Latent Variables Research Articles

Integrated deviance information criterion for spatial autoregressive models with heteroskedasticity

In this study, we introduce the integrated deviance information criterion (DIC) for nested and non-nested model selection problems in heteroskedastic spatial autoregressive models. In a Bayesian estimation setting, we assume that the idiosyncratic error terms of our spatial autoregressive model have a scale mixture of normal distributions, where the scale mixture variables are latent variables that induce heteroskedasticity. We first derive the integrated likelihood function by analytically integrating out the scale mixture variables from the complete-data likelihood function. We then use the integrated likelihood function to formulate the integrated DIC measure. We investigate the finite sample performance of the integrated DIC in selecting the true model in a simulation study. The simulation results show that the integrated DIC performs satisfactorily and can be useful for selecting the correct model in specification search exercises. Finally, in a spatially augmented economic growth model, we use the integrated DIC to choose the spatial weights matrix that leads to better predictive accuracy.

Exploring the impact of preservice teacher internship programs on students’ perception of the teaching profession

This study aims to analyze how the preservice teacher internship program influences students’ perception of the teaching profession by examining the variables of reaction, learning experiences, behavior, and the mediating role of results. By examining these variables, the study synthesizes findings from multiple studies and incorporates them. Using a survey conducted among students at a university in Medan, Indonesia, the study collected data electronically through the distribution of a questionnaire via Google Forms. The sample consisted of 252 students, and partial least square structural equation modeling (PLS-SEM) was employed to analyze the data. The outer models (measurement) and inner model (structural relations among latent variables) were validated and evaluated. The results indicate significant positive direct effects of reaction, learning, and behavior on results. Moreover, the results from the preservice teacher internship program have a significant positive effect on students’ perceptions. The study also reveals that results act as a partial mediator in the relationships between behavior and perception, learning and perception, and reaction and perception.

Development of compliance theory based on self-regulation in chronic kidney failure patients on hemodialysis

Self-management in regulating diet, fluids, activities, and medication in chronic renal failure patients which getting hemodialysis is still worse. This research aimed to develop the theory of compliance based on self-regulation to increase patient obedience and awareness. A cross-sectional approach of 130 respondents was recruited using simple random sampling. Structural equation modeling partial least square (SEM-PLS) was the statistical analysis for this study. All indicators in the outer model showed outer loading value >0.7, it declared feasible to predict each of the latent variables. The R-square value of appraisal latent was 0.576, Coping was 0.897, and self-care compliance was 0.713. The model was relevant when applied in other research studies with a Q-Square value=0.832. The goodness of fit (GoF) was 0.832>0.36, which means that the model was applied well. The appraisal variable is the strongest variable that influences coping, based on the hypothesis test. The self-regulation-based compliance model for blood pressure, interdialytic weight gain (IDWG), sodium levels, and functional independence can be developed through analysis of the structural model.

Efficient integration of perceptual variational autoencoder into dynamic latent scale generative adversarial network

AbstractDynamic latent scale GAN is an architecture‐agnostic encoder‐based generative model inversion method. This paper introduces a method to efficiently integrate perceptual VAE into dynamic latent scale GAN to improve the performance of dynamic latent scale GAN. When dynamic latent scale GAN is trained with a normal i.i.d. latent random variable and the latent encoder is integrated into the discriminator, a sum of a predicted latent random variable of real data and a scaled normal noise follows the normal i.i.d. random variable. Since this random variable is paired with real data and follows the latent random variable, it can be used for both VAE and GAN training. Furthermore, by considering the intermediate layer output of the discriminator as the feature encoder output, the VAE can be trained to minimise the perceptual reconstruction loss. The forward propagation & backpropagation for minimising this perceptual reconstruction loss can be integrated with those of GAN training. Therefore, the proposed method does not require additional computations compared to typical GAN or dynamic latent scale GAN. Integrating perceptual VAE to dynamic latent scale GAN improved the generative and inversion performance of the model.

