A new strategy model for reducing industrial pollutants using structural equation modeling (SEM): a case study.

Economic developments over recent decades have led to environmental issues. Industries have been introduced among the main causes of environmental degradation. From this perspective, this research work identifies strategies for reducing industrial pollutants. In the first part, the factors concerned and their impacts on mitigating industrial pollutants are identified. Then, the initial model is developed. The case examined here is Mashhad Industrial Zone, Mashhad, Iran, wherein the questionnaire was distributed. Given the non-normal data in this study, the initial model fit is further measured by structural equation modeling (SEM), using SmartPLS. Upon the model fit confirmation, the research hypotheses, i.e., the factors affecting the reduction of industrial pollutants, are assessed. The results indicated that, among the variables examined, seven components can significantly prevent industrial pollutants. In view of this, green industry status quo with the effect size of 0.3 has the greatest possible impact on diminishing industrial pollutants. The next uppermost strategies for this purpose are government incentives, management commitment, green product marketing, competitive strategies, government oversight, and political issues, with the effect size values of 0.21, 0.20, 0.19, 0.15, 0.12, and 0.09, respectively. Financial issues and government regulations are not directly linked with lowering pollutants emanating from industries.

The Accuracy of Bayesian Model Fit Indices in Selecting Among Multidimensional Item Response Theory Models.

Item response theory (IRT) models are often compared with respect to predictive performance to determine the dimensionality of rating scale data. However, such model comparisons could be biased toward nested-dimensionality IRT models (e.g., the bifactor model) when comparing those models with non-nested-dimensionality IRT models (e.g., a unidimensional or a between-item-dimensionality model). The reason is that, compared with non-nested-dimensionality models, nested-dimensionality models could have a greater propensity to fit data that do not represent a specific dimensional structure. However, it is unclear as to what degree model comparison results are biased toward nested-dimensionality IRT models when the data represent specific dimensional structures and when Bayesian estimation and model comparison indices are used. We conducted a simulation study to add clarity to this issue. We examined the accuracy of four Bayesian predictive performance indices at differentiating among non-nested- and nested-dimensionality IRT models. The deviance information criterion (DIC), a commonly used index to compare Bayesian models, was extremely biased toward nested-dimensionality IRT models, favoring them even when non-nested-dimensionality models were the correct models. The Pareto-smoothed importance sampling approximation of the leave-one-out cross-validation was the least biased, with the Watanabe information criterion and the log-predicted marginal likelihood closely following. The findings demonstrate that nested-dimensionality IRT models are not automatically favored when the data represent specific dimensional structures as long as an appropriate predictive performance index is used.

The InterModel Vigorish as a Lens for Understanding (and Quantifying) the Value of Item Response Models for Dichotomously Coded Items.

The deployment of statistical models-such as those used in item response theory-necessitates the use of indices that are informative about the degree to which a given model is appropriate for a specific data context. We introduce the InterModel Vigorish (IMV) as an index that can be used to quantify accuracy for models of dichotomous item responses based on the improvement across two sets of predictions (i.e., predictions from two item response models or predictions from a single such model relative to prediction based on the mean). This index has a range of desirable features: It can be used for the comparison of non-nested models and its values are highly portable and generalizable. We use this fact to compare predictive performance across a variety of simulated data contexts and also demonstrate qualitative differences in behavior between the IMV and other common indices (e.g., the AIC and RMSEA). We also illustrate the utility of the IMV in empirical applications with data from 89 dichotomous item response datasets. These empirical applications help illustrate how the IMV can be used in practice and substantiate our claims regarding various aspects of model performance. These findings indicate that the IMV may be a useful indicator in psychometrics, especially as it allows for easy comparison of predictions across a variety of contexts.

Evaluating Close Fit in Ordinal Factor Analysis Models With Multiply Imputed Data.

