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Emotions are part and parcel of the human condition, but their nature is debated. Three broad classes of theories about the nature of emotions can be distinguished: affect-program theories, constructionist theories, and appraisal theories. Integrating these broad classes of theories into a unifying theory is challenging. An integrative psychometric model of emotions can inform such a theory because psychometric models are intertwined with theoretical perspectives about constructs. To identify an integrative psychometric model, we delineate properties of emotions stated by emotion theories and investigate whether psychometric models account for these properties. Specifically, an integrative psychometric model of emotions should allow (a) identifying distinct emotions (central in affect-program theories), (b) between-and within-person variations of emotions (central in constructionist theories), and (c) causal relationships between emotion components (central in appraisal theories). Evidence suggests that the popular reflective and formative latent variable models—in which emotions are conceptualized as unobservable causes or consequences of emotion components—cannot account for all properties. Conversely, a psychometric network model—in which emotions are conceptualized as systems of causally interacting emotion components—accounts for all properties. The psychometric network model thus constitutes an integrative psychometric model of emotions, facilitating progress toward a unifying theory.

Bangladesh has emphasized active transportation in its transportation policies and has encouraged its population, especially the youth and students, towards bicycling. However, there is a scarcity of studies that have examined the factors important to the choice of active transportation that can be referenced to support the initiative. To address this research gap, in this study, we explore the influence of sociodemographics and latent perceptions of a built environment on the choice to walk and bicycle among students and nonstudents in Rajshahi, Bangladesh. In Rajshahi, we conducted a household survey between July and August, 2017. We used a modeling framework that integrated choice and latent variable (ICLV) models to effectively incorporate the latent perception variables in the choice model, addressing measurement error and endogeneity bias. Our models show that students are influenced by perceptions of safety from crime, while nonstudents are influenced by their perceptions of the walkability of a built environment when choosing a bicycle for commuting trips. For recreational bicycle trips, students are more concerned about the perceptions of road safety, whereas nonstudents are concerned about safety from crime. We find that road safety perception significantly and positively influences walking behavior among nonstudents. Structural equation models of the latent perception variables show that females are more likely to provide lower perceptions of neighborhood walkability, road safety, and safety from crime. Regarding active transportation decisions, overall, we find there is a difference between student and nonstudent groups and also within these groups. The findings of this study can assist in developing a sustainable active transportation system by addressing the needs of different segments of the population. In this study, we also provide recommendations regarding promoting active transportation in Rajshahi.

Background For multivariate data analysis involving only two input matrices (e.g., X and Y), the previously published methods for variable influence on projection (e.g., VIP OPLS or VIP O2PLS ) are widely used for variable selection purposes, including (i) variable importance assessment, (ii) dimensionality reduction of big data and (iii) interpretation enhancement of PLS, OPLS and O2PLS models. For multiblock analysis, the OnPLS models find relationships among multiple data matrices (more than two blocks) by calculating latent variables; however, a method for improving the interpretation of these latent variables (model components) by assessing the importance of the input variables was not available up to now. Results A method for variable selection in multiblock analysis, called multiblock variable influence on orthogonal projections (MB-VIOP) is explained in this paper. MB-VIOP is a model based variable selection method that uses the data matrices, the scores and the normalized loadings of an OnPLS model in order to sort the input variables of more than two data matrices according to their importance for both simplification and interpretation of the total multiblock model, and also of the unique, local and global model components separately. MB-VIOP has been tested using three datasets: a synthetic four-block dataset, a real three-block omics dataset related to plant sciences, and a real six-block dataset related to the food industry. Conclusions We provide evidence for the usefulness and reliability of MB-VIOP by means of three examples (one synthetic and two real-world cases). MB-VIOP assesses in a trustable and efficient way the importance of both isolated and ranges of variables in any type of data. MB-VIOP connects the input variables of different data matrices according to their relevance for the interpretation of each latent variable, yielding enhanced interpretability for each OnPLS model component. Besides, MB-VIOP can deal with strong overlapping of types of variation, as well as with many data blocks with very different dimensionality. The ability of MB-VIOP for generating dimensionality reduced models with high interpretability makes this method ideal for big data mining, multi-omics data integration and any study that requires exploration and interpretation of large streams of data.

Empirical results both from information technology acceptance research as well as from other fields suggest that attitude and subjective norms may have a nonlinear relationship. Based on the economic theory of complementarities, the present paper hypothesizes a substitution relationship or negative synergy between attitude and subjective norms in organizational IT use contexts. Employing two methods for modeling and measuring nonlinear effects of latent constructs, as well as two approaches for visualizing and interpreting interaction and quadratic terms, structural equation modeling analysis of data collected from 258 users of a variety of IT applications in 14 organizations provides support for the hypothesis that attitude and subjective norms were substitutes in predicting intention to use.

This article presents a methodological proposal for formulating a Human Security Index (HSI), including information from institutional sources and the inhabitants' perception of security. The developed methodology uses quantitative methods to evaluate HS (Human Security) in small municipalities with large rural areas affected by the confluence of different social and economic problems. Given the security conditions in the area, it was impossible to use a random sampling mechanism. Therefore, the data collected have a sample size that cannot be considered significant enough to make inferences using a frequentist statistics approach. The method to construct the index is illustrated using Miranda's data, a Colombian municipality exposed to decades of armed conflict. With the answers given by 55 interviewees to questions related to the armed conflict such as presence-absence reminders and retained values of violent events, a proposal of 36 indices was made, and two of them were selected for the study, following some statistical criteria. In the construction of one of these selected indices, we used information from binary variables and, for the other index, we used information from count data. The values obtained by both indices for the municipality of Miranda were, respectively, 46.4 and 35.8. According to HS experts, both values can be considered moderate levels in the perception of insecurity by residents of the municipality.