Signal-to-Noise Ratio in Estimating and Testing the Mediation Effect: Structural Equation Modeling versus Path Analysis with Weighted Composites.

Mediation analysis plays an important role in understanding causal processes in social and behavioral sciences. While path analysis with composite scores was criticized to yield biased parameter estimates when variables contain measurement errors, recent literature has pointed out that the population values of parameters of latent-variable models are determined by the subjectively assigned scales of the latent variables. Thus, conclusions in existing studies comparing structural equation modeling (SEM) and path analysis with weighted composites (PAWC) on the accuracy and precision of the estimates of the indirect effect in mediation analysis have little validity. Instead of comparing the size on estimates of the indirect effect between SEM and PAWC, this article compares parameter estimates by signal-to-noise ratio (SNR), which does not depend on the metrics of the latent variables once the anchors of the latent variables are determined. Results show that PAWC yields greater SNR than SEM in estimating and testing the indirect effect even when measurement errors exist. In particular, path analysis via factor scores almost always yields greater SNRs than SEM. Mediation analysis with equally weighted composites (EWCs) also more likely yields greater SNRs than SEM. Consequently, PAWC is statistically more efficient and more powerful than SEM in conducting mediation analysis in empirical research. The article also further studies conditions that cause SEM to have smaller SNRs, and results indicate that the advantage of PAWC becomes more obvious when there is a strong relationship between the predictor and the mediator, whereas the size of the prediction error in the mediator adversely affects the performance of the PAWC methodology. Results of a real-data example also support the conclusions.

Skilful surfing: modelling the optimum method of online health information seeking during pregnancy

PurposeThe purpose of this study was to develop a model of skilful surfing to aid understanding of how best to seek health information, online and offline, during pregnancy.Design/methodology/approachThis study used an observational, descriptive design, using a purpose written questionnaire, factor analysis and structural equation modelling.FindingsAnalysis resulted in the factor loading of five components: online health information seeking behaviour, normalisation, offline support, trust and data saturation. These components were included as latent variables in an SEM to evaluate the credibility, and subsequently confirm the viability of the theory of skilful surfing.Originality/valueTo the authors' knowledge, this study is the first of its kind to successfully model and define what it is to skilfully surf health information online whilst pregnant, with empirical and theoretical underpinnings.

Unveiling the Power of Adaptive InformationTechnology: A Catalyst for Elevating SME Performance in Indonesia – A Case Study in Malang City

This research seeks to comprehend the impact of information technology (IT) utilization on the performance of Small and Medium Enterprises (SMEs) in Malang. The study investigates the perceptions of SMEs stakeholders through a survey involving 90 SMEs respondents in Malang. Employing a questionnaire and utilizing the Partial Least Squares Structural Equation Modeling (PLS-SEM) method for data analysis, the research reveals positive and significant outcomes for latent variables such as performance expectations, business expectations, social influence, hedonic motivation, and habits concerning SMEs behavior in adopting information technology, based on the Unified Theory of Acceptance and Use of Technology (UTAUT 2). However, the latent variable of price value does not exert a significant influence on SMEs behavioral interest in adopting information technology. Additionally, SMEs behavioral interest positively and significantly influences their usage behavior in utilizing information technology, and the subsequent usage behavior has a positive and significant effect on SME performance.

MousiPLIER: A Mouse Pathway-Level Information Extractor Model.

