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

Trigger-Point

Practical Implementation of Test Equating Using ,lthough software for test equating is currently available, in this paper we focus the attention on four different . packages which can facilitate test equating for researchers and test developers. This paper list the different . packages which are available at the moment. Examples are provided for t

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Predictive Validity Under Partial Observability,per we propose to evaluate the predictive capacity of selection tests through marginal effects under a partial identification approach. Identification bounds are defined for the marginal effects under monotonicity assumptions of the response variable. The performance of our method is assessed using

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Multiple-Group Propensity Score Inverse Weight Trimming and Its Impact on Covariate Balance and Biansity score methods facilitate the estimation of treatment effects when more than two intervention groups are compared. Former research has documented the benefits and weaknesses of trimming inverse weights of propensity scores in the context of one treatment and one comparison group, but no researc

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Procrustes Penalty Function for Matching Matrices to Targets with Its Applications,s. In this research, as a generalized form of penalized estimation, a new penalty function is proposed. The proposed function shrinks solutions to a prespecified target matrix which possesses a certain simple structure. The resulting solution is therefore simple and easy to interpret, and its simpli

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Factor Score Estimation from the Perspective of Item Response Theory,ntities, so one would expect that their estimation or characterization would follow parallel tracks in CFA and IRT. However, historically they have not. Different procedures have been used to derive factor score estimates and latent variable estimates in IRT, and different computational procedures h

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On the Precision Matrix in Semi-High-Dimensional Settings, than the sample size, the sample covariance matrix is no longer positive definite, and the inverse does not exist. Under the sparsity assumption on the elements of the precision matrix, the problem can be solved by fitting a Gaussian graphical model with lasso penalty. However, in high-dimensional

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Restenosis

Constant CSEM Achieved Through Scale Transformation and Adaptive Testing,nstant CSEM across all scores not only simplifies score interpretation and score reporting, but also contributes to the fairness of testing. This paper compares two fundamentally different approaches to achieving constant CSEMs: CSEM stabilizing scale transformations and computer adaptive tests (CAT

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Synchronized Time Profile Similarity in Applications to Nearest Neighbor Classification,implied data. Synchronization aligns inter-individual data from different time points to account for potential phase offsets and nonstationarity in the data. Such synchronization has been applied in psychology: For example, coordinated motion between two individuals exchanging information was used a