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Causal-formative indicators are often used in social science research. To achieve identification in causal-formative indicator modeling, constraints need to be applied. A conventional method is to constrain the weight of a formative indicator to be 1. The selection of which indicator to have the fixed weight, however, may influence statistical inferences of the structural path coefficients from the causal-formative construct to outcomes. Another conventional method is to use equal weights (e.g., 1) and assumes that all indicators equally contribute to the latent construct, which can be a strong assumption. To address the limitations of the conventional methods, we proposed an alternative constraint method, in which the sum of the weights is constrained to be a constant. We analytically studied the relations and interpretations of structural path coefficients from the constraint methods, and the results showed that the proposed method yields better interpretations of path coefficients. Simulation studies were conducted to compare the performance of the weight constraint methods in causal-formative indicator modeling with one or two outcomes. Results showed that higher biases in the path coefficient estimates were observed from the conventional methods compared to the proposed method. The proposed method had ignorable bias and satisfactory coverage rates in the studied conditions. This study emphasizes the importance of using an appropriate weight constraint method in causal-formative indicator modeling. [ABSTRACT FROM AUTHOR]

Abstract Background The main challenge in heritage education is to identify the verbs—and their hierarchical relations—that explain heritage learning as based on empirical evidence. The Heritage Learning Sequence (HLS) selects seven verbs (Knowing-Understanding-Respecting-Valuing-Caring-Enjoying-Transmitting) on the basis of (a) theoretical studies, (b) analyses of international standards, and (c) evaluation of heritage education programs. The study has the following objectives: (a) to clarify the heritage learning process; (b) to test a theoretical model that groups the verbs that make up the Heritage Learning Sequence (HLS), as well as the relationships between them; (c) to identify possible sub-models that explain the different heritage learning itineraries. Methods The Q-Herilearn scale (previously calibrated using SEM and IRT models) was administered to $$N = 1454$$ N = 1454 individuals, focusing on seven factors (corresponding to each HLS verb) that measure heritage learning. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used as a general analytical strategy. Findings The results obtained provided sufficient guarantees to validate the HLS and showed the adequate explanatory and predictive power and general fit of the proposed model (Heritage Learning Model); all twelve hypothesized direct influence relations between the main verbs that define heritage learning were confirmed. The statistical significance values suggested the existence of other internal subsequences that could be explored in further studies. Contribution Learning modeling provides a key structural framework for (a) the design of effective, efficient, and comprehensive tools to measure heritage learning and (b) their operationalization in heritage education designs.

Translation abstract The cable net models for the Olympic roof landscape in Munich. Last witnesses of model analysisAs part of the research project “Last Witnesses measurement models in civil engineering – scientific significance and preservation”, funded by the DFG in the priority programme 2255 ”Construction as Cultural Heritage“, the few surviving measurement models from the era of model analysis were examined and documented. This report provides insight into the physical models built for the Munich Olympic roof and their use in the planning process. The limitations of the model analysis method for large cable net constructions become clear and how this promoted digitalization in architecture and structural engineering. [ABSTRACT FROM AUTHOR]

Background: The main challenge in heritage education is to identify the verbs—and their hierarchical relations—that explain heritage learning as based on empirical evidence. The Heritage Learning Sequence (HLS) selects seven verbs (Knowing-Understanding-Respecting-Valuing-Caring-Enjoying-Transmitting) on the basis of (a) theoretical studies, (b) analyses of international standards, and (c) evaluation of heritage education programs. The study has the following objectives: (a) to clarify the heritage learning process; (b) to test a theoretical model that groups the verbs that make up the Heritage Learning Sequence (HLS), as well as the relationships between them; (c) to identify possible sub-models that explain the different heritage learning itineraries. Methods: The Q-Herilearn scale (previously calibrated using SEM and IRT models) was administered to N = 1454 individuals, focusing on seven factors (corresponding to each HLS verb) that measure heritage learning. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used as a general analytical strategy. Findings: The results obtained provided sufficient guarantees to validate the HLS and showed the adequate explanatory and predictive power and general fit of the proposed model (Heritage Learning Model); all twelve hypothesized direct influence relations between the main verbs that define heritage learning were confirmed. The statistical significance values suggested the existence of other internal subsequences that could be explored in further studies. Contribution: Learning modeling provides a key structural framework for (a) the design of effective, efficient, and comprehensive tools to measure heritage learning and (b) their operationalization in heritage education designs. [ABSTRACT FROM AUTHOR]