High throughput gene expression profiling measures individual gene expression across conditions. However, genes are regulated in complex networks, not as individual entities, limiting the interpretability of gene expression data. Machine learning models that incorporate prior biological knowledge are a powerful tool to extract meaningful biology from gene expression data. Pathway-level information extractor (PLIER) is an unsupervised machine learning method that defines biological pathways by leveraging the vast amount of published transcriptomic data. PLIER converts gene expression data into known pathway gene sets, termed latent variables (LVs), to substantially reduce data dimensionality and improve interpretability. In the current study, we trained the first mouse PLIER model on 190,111 mouse brain RNA-sequencing samples, the greatest amount of training data ever used by PLIER. We then validated the mousiPLIER approach in a study of microglia and astrocyte gene expression across mouse brain aging. mousiPLIER identified biological pathways that are significantly associated with aging, including one latent variable (LV41) corresponding to striatal signal. To gain further insight into the genes contained in LV41, we performed k-means clustering on the training data to identify studies that respond strongly to LV41. We found that the variable was relevant to striatum and aging across the scientific literature. Finally, we built a web server (http://mousiplier.greenelab.com/) for users to easily explore the learned latent variables. Taken together this study defines mousiPLIER as a method to uncover meaningful biological processes in mouse brain transcriptomic studies.Significance statement RNA-sequencing studies define differential expression of individual genes across conditions. However, genes are regulated in complex networks, not as individual entities. Machine learning models that incorporate biological pathway information are a powerful tool to analyze human gene expression. However, such models are lacking for mouse, despite the vast number of mouse RNA-seq datasets. We trained a mouse pathway-level information extractor model (mousiPLIER) to reduce data dimensionality from over 10,000 genes to 196 'latent variables' that map to known biological pathways. To validate this approach, we applied mousiPLIER to differential expression across mouse brain aging. We identified 26 functional pathways (latent variables) that varied across aging. Finally, we developed a web server to facilitate use of mousiPLIER by the scientific community.

Rational resistance amidst gentle breeze and mild rain: Examining online collective behavior among the Chinese public using the elaborated social identity model.

This research examines internet collective behavior in mainland China during the COVID-19 pandemic, focusing on the factors and characteristics that drive such behavior. The Chinese government initially implemented a conservative and biased policy to contain the spread of the virus, but the sudden lifting of lockdown measures in late 2022 resulted in a surge in infections and scarcity of medical resources. This policy shift led many Chinese internet users to perceive the government's actions as hasty and harsh, prompting them to engage in collective online behavior. The study employed a survey-based approach, collecting 1,626 valid questionnaires, which underwent reliability testing, descriptive statistical analysis, and a difference-in-differences test. A structural equation model (SEM) was then constructed and applied to comprehensively analyze the mediating and moderating effects of latent variables. Ethical considerations were prioritized, with informed consent obtained from all participants, who were provided with detailed information about the study and given sufficient time to review and ask questions. The research yielded three primary conclusions: the Chinese public demonstrated a perception of fairness and exhibited obedience, respect, and cooperation with the government during the epidemic; the observed online collective behavior can be characterized as a moderate and rational form of resistance, explained by the elaborated social identity model (ESIM); and the middle class consistently adopted a self-vulnerability strategy, positioning themselves as beneficiaries of protection to maximize their own interests in epidemic prevention and control. This study shows notable insights into internet collective behavior in mainland China during the COVID-19 pandemic, highlighting perceptions, resistance, and strategies adopted by different segments of the population.

Light-Aware Contrastive Learning for Low-Light Image Enhancement

Low-Light Image Enhancement (LLIE) presents challenges due to texture information loss and uneven illumination, which can distort feature distribution and reduce the quality of the enhanced images. However, current deep learning methods for LLIE only use supervised information from clear images to extract low-light image features, while disregarding the negative information in low-light images (i.e., low illumination and noise). To address these challenges, we propose a novel LLIE method, LACR-VAE, by leveraging the negative information and considering the uneven illumination. In particular, a Light-Aware Contrastive Regularization (LACR) based on contrastive learning is designed to exploit information from both clear and low-light images. The LACR aims to align latent variables of enhanced images with clear images, away from those of low-light images. This allows the method to prioritize essential elements for LLIE and minimize noise and lighting variations. Furthermore, considering the uneven illumination with diverse region sizes and shapes, a Region-CAlibrated Module (RCAM) is present to learn local and global illumination relations among image regions, and an Attention-guided Multi-Scale Module (AMSM) is designed to extract multi-scale features that improve the model’s representation capability. Extensive experiments show that our method achieves superior performance than previous works. Specifically, our method yields a significant enhancement in the NASA testset, achieving an improvement of at least 0.99 in PSNR and 0.0409 in SSIM. Codes and datasets are available at https://github.com/csxuwu/LACR-VAE .

