Incorporating temporal decline in predictive validity into mental testing theory is outlined. Starting from multivariate regression of criterion on repeated measurements, an analytic extension results in a weighting function for repeated measurements, replacing the beta weights. Besides optimizing, the procedure permits an evaluation of any particular prognosis setting: In cases of exponentially declining predictive validity, prognostic range can be extended if concurrent validity is nonperfect by optimal weighting (“predictive filtering”) of repeated measurements. Considerable gain in prognostic range over the traditional approach can be achieved if predictive validity declines concavely downwards.

In predicting\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\tilde y$$\end{document} scores from p > 1 observed scores\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$(\tilde x)$$\end{document} in a sample of size ñ, the optimal strategy (minimum expected loss), under certain assumptions, is shown to be based upon the least squares regression weights\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$(\hat \beta )$$\end{document} computed from a previous sample. Letting\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\tilde r(\hat \beta )$$\end{document} represent the correlation between\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\tilde y$$\end{document} and the predicted values\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$(\hat \beta '\tilde x)$$\end{document}, and letting\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\tilde r(w)$$\end{document} represent the correlation between\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\tilde y$$\end{document} and a different set of predicted values\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$(w'\tilde x)$$\end{document}, where w is any weighting system which is not a function of\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\tilde y$$\end{document}, it is shown that the probability of\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\tilde r(\hat \beta )$$\end{document} being less than\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\tilde r(w)$$\end{document} cannot exceed .50. The relationship of this result to previous research and practical implications are discussed.

Issues of model selection have dominated the theoretical and applied statistical literature for decades. Model selection methods such as ridge regression, the lasso, and the elastic net have replaced ad hoc methods such as stepwise regression as a means of model selection. In the end, however, these methods lead to a single final model that is often taken to be the model considered ahead of time, thus ignoring the uncertainty inherent in the search for a final model. One method that has enjoyed a long history of theoretical developments and substantive applications, and that accounts directly for uncertainty in model selection, is Bayesian model averaging (BMA). BMA addresses the problem of model selection by not selecting a final model, but rather by averaging over a space of possible models that could have generated the data. The purpose of this paper is to provide a detailed and up-to-date review of BMA with a focus on its foundations in Bayesian decision theory and Bayesian predictive modeling. We consider the selection of parameter and model priors as well as methods for evaluating predictions based on BMA. We also consider important assumptions regarding BMA and extensions of model averaging methods to address these assumptions, particularly the method of Bayesian stacking. Simple empirical examples are provided and directions for future research relevant to psychometrics are discussed.

Under a formula score instruction (FSI), test takers omit items. If students are encouraged to answer every item (under a rights-only scoring instruction, ROI), the score distribution will be different. In this paper, we formulate a simple statistical model to predict the score ROI distribution using the FSI data. Estimation error is also provided. In addition, a preliminary investigation of the probability of guessing correctly on omitted items and its sensitivity is presented in the paper. Based on the data used in this paper, the probability of guessing correctly may be close or slightly greater than the chance score.

Consumers typically overstate their intentions to purchase products, compared to actual rates of purchases, a pattern called “hypothetical bias”. In laboratory choice experiments, we measure participants’ visual attention using mousetracking or eye-tracking, while they make hypothetical as well as real purchase decisions. We find that participants spent more time looking both at price and product image prior to making a real “buy” decision than making a real “don’t buy” decision. We demonstrate that including such information about visual attention improves prediction of real buy decisions. This improvement is evident, although small in magnitude, using mousetracking data, but is not evident using eye-tracking data.

Prediction is a crucial mechanism of language comprehension. Our research question asked whether learners of Spanish were capable of using word order cues to predict the semantic class of the upcoming verb, and how this ability develops with proficiency. To answer this question, we conducted a self-paced reading study with three L2 Spanish groups at different proficiency levels and one native control group. Among the advanced L2 learners and native speakers, we found that reading times increased after the verb appeared in a word order not strongly associated with its semantic class. Because the only cue to the sentences’ word order was the presence or absence of the object marker a before the first noun, we suggest that these groups use this morphosyntactic cue to anticipate the semantic class of the upcoming verb. However, this pattern of processing behavior was not detected in our less experienced L2 groups.

This study investigated the predictive use of dative verb constraints in Mandarin among home-country-raised native speakers and classroom learners (including both sequential L2 learners and heritage speakers). In a visual world eye-tracking experiment, participants made anticipatory looks to the upcoming argument (recipient versus theme) following categorical restrictions of non-alternating verbs and gradient bias of alternating verbs before the acoustic onset of the disambiguating noun. Crucially, no delay or reduction in the prediction effects was observed among L2 learners and heritage speakers in comparison with home-country-raised native speakers. Mandarin proficiency and dominant language (English versus other) did not modulate prediction effects among classroom learners. These findings provide direct support for the assumption of error-driven learning accounts of the dative alternation, that is, language users actively predict upcoming arguments based on verb information during real-time sentence processing.

We tested whether verb-based prediction in late bilinguals is facilitated when the verb is a cognate versus non-cognate. Spanish–English bilinguals and Chinese–English bilinguals (control) listened to English sentences such as “The girl will adopt the dog” while viewing a scene containing either a dog and unadoptable objects (predictable condition) or a dog and other adoptable animals (unpredictable condition). The verb was either a cognate or non-cognate between Spanish and English and never a cognate between Chinese and English. Both groups of bilinguals were more likely to look at the target (the dog) in the predictable versus unpredictable condition. However, only low-proficient L1 Spanish bilinguals showed greater and earlier prediction when the verb was cognate than when it was non-cognate, suggesting that cognate facilitation effect occurs not only on the cognate word itself but also on prediction based on this cognate word, and that this effect is modulated by L2 proficiency.

The aspirations-ability framework proposed by Carling has begun to place the question of who aspires to migrate at the center of migration research. In this article, building on key determinants assumed to impact individual migration decisions, we investigate their prediction accuracy when observed in the same dataset and in different mixed-migration contexts. In particular, we use a rigorous model selection approach and develop a machine learning algorithm to analyze two original cross-sectional face-to-face surveys conducted in Turkey and Lebanon among Syrian migrants and their respective host populations in early 2021. Studying similar nationalities in two hosting contexts with a distinct history of both immigration and emigration and large shares of assumed-to-be mobile populations, we illustrate that a) (im)mobility aspirations are hard to predict even under ‘ideal’ methodological circumstances, b) commonly referenced “migration drivers” fail to perform well in predicting migration aspirations in our study contexts, while c) aspects relating to social cohesion, political representation and hope play an important role that warrants more emphasis in future research and policymaking. Methodologically, we identify key challenges in quantitative research on predicting migration aspirations and propose a novel modeling approach to address these challenges.

Broadening prediction efforts from imminent psychotic symptoms to neurodevelopmental vulnerabilities can enhance the accuracy of diagnosing severe mental disorders. Early interventions, especially during adolescence, are vital as these disorders often follow a long prodromal phase of neurodevelopmental disturbances. Child and adolescent mental health services should lead a developmentally-sensitive model for timely, effective detection and intervention.