Many methods have been developed since the 1980s to measure Brand Equity (BE). These methods range from theoretical and pragmatic approaches to qualitative methods that determine the rankings of remembrance, emotionality, and valuation based on the likelihood of purchasing a good, or service. The goal of this systematic review was to identify gaps in the literature and provide recommendations for future research by analyzing and summarizing the available data on models used to quantify BE. A systematic search of Dialnet, Ebsco, Google Scholar, Isi We of Knowledge, Redalyc, Science Direct, Scopus, Scielo, and Web of Science was performed using PRISMA principles. Of the 551 abstracts screened, only 23 met inclusion criteria. The review showed that no model allows for a comprehensive evaluation of BE. This makes it easier for us to understand and recognize their benefits, and drawbacks. Although there are many variations, none has been adequately verified to be reliably quantified. The combination of variables between the models indicates that the measurement does not produce an agreement between the theoretical and pragmatic. Everybody tries to appreciate the intangible, yet they all overlook branding messaging. Our findings shows that a thorough proposal incorporating a model that enables the inclusion of both tangible and intangible BE factors is required.

Just as receptive emotional abilities, productive emotional abilities are essential for social communication. Although individual differences in receptive emotional abilities, such as perceiving and recognizing emotions, are well-investigated, individual differences in productive emotional abilities, such as the ability to express emotions in the face, are largely neglected. Consequently, little is known about how emotion expression abilities fit in a nomological network of related abilities and typical behavior. We developed a multitask battery for measuring the ability to pose emotional expressions scored with facial expression recognition software. With three multivariate studies (n1 = 237; n2 = 141; n3 = 123), we test competing measurement models of emotion posing and relate this construct with other socio-emotional traits and cognitive abilities. We replicate the measurement model that includes a general factor of emotion posing, a nested task-specific factor, and emotion-specific factors. The emotion-posing ability factor is moderately to strongly related to receptive socio-emotional abilities, weakly related to general cognitive abilities, and weakly related to extraversion. This is strong evidence that emotion posing is a cognitive interpersonal ability. This new understanding of abilities in emotion communication opens a gateway for studying individual differences in social interaction. [ABSTRACT FROM AUTHOR]

Surveys often add reverse-coded questions to monitor respondents with insufficient effort responses (IERs) but often wrongly assume that all respondents consistently answer all questions with full effort. By contrast, this study expanded the mixture model for IERs and ran a simulation via LatentGOLD to show the harmful consequences of ignoring IERs to positively and negatively worded questions: less test reliability, bias and less accuracy in slope and intercept parameters. We showed its practical application to two public data sets: Machiavellianism (five-point scale) and self-reported depression (four-point scale). [ABSTRACT FROM AUTHOR]

Rapid advances in psychology and technology open opportunities and present challenges beyond familiar forms of educational assessment and measurement. Viewing assessment through the perspectives of complex adaptive sociocognitive systems and argumentation helps us extend the concepts and methods of educational measurement to new forms of assessment, such as those involving interaction in simulation environments and automated evaluation of performances. I summarize key ideas for doing so and point to the roles of measurement models and their relation to sociocognitive systems and assessment arguments. A game-based learning assessment SimCityEDU: Pollution Challenge! is used to illustrate ideas. [ABSTRACT FROM AUTHOR]

In order to explore the effective path to improve the efficiency of industrial water pollution governance efficiency (WGE), this study takes 27 prefecture-level cities in the Yangtze River Delta urban agglomeration in China as the research object, measures WGE through the improved SBM model, and tests the impact of WGE drivers using the spatial Durbin model (SDM). The study found that environmental pollution governance investment (EPGI) is positively correlated with WGE, and industrial agglomeration status (IAS) has inhibitory effects on the improvement of WGE. By testing IAS2, it was determined that the impact of IAS on WGE has a "U"-shaped relationship. The direct impact of EPGI on WGE is 0.5016, and the indirect impact on WGE is 0.6428; the direct impact of IAS on WGE is −0.3036, and the indirect impact on WGE is −0.5158. Among the other tested impact drivers, per capita GDP (PCG), industrial structure (IS), and level of technological innovation (TIL) are positively correlated with the dependent variable WGE, while energy consumption intensity (ECI), environmental regulation intensity (ERI), and degree of openness to foreign investment (FIR) are negatively correlated with the dependent variable WGE. In addition to the impact of the aforementioned main drivers, IAS and EPGI, these six drivers also largely influence and determine the final impact on WGE. [ABSTRACT FROM AUTHOR]

