
Summary
Item response theory (IRT) has increasingly been popular. As noted by Steinberg and Thissen (2013), many studies have been conducted to enrich literatures in the field of psychometrics. A difficult case occurs to ai negative value, or it is better to remove items with negative distinguishing capacity due to possible error, and this indicates correct answer probability decreases when the ability level increases. The highest opportunity will depend on the probability of the correct answers and incorrect answers by the participants, and on the logistic parameter employed, the determination of maximum ability value is carried out through iteration calculation (Baker, 2001). The curve in logistic model extends towards 0 or 1 asymptotically This means that the curve will reach 0 or 1 at infinite point that the estimation method is incapable of estimating the parameter when there are items or participants make all correct answer or all incorrect answer (Naga, 1992a).
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