Item performance of the scale for the assessment and rating of ataxia in rare and ultra-rare genetic ataxias.

The Scale for the Assessment and Rating of Ataxia (SARA) is widely used for assessing the severity and progression of genetic cerebellar ataxias. SARA is now considered a primary end point in several ataxia treatment trials, but its underlying composite item measurement model has not yet been tested. This work aimed to evaluate the composite properties of SARA and its items using item response theory (IRT) and to demonstrate its applicability across even ultra-rare genetic ataxias. Leveraging SARA subscores data from 1932 visits from 990 patients of the Autosomal Recessive Cerebellar Ataxias (ARCA) registry, we assessed the performance of SARA using IRT methodology. The item characteristics were evaluated over the ataxia severity range of the entire ataxia population as well as the assessment validity across 115 genetic ARCA subpopulations. A unidimensional IRT model was able to describe SARA item data, indicating that SARA captures one single latent variable. All items had high discrimination values (1.5-2.9) indicating the effectiveness of the SARA in differentiating between subjects with different disease statuses. Each item contributed between 7% and 28% of the total assessment informativeness. There was no evidence for differences between the 115 genetic ARCA subpopulations in SARA applicability. These results show the good discrimination ability of SARA with all of its items adding informational value. The IRT framework provides a thorough description of SARA on the item level, and facilitates its utilization as a clinical outcome assessment in upcoming longitudinal natural history or treatment trials, across a large number of ataxias, including ultra-rare ones.

Approach to the sense of belonging: construct for the marketing of entrepreneurships in higher education

SummaryThis article investigates the potential of belonging as a marketing argument, focusing on customer behaviors driven by this sense of connection with brands. This variable is explored using six robust indicators to define the sense of belonging and its relationship with customer behavior. The research was carried out in the context of Higher Education, highlighting the transformation of this area to offer continuous training and innovative skills. The article highlights the importance of incorporating belonging into marketing strategies, especially for educational institutions seeking to optimize student engagement, especially in those institutions that are the result of ventures and that have given rise to new institutions. This study reveals the sense of belonging of graduates to their institutions and highlights its importance in various sectors. Companies must strive to cultivate a sense of belonging among their customers, using marketing strategies and policies to build lasting relationships and consolidate their ventures. This latent variable has the potential to influence customer behavior and therefore deserves further study.

Leveraging Autoencoders for Better Representation Learning

ABSTRACT The effectiveness of a machine learning algorithm depends to a considerable extent on the representation used for the modeling. It is more critical with medical image data like Parkinson’s, where complexity plays a significant role. The paper proposes a methodology that leverages representation learning in Parkinson’s disease data. It uses seven types of autoencoders as learning techniques, identifying the best among them. The method is validated using three additional measures: Firstly, another dataset of Parkinson’s is used to confirm its effectiveness, thereby making the method dataset-agnostic. Secondly, seven machine-learning techniques formulate the problem in a supervised learning setting by taking the representation given by the best autoencoder as the features and the disease stage as the labels. Lastly, it uses three-dimensionality reduction techniques to visualize these latent variables or representations in a lower dimension. The high accuracy demonstrated at the supervised learning level, and the formation of patterns exhibited at the visualization level demonstrate the effectiveness of the proposed methodology.