We review the development of path model fit measures for latent variable models and highlight how they are different from global fit measures. Next, we consider findings from two published simulation articles that reach different conclusions about the effectiveness of one path model fit measure (RMSEA-P). We then report the results of a new simulation study aimed at resolving the questions of whether and how the RMSEA-P should be used by organizational researchers. These results show that the RMSEA-P and its confidence interval is very effective with multiple indicator models at identifying misspecifications across large and small sample sizes and is effective at identifying true models at moderate to large sample sizes. We conclude with recommendations for how the RMSEA-P can be incorporated along with other information into model evaluation. [ABSTRACT FROM AUTHOR]

Survival analysis has several advantages, including the ability to handle incomplete data. In general, the explanatory variables used in survival analysis are manifest variables. In other fields such as social sciences, variables such as perception, attitudes, and psychology are often involved in statistical analysis. This research aims to model cox proportional hazard regression on data with latent variable types. Research was conducted in the banking sector using Likert scale questionnaire data. It will examine how the assessment of the 5C variable in the form of a latent variable relates to the time of delay in credit payments. Confirmatory Factor Analysis (CFA) was used to form a re ectively latent variable indicator model which was then formed into a cox proportional hazards regression model. The results show that all 5C variable assessments in uence the speed of credit payments. The novelty in this research lies in the use of indicator analysis models to form latent variables before undergoing survival. The resulting interpretation and conclusions are expected to provide more in-depth information because they include the relationship between survival time and indicators. [ABSTRACT FROM AUTHOR]

Just as receptive emotional abilities, productive emotional abilities are essential for social communication. Although individual differences in receptive emotional abilities, such as perceiving and recognizing emotions, are well-investigated, individual differences in productive emotional abilities, such as the ability to express emotions in the face, are largely neglected. Consequently, little is known about how emotion expression abilities fit in a nomological network of related abilities and typical behavior. We developed a multitask battery for measuring the ability to pose emotional expressions scored with facial expression recognition software. With three multivariate studies (n1 = 237; n2 = 141; n3 = 123), we test competing measurement models of emotion posing and relate this construct with other socio-emotional traits and cognitive abilities. We replicate the measurement model that includes a general factor of emotion posing, a nested task-specific factor, and emotion-specific factors. The emotion-posing ability factor is moderately to strongly related to receptive socio-emotional abilities, weakly related to general cognitive abilities, and weakly related to extraversion. This is strong evidence that emotion posing is a cognitive interpersonal ability. This new understanding of abilities in emotion communication opens a gateway for studying individual differences in social interaction.

Finke, Scheiner, Giurfa, and Avarguès-Weber (2023) published correlational data on the performance of honeybees (Apis mellifera) in three learning tasks (associative, reversal, and negative patterning, capturing the domains of operant conditioning , executive-functioning-like ability , and inhibition plus configural processing , respectively) evaluated under both visual and olfactory stimulus conditions. They speculate that general cognitive ability (GCA) may be weakly causing all-positive correlations between performance in these different learning modalities, but do not formally test this possibility. A factor-analytic model applied to Finke et al. (2023) data revealed the presence of two perfectly congruent GCA factors (one for each stimulus condition). Both exhibited all-positive loadings, with the visual factor accounting for 46.8% of the performance variance and the olfactory factor accounting for 52.3%. Diagnostic statistics confirmed that in both stimulus conditions, the correlation matrices were adequate for factor analysis. These findings support extant hypotheses that GCA influences covariation between cognitive measures in honeybees, and constitute the first formal potential demonstration of GCA in an invertebrate. It is argued that GCA might be ubiquitous with respect to metazoans possessing organized nervous systems, perhaps because it convergently evolved multiple times in independent phylogenies, this being a key prediction of Christopher Chabris' Law of General Intelligence. Indeed, GCA has now been identified in insect, avian, mammal, and fish taxa. Some "primordial" aspects of GCA may even by basal to metazoans, and experiments employing Caenorhabditis elegans are suggested that could potentially shed light on such aspects. The findings are also strikingly inconsistent with evolutionary and comparative psychological theories positing a "modules first" understanding of cognitive evolution, such as one recent proposal that smaller brains cannot accommodate structures that give rise to GCA. Other theoretical implications of these findings are discussed. • The present study performed exploratory factor analyses on correlational matrices based on cognitive data on honey bees. • Horn's parallel models recommended the extraction of a general cognitive ability factor. • The measurement models accounted from 46.8% to 52.3% of the variance. • Principal Axis Factor analyses supported the presence of a general cognitive ability factor. • This factor loaded positively onto all cognitive indictors. [ABSTRACT FROM AUTHOR]