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In a companion work (Paper I) we detected a large population of highly variable Young Stellar Objects (YSOs) in the Vista Variables in the Via Lactea (VVV) survey, typically with class I or flat spectrum spectral energy distributions and diverse light curve types. Here we present infrared spectra (0.9-2.5 µm) of 37 of these variables, many of them observed in a bright state. The spectra confirm that 15/18 sources with eruptive light curves have signatures of a high accretion rate, either showing EXorlike emission features (∆v=2 CO, Brγ) and/or FUor-like features (∆v=2 CO and H 2 O strongly in absorption). Similar features were seen in some long term periodic YSOs and faders but not in dippers or short-term variables. The sample includes some dusty Mira variables (typically distinguished by smooth Mira-like light curves), 2 cataclysmic variables and a carbon star. In total we have added 19 new objects to the broad class of eruptive variable YSOs with episodic accretion. Eruptive variable YSOs in our sample that were observed at bright states show higher accretion luminosities than the rest of the sample. Most of the eruptive variables differ from the established FUor and EXor subclasses, showing intermediate outburst durations and a mixture of their spectroscopic characteristics. This is in line with a small number of other recent discoveries. Since these previously atypical objects are now the majority amongst embedded members of the class, we propose a new classification for them as MNors. This term (pronounced emnor) follows V1647 Ori, the illuminating star of McNeil's Nebula.
This study proposes a multiple-group cognitive diagnosis model to account for the fact that students in different groups may use distinct attributes or use the same attributes but in different manners (e.g., conjunctive, disjunctive, and compensatory) to solve problems. Based on the proposed model, this study systematically investigates the performance of the likelihood ratio (LR) test and Wald test in detecting differential item functioning (DIF). A forward anchor item search procedure was also proposed to identify a set of anchor items with invariant item parameters across groups. Results showed that the LR and Wald tests with the forward anchor item search algorithm produced better calibrated Type I error rates than the ordinary LR and Wald tests, especially when items were of low quality. A set of real data were also analyzed to illustrate the use of these DIF detection procedures.
This study aims to conduct differential item functioning analyses in the context of cognitive diagnosis assessments using various formulations of the Wald test. In implementing the Wald test, two scenarios are considered: one where the underlying reduced model can be assumed; and another where a saturated CDM is used. Illustration of the different Wald test to detect DIF in CDM data was based on the items' performance of the Proportional Reasoning test among low-and highperforming school students. A benchmark simulation study was included to compare the performance of the Wald test in each scenario. The agreement of the latent attribute classification based on different cognitive diagnosis models was also discussed.
Abstract:In cognitive diagnosis modeling, the attributes required for each item are specified in the Q-matrix. The traditional way of constructing a Qmatrix based on expert opinion is inherently subjective, consequently resulting in serious validity concerns. The current study proposes a new validation method under the deterministic inputs, noisy "and" gate (DINA) model to empirically validate attribute specifications in the Q-matrix. In particular, an iterative procedure with a modified version of the sequential search algorithm is introduced. Simulation studies are conducted to compare the proposed method with existing parametric and nonparametric methods. Results show that the new method outperforms the other methods across the board. Finally, the method is applied to real data using fraction-subtraction data. ARTICLE HISTORY
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