The Role of Family Functioning in the Prevention of Depressive Symptoms and Drug Use Among Hispanic Youth

ABSTRACT Background There is an established link between depression and drug use among adolescents. This relationship holds true for Hispanic youth, for whom familial relationships are especially impactful toward health outcomes. Purpose Our purpose was to examine the relationships between family functioning and adolescent and parent depressive symptoms, and the indirect effects of depressive symptoms on the relationship between family functioning and adolescent drug use. Methods Participants were 231 Hispanic adolescents (52.9% female, Mage = 13.90 ± 1.37) and their parents (93.0% female, Mage = 43.24 ± 6.64) enrolled in an RCT. Data on depressive symptoms, drug use, and family functioning were collected and a latent variable was specified using four indicators of family functioning (positive parenting, monitoring, communication, and involvement). A structural equation model approach was utilized. Results Youth family functioning predicted youths’ depressive symptoms which predicted their own drug use. Youths’ depressive symptoms mediated the relationship between youth family functioning and drug use. Discussion Findings suggest that adolescents’ perceptions of family functioning plays a role in preventing depressive symptoms and drug use among Hispanic youth. Translation to Health Education Practice Teaching students of the role that family support and depressive symptoms play in the manifestation of drug use may be an effective approach to preventing drug use.

Impact of construction workers informal safety communication (CWISC) on safety performance on construction sites

PurposeAn understanding of the impact of construction workers informal safety communication (CWISC), a form of parallel safety communication between workers, on safety performance among construction workers is crucial in order to develop effective strategies for improving safety performance in the construction industry. However, research remains scant on the impact of CWISC on safety performance. This study empirically aims to test the relationship between these important constructs.Design/methodology/approachStatistical analysis was used to examine the relationship in a hypothetical model with two latent variables; the exogenous variables represented by two groups of informal safety communication: friends and crew members and the endogenous variables represented by two groups of Safety performance metrics: safety compliance and safety participation, was tested.FindingsThe emergent findings revealed that there is a significant relationship between informal safety communication among crew members and safety compliance, and also between informal safety communication among friends on construction sites and safety participation. These findings emphasize the importance of fostering effective safety communication and collaboration within construction crews, as well as recognizing the influence friendships on safety performance. Stakeholders can leverage on these findings to implement policies to improve safety performance.Originality/valueThe study presents insightful practical knowledge on how CWISC impacts safety performance on construction sites. Practical recommendations for organizations are also proposed, e.g., development of team-building activities, platforms for sharing safety-related information and experiences, mentorship programs and initiatives that encourage social interaction among workers.

How Much Retrieval Ability Is in Originality?

ABSTRACTCreative fluency and originality are pivotal indicators of creative potential. Both have been embedded in hierarchical intelligence models as part of the ability to retrieve information from long‐term memory; an ability that is often measured with indicators of retrieval fluency. Creative fluency and retrieval fluency, both expressed by the count of correct responses, are procedurally highly similar. This raises the question how creative fluency and originality are related with retrieval fluency and how both are predicted by other cognitive abilities. In a multivariate study (N = 320), we found that retrieval fluency is very strongly related with creative fluency (r = .87) and substantially related with originality (r = .59). A combined fluency factor still fitted the data well. Cognitive abilities accounted for 63% variance in fluency and 47% variance in originality. After controlling for established cognitive abilities, latent variables for fluency and originality were unrelated with one another. This suggests that the procedural proximity of the ability to fluently generate either information from long‐term memory or ad‐hoc solutions to unusual tasks and the ability to come up with original ideas needs reconsideration. Locating originality below an overarching retrieval factor is contradicted by the present data.

Identification of cancer risk groups through multi-omics integration using autoencoder and tensor analysis.

Identifying cancer risk groups by multi-omics has attracted researchers in their quest to find biomarkers from diverse risk-related omics. Stratifying the patients into cancer risk groups using genomics is essential for clinicians for pre-prevention treatment to improve the survival time for patients and identify the appropriate therapy strategies. This study proposes a multi-omics framework that can extract the features from various omics simultaneously. The framework employs autoencoders to learn the non-linear representation of the data and applies tensor analysis for feature learning. Further, the clustering method is used to stratify the patients into multiple cancer risk groups. Several omics were included in the experiments, namely methylation, somatic copy-number variation (SCNV), micro RNA (miRNA) and RNA sequencing (RNAseq) from two cancer types, including Glioma and Breast Invasive Carcinoma from the TCGA dataset. The results of this study are promising, as evidenced by the survival analysis and classification models, which outperformed the state-of-the-art. The patients can be significantly (p-value<0.05) divided into risk groups using extracted latent variables from the fused multi-omics data. The pipeline is open source to help researchers and clinicians identify the patients' risk groups using genomics.

