Fit Measures Research Articles

The link among X-ray spectral properties, AGN structure, and the host galaxy

In this work, we compare the supermassive black hole (SMBH) and host galaxy properties of X-ray obscured and unobscured AGN. For that purpose, we used ∼35 000 X-ray detected AGN in the 4XMM-DR11 catalogue for which there are available measurements for their X-ray spectral parameters, such as the hydrogen column density, NH, and photon index, Γ, from the XMM2Athena Horizon 2020 European project. We constructed the spectral energy distributions (SEDs) of the sources, and we calculated the host galaxy properties via SED fitting analysis, utilising the CIGALE code. We applied strict photometric requirements and quality selection criteria to include only sources with robust X-ray and SED fitting measurements. Our sample consists of 1443 AGN. In the first part of our analysis, we used different NH thresholds (1023 cm−2 or 1022 cm−2) while also taking into account the uncertainties associated with the NH measurements in order to classify these sources as obscured and unobscured (or mildly obscured). We find that obscured AGN tend to live in more massive systems (by ∼0.1 dex) that have a lower star-formation rate, SFR, (by ∼0.25 dex) compared to their unobscured counterparts. However, only the difference in stellar mass, M*, appears statistically significant (> 2σ). The results do not depend on the NH threshold used to classify AGN. The differences in M* and SFR are not statistically significant for luminous AGN (log (LX,2−10 KeV/erg s−1) > 44). Our findings also show that unobscured AGN have, on average, higher specific black hole accretion rates, λsBHAR, compared to their obscured counterparts, a parameter which is often used as a proxy of the Eddington ratio. In the second part of our analysis, we cross-matched the 1443 X-ray AGN with the SDSS DR16 quasar catalogue of Wu and Shen to obtain information on the SMBH properties of our sources. This resulted in 271 type 1 AGN at z < 1.9. Our findings show that type 1 AGN with increased NH (> 1022 cm−2) tend to have higher black hole masses, MBH, compared to AGN with lower NH values at similar M*. The MBH/M* ratio remains consistent for NH values below 1022 cm−2, but it exhibits signs of increasing at higher NH values. Finally, we detected a correlation between Γ and Eddington ratios, but only for type 1 sources with NH < 1022 cm−2.

MAKING BAYESIAN DISEASE MAPPING EASY AND INTERACTIVE: AN R SHINY APPLICATION

Spatial analysis of count data is important in epidemiology and other domains to identify spatial patterns. While Bayesian spatial models are a popular approach, they do require detailed knowledge of the process for model fitting, checking, and visualising results. Although a number of R packages are available to simplify running the model, there are still complexities when checking the model. This paper aims to provide a user-friendly and interactive R Shiny web application for the analysis of spatial data using Bayesian spatial Conditional Autoregressive Leroux models. The web application is built with the integration of the R packages shiny and CARBayes. The required data are the number of cases, population, and optionally some covariates for each region. In this case, we used Covid-19 data in 2021 in South Sulawesi province, Indonesia. This application enables fitting a Bayesian spatial CAR Leroux model under several hyperpriors and selecting the most appropriate through comparing several goodness of fit measures. The application also enables checking convergence, plus obtaining and visualising in an interactive map the relative risk of disease for each region.

Generalized linear models for symbolic polygonal data

Symbolic data analysis data has provided several advances in regression models concerning the type of symbolic variable. Due to the advantages of using symbolic polygonal data, this paper introduces a linear regression approach for polygonal data based on the generalize linear model theory that provides a unified method to broad range of modeling problems for different types of response as asymmetric continuous and discrete. Ordinary polygonal residuals and a way for finding model inadequacies are presented. Moreover, a quality measure of fit for polygons is also proposed in this paper. Experimental evaluation results illustrate the usefulness of the proposed approach regarding synthetic and real polygonal data.

Why Do Bi-Factor Models Outperform Higher-Order g Factor Models? A Network Perspective.