Multiple imputation (MI) is one of the recommended techniques for handling missing data in ordinal factor analysis models. However, methods for computing MI-based fit indices under ordinal factor analysis models have yet to be developed. In this short note, we introduced the methods of using the standardized root mean squared residual (SRMR) and the root mean square error of approximation (RMSEA) to assess the fit of ordinal factor analysis models with multiply imputed data. Specifically, we described the procedure for computing the MI-based sample estimates and constructing the confidence intervals. Simulation results showed that the proposed methods could yield sufficiently accurate point and interval estimates for both SRMR and RMSEA, especially in conditions with larger sample sizes, less missing data, more response categories, and higher degrees of misfit. Based on the findings, implications and recommendations were discussed.

Evaluating Model Fit of Measurement Models in Confirmatory Factor Analysis.

Confirmatory factor analyses (CFA) are often used in psychological research when developing measurement models for psychological constructs. Evaluating CFA model fit can be quite challenging, as tests for exact model fit may focus on negligible deviances, while fit indices cannot be interpreted absolutely without specifying thresholds or cutoffs. In this study, we review how model fit in CFA is evaluated in psychological research using fit indices and compare the reported values with established cutoff rules. For this, we collected data on all CFA models in Psychological Assessment from the years 2015 to 2020 (NStudies=221). In addition, we reevaluate model fit with newly developed methods that derive fit index cutoffs that are tailored to the respective measurement model and the data characteristics at hand. The results of our review indicate that the model fit in many studies has to be seen critically, especially with regard to the usually imposed independent clusters constraints. In addition, many studies do not fully report all results that are necessary to re-evaluate model fit. We discuss these findings against new developments in model fit evaluation and methods for specification search.

An examination of model fit and measurement invariance of general mental ability and personality measures used in the multilingual context of the Swiss Armed Forces: A Bayesian structural equation modeling approach.

Measurement invariance of psychological test batteries is an essential quality criterion when the test batteries are administered in different cultural and language contexts. The purpose of this study was to examine to what extent measurement model fit and measurement invariance across the two largest language groups in Switzerland (i.e., German and French speakers) can be assumed for selected general mental ability and personality tests used in the Swiss Armed Forces' cadre selection process. For the model fit and invariance testing, we used Bayesian structural equation modeling (BSEM). Because the sizes of the language group samples were unbalanced, we reran the invariance testing with the subsampling procedure as a robustness check. The results showed that at least partial approximate scalar invariance can be assumed for the constructs. However, comparisons in the full sample and subsamples also showed that certain test items function differently across the language groups. The results are discussed regarding the three following issues: First, we critically discuss the applied criterion and alternative effect size measures for assessing the practical importance of non-invariances. Second, we highlight potential remedies and further testing options, that can be applied, once certain items have been detected to function differently. Third, we discuss alternative modeling and invariance testing approaches to BSEM and outline future research avenues.

The effects of careless responding on the fit of confirmatory factor analysis and item response theory models.

It is common to model responses to surveys within latent variable frameworks (e.g., item response theory [IRT], confirmatory factor analysis [CFA]) and use model fit indices to evaluate model-data congruence. Unfortunately, research shows that people occasionally engage in careless responding (CR) when completing online surveys. While CR has the potential to negatively impact model fit, this issue has not been systematically explored. To better understand the CR-fit linkage, two studies were conducted. In study 1, participants' response behaviors were experimentally shaped and used to embed aspects of a comprehensive simulation (study 2) with empirically informed data. For this simulation, 144 unique conditions (which varied the sample size, number of items, CR prevalence, CR severity, and CR type), two latent variable models (IRT, CFA), and six model fit indices (χ2, RMSEA, SRMSR [CFA] and M2, RMSEA, SRMSR [IRT]), were examined. The results indicated that CR deteriorates model fit under most circumstances, though these effects are nuanced, variable, and contingent on many factors. These findings can be leveraged by researchers and practitioners to improve survey methods, obtain more accurate survey results, develop more precise theories, and enable more justifiable data-driven decisions.

DeepQs: Local quality assessment of cryo-EM density map by deep learning map-model fit score.

