Existing measures of Actively Open-Minded Thinking (AOT) primarily assess the acceptance of rational thinking norms and standards, rather than actual thinking and resulting behavior. These scales can be susceptible to impression management, often yield inflated scores, and may not accurately capture how individuals think in real-life contexts. To address these limitations, we developed and validated a novel Situational Judgment Test for Actively Open-Minded Thinking (AOT-SJT), designed to assess behavioral tendencies related to AOT in realistic scenarios. AOT is conceptualized as the disposition to consider alternative viewpoints, seek disconfirming evidence, and revise beliefs in light of new information. Across 4 studies, we constructed and refined the AOT-SJT using scenarios that simulate everyday decision-making. In Study 1, we tested initial items among Croatian participants, resulting in a 13-item measure with solid psychometric properties. Study 2 confirmed the test’s convergent validity with cognitive and personality constructs and its predictive power for different forms of rational thinking. In Study 3, new items were introduced to enhance construct coverage, particularly around evidence search direction. Study 4 extended validation to an English-speaking sample, supporting cross-linguistic applicability, although effect sizes related to convergent validity were somewhat lower than before. Findings across studies show that the AOT-SJT aligns with theoretical expectations, demonstrates solid convergent validity with existing AOT scales, and effectively distinguishes levels of open-mindedness. By measuring behavioral intentions rather than standards acceptance, the AOT-SJT offers an externally valid assessment of AOT.

Understanding consumers' food wasting behaviours is crucial to reducing food waste. This study aimed to adapt the Food Wasting Behaviours Questionnaire (FWBQ) to Turkish and evaluate its validity and reliability. A cross-sectional study was conducted with 421 adults, preceded by a pilot study with 30 individuals to assess the intelligibility of the questionnaire. Exploratory factor analysis (EFA) was performed on a subsample of 219 participants, followed by confirmatory factor analysis (CFA) on an independent subsample of 202 participants. Reliability was assessed using Cronbach’s Alpha (CA), and test–retest reliability was evaluated with the intraclass correlation coefficient (ICC) in a separate group of 100 participants. As a result of EFA, a six-factor structure emerged—diverging from the original five-factor model of the FWBQ—indicating that in the Turkish context, planning meals and planning shopping behaviours formed distinct dimensions. Three items with low factor loadings (<0.30) were excluded, and the final version included 27 items with a total explained variance of 57.3%. CFA confirmed good model fit, and internal consistency was strong (CA = 0.700 to 0.924). Test–retest reliability was also high (ICC = 0.787 to 0.896). In conclusion, the Turkish version of the FWBQ is a valid and reliable tool for assessing a wide range of food wasting behaviours. The emergence of a sixth factor highlights the importance of cultural context in shaping food management routines and has implications for cross-cultural comparisons and future adaptations.

As the use of computational text analysis in the social sciences has increased, topic modeling has emerged as a popular method for identifying latent themes in textual data. Nevertheless, concerns have been raised regarding the validity of the results produced by this method, given that it is largely automated and inductive in nature, and the lack of clear guidelines for validating topic models has been identified by scholars as an area of concern. In response, we conducted a comprehensive systematic review of 789 studies that employ topic modeling. Our goal is to investigate whether the field is moving toward a common framework for validating these models. The findings of our review indicate a notable absence of standardized validation practices and a lack of convergence toward specific methods of validation. This gap may be attributed to the inherent incompatibility between the inductive, qualitative approach of topic modeling and the deductive, quantitative tradition that favors standardized validation. To address this, we advocate for incorporating qualitative validation approaches, emphasizing transparency and detailed reporting to improve the credibility of findings in computational social science research when using topic modeling.

We provide evidence on the extent to which survey items in the Preference Survey Module and the resulting Global Preference Survey measuring social preferences—trust, altruism, positive and negative reciprocity—predict behavior in corresponding experimental games outside the original participant sample of Falk et al. (Manag Sci, 2022. https://doi.org/10.1287/mnsc.2022.4455). Our results, which are based on a replication study with university students in Tehran, Iran, are mixed. While quantitative items considering hypothetical versions of the experimental games correlate significantly and economically meaningfully with individual behavior, none of the qualitative items show significant correlations. The only exception is altruism where results correspond more closely to the original findings.

Cognitive diagnosis models (CDMs) are an important psychometric framework for classifying students in terms of attribute and/or skill mastery. The $\mathit{Q}$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\varvec{Q}$$\end{document} matrix, which specifies the required attributes for each item, is central to implementing CDMs. The general unavailability of $\mathit{Q}$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\varvec{Q}$$\end{document} for most content areas and datasets poses a barrier to widespread applications of CDMs, and recent research accordingly developed fully exploratory methods to estimate Q. However, current methods do not always offer clear interpretations of the uncovered skills and existing exploratory methods do not use expert knowledge to estimate Q. We consider Bayesian estimation of $\mathit{Q}$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\varvec{Q}$$\end{document} using a prior based upon expert knowledge using a fully Bayesian formulation for a general diagnostic model. The developed method can be used to validate which of the underlying attributes are predicted by experts and to identify residual attributes that remain unexplained by expert knowledge. We report Monte Carlo evidence about the accuracy of selecting active expert-predictors and present an application using Tatsuoka’s fraction-subtraction dataset.