The study of human behaviour and in particular individual choices is of great interest in the field of environmental economics. Substantial attention has been paid to the way in which preferences vary across individuals, and there is a realisation that such differences are at least in part due to underlying attitudes and convictions. While this has been confirmed in empirical work, the methods typically employed are based on the arguably misguided use of responses to attitudinal questions as direct measures of underlying attitudes. As discussed in other literature, especially in transport research, this potentially leads to measurement error and endogeneity bias, and attitudes should rather be treated as latent variables. In this paper, we illustrate the use of such an Integrated Choice and Latent Variable model in the context of beach visitors’ willingness-to-pay for improvements in water quality. We show how a latent attitudinal variable, which we refer to as a pro-intervention attitude, helps explain both the responses from the stated choice exercise as well as answers to various rating questions related to respondent attitudes. The incorporation of the latent variable leads to important gains in model fit and substantially different willingness-to-pay patterns.

This article proposes log-linear models for the latent intensity functions of replicated spatio-temporal point processes. By simultaneously fitting correlated spatial and temporal Karhunen–Loève expansions, these models produce spatial and temporal components that are usually easy to interpret and capture the main directions of spatio-temporal correlation. The asymptotic distribution of the estimators is derived, and their finite sample properties are studied by simulation. As an example of application, we analyze the spatio-temporal patterns of usage of a bike station in the Divvy bike-sharing system of the city of Chicago. Dans cet article, l’auteur utilise des modèles log-linéaires pour estimer des fonctions d’intensité latente de processus ponctuels spatio-temporels répliqués. Plus précisément, un ajustement de ces modèles à des développements de Karhunen–Loève corrélées dans l’espace et dans le temps lui permet d’obtenir des composantes spatiales et temporelles qui capturent les principales directions de la corrélation spatio-temporelle en plus d’être faciles à interpréter. Le comportement à distance finie ou infinie des estimateurs proposés est exploré à travers leur distribution asymptotique et une simulation numérique. En guise d’illustration pratique, l’auteur utilise ces estimateurs pour analyser les données d’utilisation du système de partage de vélos Divvy de la ville de Chicago.

How employees react to an organization’s ethical/social initiatives has little support in terms of empirical evidence. We examine employee perceptions about organizational social commitment (OSC) and its association with employee well-being (WB). The sample consists of 289 participants of a healthcare organization in Colombia. We use a comprehensive methodology for mining psychological/managerial constructs in R comprising six processes (observe, explore, confirm, explain, predict, and report). We provide information concerning the scales’ plausibility, reliability, convergent/discriminant validity, and equity. We contrast the relationship between OSC and WB by using structural equation modelling with bootstrap approaches. We examine the capability of OSC to predict WB by using machine learning methods. We found a positive relationship between the constructs, which shows that OSC is a valuable strategy for contributing to employee objectives from a ‘being well together’ perspective. The paper stimulates/facilitates future research and teaching-learning initiatives in latent variable analysis using the R language.

Assessing public preferences for air pollution control is essential to achieving effective air quality improvement, but the internal psychological factors affecting public preferences, especially psychological distance (PD), have only received limited attention. Therefore, this paper explores the role of PD in assessing public preferences for air quality improvement policies. Compared with previous studies that consider psychological factors in the choice model, this study incorporates PD into the choice model as a latent variable by considering both individual responses to measurement questions and socio-economic characteristics in the integrated choice and latent variable model. The results of this study clearly show that PD significantly affects public preferences for air quality improvement policies. Respondents with close PD had obvious preferences for air quality improvement, while those with distant PD were satisfied with the current situation and reluctant to improve it. After considering PD in the analysis, respondents’ willingness to pay for one-unit level change of attributes “heavily polluted days,” “good air days,” “mortality,” and “policy postponement” were respectively 10.3791CNY, 10.9005CNY, 11.0427CNY, 28.3412CNY per year. In addition, the paper also found men and respondents with lower levels of education and higher monthly incomes tended to view air pollution as psychologically distant and thus less willing to improve air quality. It is suggested that policy makers should reduce the PD of air pollution among these people by increasing publicity about the hazards of air pollution. This study not only contributes to the literature on the importance of PD in assessing individual preferences, but also provides constructive guidance for policy makers to assess the public’s acceptability of air quality improvement.

Latent variables play a crucial role in psychometric research, yet traditional models often struggle to address context-dependent effects, ambivalent states, and non-commutative measurement processes. This study proposes a quantum-inspired framework for latent variable modeling that employs Hilbert space representations, allowing questionnaire items to be treated as pure or mixed quantum states. By integrating concepts such as superposition, interference, and non-commutative probabilities, the framework captures cognitive and behavioral phenomena that extend beyond the capabilities of classical methods. To illustrate its potential, we introduce quantum-specific metrics—fidelity, overlap, and von Neumann entropy—as complements to correlation-based measures. We also outline a machine-learning pipeline using complex and real-valued neural networks to handle amplitude and phase information. Results highlight the capacity of quantum-inspired models to reveal order effects, ambivalent responses, and multimodal distributions that remain elusive in standard psychometric approaches. This framework broadens the multivariate analysis theoretical and methodological toolkit, offering a dynamic and context-sensitive perspective on latent constructs while inviting further empirical validation in diverse research settings.