Cryogenic electron microscopy maps are valuable for determining macromolecule structures. A proper quality assessment method is essential for cryo-EM map selection or revision. This article presents DeepQs, a novel approach to estimate local quality for 3D cryo-EM density maps, using a deep-learning algorithm based on map-model fit score. DeepQs is a parameter-free method for users and incorporates structural information between map and its related atomic model into well-trained models by deep learning. More specifically, the DeepQs approach leverages the interplay between map and atomic model through predefined map-model fit score, Q-score. DeepQs can get close results to the ground truth map-model fit scores with only cryo-EM map as input. In experiments, DeepQs demonstrates the lowest root mean square error with standard method Fourier shell correlation metric and high correlation with map-model fit score, Q-score, when compared with other local quality estimation methods in high-resolution dataset (<=5 Å). DeepQs can also be applied to evaluate the quality of the post-processed maps. In both cases, DeepQs runs faster by using GPU acceleration. Our program is available at http://www.csbio.sjtu.edu.cn/bioinf/DeepQs for academic use.

What Can We Learn from a Semiparametric Factor Analysis of Item Responses and Response Time? An Illustration with the PISA 2015 Data.

It is widely believed that a joint factor analysis of item responses and response time (RT) may yield more precise ability scores that are conventionally predicted from responses only. For this purpose, a simple-structure factor model is often preferred as it only requires specifying an additional measurement model for item-level RT while leaving the original item response theory (IRT) model for responses intact. The added speed factor indicated by item-level RT correlates with the ability factor in the IRT model, allowing RT data to carry additional information about respondents' ability. However, parametric simple-structure factor models are often restrictive and fit poorly to empirical data, which prompts under-confidence in the suitablity of a simple factor structure. In the present paper, we analyze the 2015 Programme for International Student Assessment mathematics data using a semiparametric simple-structure model. We conclude that a simple factor structure attains a decent fit after further parametric assumptions in the measurement model are sufficiently relaxed. Furthermore, our semiparametric model implies that the association between latent ability and speed/slowness is strong in the population, but the form of association is nonlinear. It follows that scoring based on the fitted model can substantially improve the precision of ability scores.

The Effects of Aberrant Responding on Model-Fit Assuming Different Underlying Response Processes.

Aberrant responding on tests and surveys has been shown to affect the psychometric properties of scales and the statistical analyses from the use of those scales in cumulative model contexts. This study extends prior research by comparing the effects of four types of aberrant responding on model fit in both cumulative and ideal point model contexts using graded partial credit (GPCM) and generalized graded unfolding (GGUM) models. When fitting models to data, model misfit can be both a function of misspecification and aberrant responding. Results demonstrate how varying levels of aberrant data can severely impact model fit for both cumulative and ideal point data. Specifically, longstring responses have a stronger impact on dimensionality for both ideal point and cumulative data, while random responding tends to have the most negative impact on data model fit according to information criteria (AIC, BIC). The results also indicate that ideal point data models such as GGUM may be able to fit cumulative data as well as the cumulative model itself (GPCM), whereas cumulative data models may not provide sufficient model fit for data simulated using an ideal point model.

R-package LNIRT for joint modeling of response accuracy and times.

In computer-based testing it has become standard to collect response accuracy (RA) and response times (RTs) for each test item. IRT models are used to measure a latent variable (e.g., ability, intelligence) using the RA observations. The information in the RTs can help to improve routine operations in (educational) testing, and provide information about speed of working. In modern applications, the joint models are needed to integrate RT information in a test analysis. The R-package LNIRT supports fitting joint models through a user-friendly setup which only requires specifying RA, RT data, and the total number of Gibbs sampling iterations. More detailed specifications of the analysis are optional. The main results can be reported through the summary functions, but output can also be analysed with Markov chain Monte Carlo (MCMC) output tools (i.e., coda, mcmcse). The main functionality of the LNIRT package is illustrated with two real data applications.

A Monte Carlo study of IRTree models' ability to recover item parameters.

Item response tree (IRTree) models are theorized to extract response styles from self-report data by utilizing multidimensional item response theory (IRT) models based on theoretical decision processes. Despite the growing popularity of the IRTree framework, there has been little research that has systematically examined the ability of its most popular models to recover item parameters across sample size and test length. This Monte Carlo simulation study explored the ability of IRTree models to recover item parameters based on data created from the midpoint primary process model. Results indicate the IRTree model can adequately recover item parameters early in the decision process model, specifically the midpoint node. However, as the model progresses through the decision hierarchy, item parameters have increased associated error variance. The authors ultimately recommend caution when employing the IRTree framework.