The validity of a test is often estimated in a nonrandom sample of selected individuals. To accurately estimate the relation between the predictor and the criterion we correct this correlation for range restriction. Unfortunately, this corrected correlation cannot be transformed using Fisher's Z transformation, and asymptotic tests of hypotheses based on small or moderate samples are not accurate. We developed a Fisher r to Z transformation for the corrected correlation for each of two conditions: (a) the criterion data were missing due to selection on the predictor (the missing data were MAR); and (b) the criterion was missing at random, not due to selection (the missing data were MCAR). The two Z transformations were evaluated in a computer simulation. The transformations were accurate, and tests of hypotheses and confidence intervals based on the transformations were superior to those that were not based on the transformations.

This rejoinder responds to the commentary by Liu (Psychometrika, 2015) entitled “On the consistency of Q-matrix estimation: A commentary” on the paper “A general method of empirical Q-matrix validation” by de la Torre and Chiu (Psychometrika, 2015). It discusses and addresses three concerns raised in the commentary, namely the estimation accuracy when a provisional Q-matrix is used, the consistency of the Q-matrix estimator, and the computational efficiency of the proposed method.

In contrast to unidimensional item response models that postulate a single underlying proficiency, cognitive diagnosis models (CDMs) posit multiple, discrete skills or attributes, thus allowing CDMs to provide a finer-grained assessment of examinees’ test performance. A common component of CDMs for specifying the attributes required for each item is the Q-matrix. Although construction of Q-matrix is typically performed by domain experts, it nonetheless, to a large extent, remains a subjective process, and misspecifications in the Q-matrix, if left unchecked, can have important practical implications. To address this concern, this paper proposes a discrimination index that can be used with a wide class of CDM subsumed by the generalized deterministic input, noisy “and” gate model to empirically validate the Q-matrix specifications by identifying and replacing misspecified entries in the Q-matrix. The rationale for using the index as the basis for a proposed validation method is provided in the form of mathematical proofs to several relevant lemmas and a theorem. The feasibility of the proposed method was examined using simulated data generated under various conditions. The proposed method is illustrated using fraction subtraction data.

Wild species of rice possess tremendous genetic variations and harbour resistance genes for biotic stresses. Bacterial blight (BB), caused by Xanthomonas oryzae pv. oryzae (Xoo), is a major disease affecting rice production globally. The current study characterized 116 accessions from 17 species of Oryza for BB disease during three seasons viz., kharif 2020, rabi 2020–21, kharif 2021 using an isolate of Xanthomonas oryzae pv. oryzae (Xoo) strain IX-020. A total of 40 accessions including Oryza rufipogon, O. nivara, O. officinalis and O. australiensis showed consistence resistance to the bacterial blight disease across the seasons. These accessions were further subjected to molecular characterization using 11 Xa genes viz., Xa4, xa5, xa13, Xa21, Xa23, Xa27(t), Xa32(t), Xa33, Xa35(t), Xa38 and xa41 with gene-specific markers to ascertain the novelty. Some key resistance genes such as Xa4, Xa23, Xa27(t), Xa32(t), Xa33, Xa35(t) and xa41 were detected in multiple accessions, with O. rufipogon and O. eichingeri harbouring particularly complex combinations of these genes. Notably, several accessions viz., IC521672 (O. nivara), EC861665 (O. officinalis), EC861677 (O. latifolia), EC861711 (O. punctata) and EC861738 (O. eichingeri) did not show the presence of any known genes indicating the possibility of novel genetic loci conferring BB resistance in these wild species. These promising accessions identified in the study are potential novel sources for bacterial leaf blight resistance in rice and will be useful for the development of durable bacterial blight resistance rice cultivars.

No study has validated questionnaires for assessing easily calculable diet quality scores in Japan. The Brief-type self-administered Diet History Questionnaire (BDHQ) is widely used to assess dietary intake in Japan, while the Meal-based Diet History Questionnaire (MDHQ) assesses dietary intake for each meal (breakfast, lunch, dinner and snacks) and overall dietary intake. This study examined the relative validity of the BDHQ and MDHQ for assessing three diet quality scores in Japanese adults. A total of 111 women and 111 men aged 30–76 years completed the web MDHQ and BDHQ, followed by 4-non-consective-day weighed dietary records. The diet quality scores examined included the Diet Quality Score for Japanese (DQSJ), Dietary Approaches to Stop Hypertension (DASH) score and Alternate Mediterranean Diet (AMED) score. The means of the three scores for overall diet from the BDHQ were not significantly different from those from the dietary records in both sexes, whereas those from the MDHQ were higher than those from the dietary records, except for the DASH and AMED in women. Pearson’s correlation coefficients between both questionnaires and dietary records were 0·57–0·63 for DQSJ, 0·49–0·57 for DASH and 0·31–0·49 for AMED across both sexes and both questionnaires. For each meal, Pearson’s correlation coefficients between the MDHQ and dietary records ranged from 0·01 (DASH for snacks in women) to 0·55 (DQSJ for breakfast in men), with a median of 0·35. This study showed that the ability of the BDHQ and MDHQ to rank individuals was good for DQSJ and DASH and acceptable for AMED.