Bi-factor models of intelligence tend to outperform higher-order g factor models statistically. The literature provides the following rivalling explanations: (i) the bi-factor model represents or closely approximates the true underlying data-generating mechanism; (ii) fit indices are biased against the higher-order g factor model in favor of the bi-factor model; (iii) a network structure underlies the data. We used a Monte Carlo simulation to investigate the validity and plausibility of each of these explanations, while controlling for their rivals. To this end, we generated 1000 sample data sets according to three competing models-a bi-factor model, a (nested) higher-order factor model, and a (non-nested) network model-with 3000 data sets in total. Parameter values were based on the confirmatory analyses of the Wechsler Scale of Intelligence IV. On each simulated data set, we (1) refitted the three models, (2) obtained the fit statistics, and (3) performed a model selection procedure. We found no evidence that the fit measures themselves are biased, but conclude that biased inferences can arise when approximate or incremental fit indices are used as if they were relative fit measures. The validity of the network explanation was established while the outcomes of our network simulations were consistent with previously reported empirical findings, indicating that the network explanation is also a plausible one. The empirical findings are inconsistent with the (also validated) hypothesis that a bi-factor model is the true model. In future model selection procedures, we recommend that researchers consider network models of intelligence, especially when a higher-order g factor model is rejected in favor of a bi-factor model.

A mixed methods UTAUT2-based approach to understanding unified payments interface adoption among low-income users

The Unified Payments Interface (UPI) represents a revolutionary advancement in mobile payment systems and has been primarily embraced by the middle and high-income segments of the Indian population. Its uptake among the low-income or those at the bottom-of-the-pyramid (BOP), characterized by individuals with an annual income less than USD 3,175, remains notably low, necessitating prompt investigation. This study endeavors to explore and validate contextual determinants influencing the development of behavioral intention to use UPI among BOP users. Under the mixed method approach, 26 interviews with active UPI users were conducted in the first phase. The collected data were subjected to deductive thematic analysis and the resulting factors were fused with the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model to adapt it to the BOP requirements. In the second phase, responses from 423 potential UPI users were collected and scrutinized using structural equation modelling. The data analysis unveiled that the path coefficients for social influence (0.527), performance expectancy (0.242), perceived security risk (–0.166), knowledge (0.138), price value (0.123), facilitating conditions (0.119), and social benefits (0.096) were statistically significant in impacting user intentions. The model fit measures of the structural model fell within an acceptable range, and collectively, these factors elucidated 52% of the variance in behavioral intentions. It is recommended that marketers should leverage the interconnected nature of BOP communities to enhance awareness on functionality, subjective utility, social benefits, word-of-mouth, and security issues. This strategy aims to overcome barriers and boost UPI adoption among the BOP.

Evaluating the Performance of Mixed Zero-Inflated Poisson Regression Models with Time-dependent and Time-independent Covariates

One of the issues that researchers may encounter in count data is having many zeros. One of the solutions to model these data is using zero-inflated Poisson (ZIP) regression models. Recently, researchers have started to model longitudinal count data with time-dependent covariates. However, it has not been considered whether a model with time-dependent covariates provides a better fit than a model with time-independent covariates. In this paper, the fit between a mixed ZIP model with time-dependent covariates and a mixed ZIP model with time-independent covariates is compared using simulation. Using the deviance information criterion as a measure of fit, we found that the model with time-dependent covariates exhibits a better fit than the model with time-independent covariates. KEYWORDS correlated data, count data, excess zeros, longitudinal, mixed models, model fit

Evaluation of students’ environmental attitude instruments: exploratory and confirmatory factor analysis

One’s environmental attitude can predict human behavior toward the environment. This study aimed to validate the environmental attitude instrument with the science, technology, engineering, and mathematics in ethnoscience-integrated (Ethno-STEM) approach. The questionnaire consisted of 16 items and was tested on 159 eighth-grade students. Data were analyzed using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The communality value of three instrument items was less than 0.5, so they were excluded in EFA stage 1. The EFA stage 2 test results showed that the 13 instrument items had a factor loading of more than 0.5 and were categorized into five factors. The CFA stage 1 was conducted using linear structural relations (LISREL) and obtained thet-value for all items in factors 1-4 more than the t-table, so they were declared valid. Factor 5 was omitted in CFA test stage 2 due to invalid items. The CFA test results showed that the t-count of the four factors was more than the t-table. Based on the goodness of fit value, the model and measurement matrices were the same (model fit). Thus, the environmental attitude instrument items are declared valid, and the model is fit. The instrument can be used to measure environmental attitudes.

