Titlebook: Quantitative Psychology; 84th Annual Meeting Marie Wiberg,Dylan Molenaar,Jee-Seon Kim Conference proceedings 2020 Springer Nature Switzerl

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Person Parameter Estimation for IRT Models of Forced-Choice Data: Merits and Perils of Pseudo-Likelilar estimates in most applications. However, standard errors and Bayesian estimation techniques are affected by falsely precise inference based on the independence likelihood. Fortunately, these distortions can be amended almost completely by a correction factor to the (independence) pseudo-likelih

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A Bayesian Graphical and Probabilistic Proposal for Bias Analysis,In addition, we also calculate the mean of the posterior probability of all the differences of each parameter for the groups compared. This probabilistic measurement provides a more accurate perception of intergroup performance by analyzing all items together. Our methodology is illustrated with the

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Factor Score Estimation from the Perspective of Item Response Theory, expected a posteriori and maximum likelihood estimates of IRT latent variables to clarify relations among the scores. This provides insights into the ways in which the data are combined into factor score estimates. The results provide an alternative method to compute factor scores in some simple mo

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On the Precision Matrix in Semi-High-Dimensional Settings, are compared such as unweighted least squares and factor analysis with equal unique variances (i.e., the probabilistic principal component analysis), as well as ridge factor analysis with small ridge parameters. Results indicate that they all give relatively small mean squared errors even when the

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Performance of the Modified Continuous ,-Stratification Indices in Computerized Adaptive Testing,gh percentage of overexposed items. Therefore, this study proposed three modified CAI methods for monitoring item exposure and compared their performance in item selection with some CAT item exposure control methods.

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Synergized Bootstrapping: The Whole is Faster than the Sum of Its Parts,the individual data points. As a consequence, once the costs of the individual data points are known, the parameter set’s cost can be obtained for any of the cost functions related to one of the replicated datasets. The synergized bootstrap method put forward in this paper exploits these two feature

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Synchronized Time Profile Similarity in Applications to Nearest Neighbor Classification, time warping and window-crossed lagging. The current study compares the Euclidean distance and the squared correlation before and after synchronization using window-crossed lagging and dynamic time warping in applications to one-nearest-neighbor classification tasks. Discussion, limitations, and fu