Psychometrika
Introduction
Psychometrika, 87(2)
POSTING TYPE: TOCs
https://link.springer.com/journal/11336/volumes-and-issues/87-2
Special Issue on Forecasting with Intensive Longitudinal Data
Penalized Estimation and Forecasting of Multiple Subject Intensive Longitudinal Data
—Zachary F. Fisher, Younghoon Kim, Barbara L. Fredrickson, Vladas Pipiras [Google Scholar]
Guest Editors’ Introduction to the Special Issue on Forecasting with Intensive Longitudinal Data
—Peter F. Halpin, Kathleen Gates, Siwei Liu [Google Scholar]
Bayesian Forecasting with a Regime-Switching Zero-Inflated Multilevel Poisson Regression Model: An Application to Adolescent Alcohol Use with Spatial Covariates
—Yanling Li, Zita Oravecz, Shuai Zhou, Yosef Bodovski, Ian J. Barnett, Guangqing Chi, Yuan Zhou, Naomi P. Friedman, Scott I. Vrieze, Sy-Miin Chow [Google Scholar]
A Systematic Study into the Factors that Affect the Predictive Accuracy of Multilevel VAR(1) Models
—Ginette Lafit, Kristof Meers, Eva Ceulemans [Google Scholar]
Two Filtering Methods of Forecasting Linear and Nonlinear Dynamics of Intensive Longitudinal Data
—Michael D. Hunter, Haya Fatimah, Marina A. Bornovalova [Google Scholar]
A Lasso and a Regression Tree Mixed-Effect Model with Random Effects for the Level, the Residual Variance, and the Autocorrelation
—Steffen Nestler, Sarah Humberg [Google Scholar]
Forecasting Intra-individual Changes of Affective States Taking into Account Inter-individual Differences Using Intensive Longitudinal Data from a University Student Dropout Study in Math
—Augustin Kelava, Pascal Kilian, Judith Glaesser, Samuel Merk, Holger Brandt [Google Scholar]
Control Theory Forecasts of Optimal Training Dosage to Facilitate Children’s Arithmetic Learning in a Digital Educational Application
—Sy-Miin Chow, Jungmin Lee, Abe D. Hofman, Han L. J. Maas, Dennis K. Pearl, Peter C. M. Molenaar [Google Scholar]
A Response-Time-Based Latent Response Mixture Model for Identifying and Modeling Careless and Insufficient Effort Responding in Survey Data
—Esther Ulitzsch, Steffi Pohl, Lale Khorramdel, Ulf Kroehne, Matthias Davier [Google Scholar]
Better Information From Survey Data: Filtering Out State Dependence Using Eye-Tracking Data
—Joachim Büschken, Ulf Böckenholt, Thomas Otter, Daniel Stengel [Google Scholar]
Semiparametric Factor Analysis for Item-Level Response Time Data
—Yang Liu, Weimeng Wang [Google Scholar]
An Empirical Q-Matrix Validation Method for the Polytomous G-DINA Model
—Jimmy de la Torre, Xue-Lan Qiu, Kevin Carl Santos [Google Scholar]
Modeling Conditional Dependence of Response Accuracy and Response Time with the Diffusion Item Response Theory Model
—Inhan Kang, Paul Boeck, Roger Ratcliff [Google Scholar]
Transformer-Based Deep Neural Language Modeling for Construct-Specific Automatic Item Generation
—Björn E. Hommel, Franz-Josef M. Wollang, Veronika Kotova, Hannes Zacher, Stefan C. Schmukle [Google Scholar]
Modeling Faking in the Multidimensional Forced-Choice Format: The Faking Mixture Model
—Susanne Frick [Google Scholar]