The Experts below are selected from a list of 66 Experts worldwide ranked by ideXlab platform
Bruno D Zumbo - One of the best experts on this subject based on the ideXlab platform.
-
Item Characteristic Curve estimation of signal detection theory based personality data a two stage approach to Item response modeling
International Journal of Testing, 2003Co-Authors: Kevin M Williams, Bruno D ZumboAbstract:Signal Detection Theory (SDT; MacMillan & Creelman, 1991) is a method of data collection that has been used for several years, which describes the decision-making strategies of individuals. However, its use has been largely restricted to experiments involving sensation and perception. The Overclaiming Questionnaire (OCQ; Paulhus & Bruce, 1990) is a scale that has been developed to measure intellectual ability and personality, using SDT as a guideline. Although the scale has been successful in measuring human Characteristics such as narcissism and intelligence, it is still unclear how to measure the Characteristics of the various stimuli used (e.g., Item difficulty, Item discrimination, etc.). In some ways, this is a direct consequence of the general lack of research involved in Item parameter estimation in the field of SDT. Using the OCQ, this article presents a graphical and nonparametric form of Item response modeling to address this issue. In many ways, the approach is influenced by and structured arou...
Taina Pudalov - One of the best experts on this subject based on the ideXlab platform.
-
a nonparametric Item Characteristic Curve using the gini s mean difference
Social Science Research Network, 2013Co-Authors: Shlomo Yitzhaki, Rinat Itzhaki, Taina PudalovAbstract:The Item Characteristic Curve describes the relationship between the probability of correctly answering a question and the ability presumed to be required to answer the question. Since ability is a latent variable, distributional assumptions must be imposed on it in order to estimate such relationship. In this paper we overcome the need to impose an assumption on the distribution of abilities by using the properties of concentration Curves and Gini's Mean Difference (GMD). This enables investigation of whether the probability of correctly answering a question is monotonically related to a specific ability. The method is also useful for classifying abilities. This paper details the properties of the technique and provides examples of its application.
Kevin M Williams - One of the best experts on this subject based on the ideXlab platform.
-
Item Characteristic Curve estimation of signal detection theory based personality data a two stage approach to Item response modeling
International Journal of Testing, 2003Co-Authors: Kevin M Williams, Bruno D ZumboAbstract:Signal Detection Theory (SDT; MacMillan & Creelman, 1991) is a method of data collection that has been used for several years, which describes the decision-making strategies of individuals. However, its use has been largely restricted to experiments involving sensation and perception. The Overclaiming Questionnaire (OCQ; Paulhus & Bruce, 1990) is a scale that has been developed to measure intellectual ability and personality, using SDT as a guideline. Although the scale has been successful in measuring human Characteristics such as narcissism and intelligence, it is still unclear how to measure the Characteristics of the various stimuli used (e.g., Item difficulty, Item discrimination, etc.). In some ways, this is a direct consequence of the general lack of research involved in Item parameter estimation in the field of SDT. Using the OCQ, this article presents a graphical and nonparametric form of Item response modeling to address this issue. In many ways, the approach is influenced by and structured arou...
Shlomo Yitzhaki - One of the best experts on this subject based on the ideXlab platform.
-
a nonparametric Item Characteristic Curve using the gini s mean difference
Social Science Research Network, 2013Co-Authors: Shlomo Yitzhaki, Rinat Itzhaki, Taina PudalovAbstract:The Item Characteristic Curve describes the relationship between the probability of correctly answering a question and the ability presumed to be required to answer the question. Since ability is a latent variable, distributional assumptions must be imposed on it in order to estimate such relationship. In this paper we overcome the need to impose an assumption on the distribution of abilities by using the properties of concentration Curves and Gini's Mean Difference (GMD). This enables investigation of whether the probability of correctly answering a question is monotonically related to a specific ability. The method is also useful for classifying abilities. This paper details the properties of the technique and provides examples of its application.
Seockho Kim - One of the best experts on this subject based on the ideXlab platform.
-
the Item Characteristic Curve
2017Co-Authors: Frank B Baker, Seockho KimAbstract:In many educational and psychological measurement situations there is an underlying variable of interest. This variable is often something that is intuitively understood, such as “intelligence.” People can be described as being bright or average and the listener has some idea as to what the speaker is conveying about the object of the discussion.
-
Item Characteristic Curve models
2017Co-Authors: Frank B Baker, Seockho KimAbstract:In the first chapter the properties of the Item Characteristic Curve were defined in terms of verbal descriptors. While this is useful to obtain an intuitive understanding of Item Characteristic Curves, it lacks the precision and rigor needed by a theory. Consequently, in this chapter the reader will be introduced to three mathematical models for the Item Characteristic Curve. These models provide mathematical equations for the relation of the probability of correct response to ability. Each model employs one or more Item parameters whose numerical values define a particular Item Characteristic Curve. Such mathematical models are needed if one is to develop a measurement theory that can be rigorously defined and is amenable to further growth. In addition, these models and their parameters provide a vehicle for communicating information about an Item’s technical properties. For each of the three models, the mathematical equation will be used to compute the probability of correct response at several ability levels. Then the graph of the corresponding Item Characteristic Curve will be shown. The goal of the chapter is to have you develop a sense of how the numerical values of the Item parameters for a given model relate to the shape of the Item Characteristic Curve.
-
Item response theory parameter estimation techniques
2004Co-Authors: Frank B Baker, Seockho KimAbstract:The Item Characteristic Curve: Dichotomous Response Estimating the Parameters of an Item Characteristic Curve Maximum Likelihood Estimation of Examinee Ability Maximum Likelihood Procedures for Estimating Both Ability and Item Parameters The Rasch Model Marginal Maximum Likelihood Estimation and an EM Algorithm Bayesian Parameter Estimation Procedures The Graded Item Response Nominally Scored Items Markov Chain Monte Carlo Methods Parameter Estimation with Multiple Groups Parameter Estimation for a Test with Mixed Item Types