Population-level Balance in Signed Networks

Statistical network models are useful for understanding the underlying formation mechanism and characteristics of complex networks. However, statistical models for signed networks have been largely unexplored. In signed networks, there exist both positive (e.g., like, trust) and negative (e.g., dislike, distrust) edges, which are commonly seen in real-world scenarios. The positive and negative edges in signed networks lead to unique structural patterns, which pose challenges for statistical modeling. In this paper, we introduce a statistically principled latent space approach for modeling signed networks and accommodating the well-known balance theory, i.e., “the enemy of my enemy is my friend” and “the friend of my friend is my friend”. The proposed approach treats both edges and their signs as random variables, and characterizes the balance theory with a novel and natural notion of population-level balance. This approach guides us towards building a class of balanced inner-product models, and towards developing scalable algorithms via projected gradient descent to estimate the latent variables. We also establish non-asymptotic error rates for the estimates, which are further verified through simulation studies. In addition, we apply the proposed approach to an international relation network, which provides an informative and interpretable model-based visualization of countries during World War II.

Applications of structural equation modeling in plant functional trait research

1.Plant functional traits, which encompass morphological, physiological, and ecological characteristics, are key to plant adaptation, growth, and development. In recent years, the structural equation model (SEM) has gained widespread use as a powerful statistical tool for studying plant functional traits and conducting research in this field. Its ability to distinguish between direct and indirect effects makes the SEM a robust method for investigating the complex relationships among environment components, traits and ecosystem functions. 2.Here, we review and discuss four commonly used SEMs: (1) the covariance-based structural equation model, (2) the piecewise structural equation model, (3) the Bayesian structural equation model, and (4) the partial least squares structural equation model. We also explore their applications in three typical ecosystems—forest, grassland, and wetland ecosystems—and investigate these forms of SEM in the context of their use in trait-ecosystem function research. 3.Our specific objectives were to: (i) compare the advantages and disadvantages of these four types of SEMs; (ii) analyze the current state of research on SEM applications in plant functional traits across diverse ecosystems; and (iii) highlight new approaches and potential research areas for the future application of SEM in plant functional traits. 4.In this paper, several key findings were obtained: (i) the selection of SEM type is influenced by the different spatial scales of the study. (ii) latent and composite variables were less commonly utilized in recent SEM studies. (iii) while SEMs have proven effective in distinguishing between direct and indirect effects to unravel the complex relationships among multiple variables, indirect effects deserve more attention in general studies. We propose that future applications of SEMs in plant functional traits should incorporate a broader spectrum of traits as well as the trade-offs between them. Larger and more diverse databases of plant functional traits would help make SEM analyses more accurate across different scales.

Estimating software reliability using size-biased modelling

Software testing is an important step in software development where inputs are administered repeatedly to detect bugs present in the software. In this paper, we have considered the estimation of total number of bugs and software reliability as a size-biased sampling problem by introducing the concept of eventual bug size as a latent variable. We have developed a Bayesian generalised linear mixed model (GLMM) using software testing detection data to estimate software reliability and stopping phase. The model uses size-biased approach where the probability of detecting a bug is an increasing function of eventual size of the bug which is as an index for the potential number of inputs that may eventually pass through the bug. We have tested the sensitivity of the reliability estimates by varying the number of inputs and detection probability via a simulation study and have found that the key parameters could be accurately estimated. Further, we have applied our model to two empirical data sets – one from a commercial software and the other from ISRO launch mission software testing data set. The hierarchical modelling approach provides a unified modelling framework that may find applications in other fields (e.g. hydrocarbon explorations) apart from software management.