Generalizability of Dynamic Fit Index, Equivalence Testing, and Hu & Bentler Cutoffs for Evaluating Fit in Factor Analysis.

Factor analysis is often used to model scales created to measure latent constructs, and internal structure validity evidence is commonly assessed with indices like RMSEA, and CFI. These indices are essentially effect size measures and definitive benchmarks regarding which values connote reasonable fit have been elusive. Simulations from the 1990s suggesting possible benchmark values are among the most highly cited methodological papers across any discipline. However, simulations have suggested that fixed benchmarks do not generalize well - fit indices are systematically impacted by characteristics like the number of items and the magnitude of the loadings, so fixed benchmarks can confound misfit with model characteristics. Alternative frameworks for creating customized, model-specific benchmarks have recently been proposed to circumvent these issues but they have not been systematically evaluated. Motivated by two empirical applications where different methods yield inconsistent conclusions, two simulation studies are performed to assess the ability of three different approaches to correctly classify models that are correct or misspecified across different conditions. Results show that dynamic fit indices and equivalence testing both improved upon the traditional Hu & Bentler benchmarks and dynamic fit indices appeared to be least confounded with model characteristics in the conditions studied.

The Standardized S-X 2 Statistic for Assessing Item Fit.

The S-X 2 statistic (Orlando & Thissen, 2000) is popular among researchers and practitioners who are interested in the assessment of item fit. However, the statistic suffers from the Chernoff-Lehmann problem (Chernoff & Lehmann, 1954) and hence does not have a known asymptotic null distribution. This paper suggests a modified version of the S-X 2 statistic that is based on the modified Rao-Robson χ 2 statistic (Rao & Robson, 1974). A simulation study and a real data analyses demonstrate that the use of the modified statistic instead of the S-X 2 statistic would lead to fewer items being flagged for misfit.

Filtering artifactual signal increases support for Xenacoelomorpha and Ambulacraria sister relationship in the animal tree of life.

Conflicting studies place a group of bilaterian invertebrates containing xenoturbellids and acoelomorphs, the Xenacoelomorpha, as either the primary emerging bilaterian phylum1,2,3,4,5,6 or within Deuterostomia, sister to Ambulacraria.7,8,9,10,11 Although their placement as sister to the rest of Bilateria supports relatively simple morphology in the ancestral bilaterian, their alternative placement within Deuterostomia suggests a morphologically complex ancestral bilaterian along with extensive loss of major phenotypic traits in the Xenacoelomorpha. Recent studies have questioned whether Deuterostomia should be considered monophyletic at all.10,12,13 Hidden paralogy and poor phylogenetic signal present a major challenge for reconstructing species phylogenies.14,15,16,17,18 Here, we assess whether these issues have contributed to the conflict over the placement of Xenacoelomorpha. We reanalyzed published datasets, enriching for orthogroups whose gene trees support well-resolved clans elsewhere in the animal tree.16 We find that most genes in previously published datasets violate incontestable clans, suggesting that hidden paralogy and low phylogenetic signal affect the ability to reconstruct branching patterns at deep nodes in the animal tree. We demonstrate that removing orthogroups that cannot recapitulate incontestable relationships alters the final topology that is inferred, while simultaneously improving the fit of the model to the data. We discover increased, but ultimately not conclusive, support for the existence of Xenambulacraria in our set of filtered orthogroups. At a time when we are progressing toward sequencing all life on the planet, we argue that long-standing contentious issues in the tree of life will be resolved using smaller amounts of better quality data that can be modeled adequately.19.

Phylogenetic Analysis That Models Compositional Heterogeneity over the Tree.

Molecular sequences in a phylogenetic analysis can differ in composition, and that shows that the process of evolution can change over time. However, models of evolution in common use are homogeneous over the tree, and if used in a phylogenetic analysis with compositionally tree-heterogeneous datasets these models can recover incorrect trees. The NDCH or Node-Discrete Compositional Heterogeneity model is able to model such data by accommodating differences in composition over the tree. Usage, problems, and limitations of this model are discussed, and a modification, the NDCH2 model, is described that can ameliorate some of these problems and limitations. Using these models can greatly increase the fit of the model to the data and can find better tree topologies. These models and various statistical tests are illustrated using a bacterial SSU rRNA dataset. These models are implemented in the software P4, and files for the analyses described here are made available.

Item-Fit Statistic Based on Posterior Probabilities of Membership in Ability Groups.

A novel approach to item-fit analysis based on an asymptotic test is proposed. The new test statistic, χ w 2 , compares pseudo-observed and expected item mean scores over a set of ability bins. The item mean scores are computed as weighted means with weights based on test-takers' a posteriori density of ability within the bin. This article explores the properties of χ w 2 in case of dichotomously scored items for unidimensional IRT models. Monte Carlo experiments were conducted to analyze the performance of χ w 2 . Type I error of χ w 2   was acceptably close to the nominal level and it had greater power than Orlando and Thissen's S - x 2 . Under some conditions, power of χ w 2 also exceeded the one reported for the computationally more demanding Stone's χ 2 ∗ .

Latent Class Cluster Analysis: Selecting the number of clusters.

Latent Class Cluster Analysis (LCCA) is an advanced model-based clustering method, which is increasingly used in social, psychological, and educational research. Selecting the number of clusters in LCCA is a challenging task involving inevitable subjectivity of analytical choices. Researchers often rely excessively on fit indices, as model fit is the main selection criterion in model-based clustering; it was shown, however, that a wider spectrum of criteria needs to be taken into account. In this paper, we suggest an extended analytical strategy for selecting the number of clusters in LCCA based on model fit, cluster separation, and stability of partitions. The suggested procedure is illustrated on simulated data and a real world dataset from the International Computer and Information Literacy Study (ICILS) 2018. For the latter, we provide an example of end-to-end LCCA including data preprocessing. The researcher can use our R script to conduct LCCA in a few easily reproducible steps, or implement the strategy with any other software suitable for clustering. We show that the extended strategy, in comparison to fit indices-based strategy, facilitates the selection of more stable and well-separated clusters in the data. • The suggested strategy aids researchers to select the number of clusters in LCCA • It is based on model fit, cluster separation, and stability of partitions • The strategy is useful for finding separable generalizable clusters in the data.

In search of systems consolidation.

Systems consolidation solves the stability-plasticity-dilemma and is a persuasive theory within the neuroscience of memory. The study by Tallman et al. (this issue) adds to the current literature showing that brain activity changes over time follow a power function in some neocortical areas but not in the hippocampus. In our comment, we suggest that a power function may, however, not be the only model that needs to be considered for such an analysis. We also highlight that reasoning by the absence of statistical significance should be replaced by appropriate statistics (e.g., using superiority or equivalence tests).

Development of Leisure Valuation Assessment Tool for the Elderly.

This study aimed to develop a leisure valuation assessment tool to revitalize leisure activities for the elderly living in the community. The research method, literature review, and Delphi survey were conducted for the expert panel. Then, the leisure value and participatory leisure activity items were derived to form the assessment items. The two Delphi surveys revealed 38 leisure value assessment items and 41 participating leisure activity items. We attempted to verify the model suitability and validity of the leisure value assessment items through confirmatory factor analysis. The verification showed a good fit. Based on the intensive validity test result, AVE (average variance extracted) values were 66 for physical leisure activities, 65 for emotional leisure activities, and 65 for social leisure activities. The conceptual reliability was 0.96 for physical leisure activities, 0.95 for emotional leisure activities, and 0.96 for social leisure activities. Regarding the internal consistency for reliability verification, Cronbach's alpha values for physical leisure, emotional leisure, and social leisure activities were 0.909, 0.925, and 0.955, respectively. Hence, the items were highly interrelated and homogeneous tests that measured the same characteristics. The assessment tool can be used to identify useful information on the leisure activities of the elderly and to activate leisure activities for the elderly.