A novel statistical approach to deal with spatial bias in maturity ogive estimation

The proportion of mature fish at length is one of the most important population attributes when evaluating reproductive potential for fish stock assessment purposes. Bias in maturity ogive parameters can lead to fishery management decisions based on misspecified biological reference points. These parameters can vary spatially and temporally, and this variability should be understood and included in the assessment models. However, integrating this variability becomes challenging when specific spatial-dependent ogives cannot be used in the stock assessment model. Hence, this study proposes a novel use of a multivariate response Bayesian regression model, employing an integrated nested Laplace approximation to estimate a single global maturity ogive using data from various spatial areas. This model explicitly accounts for differences in the sampling process and combines information from different areas to estimate shared maturity ogive parameters using joint-likelihood procedures. The model is applied to the European hake stock in ICES (International Council for the Exploration of the Sea) Divisions 27.8.c and 27.9.a, serving as a practical guide. In this model, we have considered different predictors to handle the relationship between the probability of being mature and the length and year covariates. Our results suggest that the logistic formulation correctly captures the relationship between the probability of being mature and length. For year variability, including a year factor covariate or year random effect in the predictor model produces similar values of goodness of fit measures.

Modeling collision risk for unsafe lane-changing behavior: A lane-changing risk index approach

The lane-changing maneuvers are challenging and contributes to traffic accidents and crashes. They are complicated task that often lead to an increased risk of vehicle collisions and deteriorates traffic flow. This paper proposes a vehicle collision risk model based on vehicle lane-changing movement for autonomous vehicles using the probability threat assessment approach and the lane-changing risk index (LCRI). Given two vehicles, A and B, we first estimate the movements of vehicle B using a GPS device, which accurately identifies the vehicle while changing lanes. We then evaluate the collision risks between vehicles using the probabilistic assessment approach to determine whether a collision event occurs at a later stage. Then, we propose the LCRI, which aims to identify the risk level associated with vehicle lane-changing movement and determine the severity of the crash level. In this study, the HighD vehicle trajectory dataset is used to obtain accurate traffic information and help us investigating collision risks among vehicles. The results show that the proposed model can identify the risks of collision ahead of time. Furthermore, the random parameter ordered logit (RPOL) with heterogeneity model achieves better performance and provides a good fit model and measures for improving traffic safety.

Forward-Thinking for Sustainable Shared Mobility Solutions in Amman

This study presents a novel examination of shared mobility’s viability and impact in Amman, Jordan, framed within the context of sustainable urban transportation. A rigorous methodological approach that integrates advanced statistical models including Probit and Decision Tree analyses was utilized to evaluate the propensity of Amman’s residents to adopt shared mobility solutions. Notably, the Ordered Probit Model provided superior model prediction compared to the multinomial logit model, evidenced by a better goodness of fit measure. The results showed that public transportation users would highly use shared mobility services based on cost and reliability, with service convenience emerging as a pivotal factor. The classification tree identified the convenience of the service as the most important factor in adopting shared mobility. The survey data revealed an initial adoption rate of 25.4%, indicating a significant inclination towards shared mobility among respondents. This is pivotal in understanding the current readiness and potential growth of shared mobility in the city. This study is one of the first to quantify the readiness and potential growth of shared mobility in a Middle Eastern urban setting. Furthermore, the impact of this adoption rate on CO2 emissions was conducted. Emission analysis is crucial for assessing the environmental benefits of transitioning towards shared mobility options and aligning with global sustainability goals. Finally, the study extrapolates strategic guidelines for advancing sustainable transportation in Amman, identifying shared mobility options with the highest potential for successful adoption and proposing strategies to foster their implementation. This research contributes a unique perspective to the discourse on urban mobility, particularly in developing urban contexts like Amman, offering valuable insights for policymakers and urban planners.

Will Fit Work Here? Using Multiple Data Sources to Adapt a Student–Institution Fit Instrument to a New Institutional Context

Several authors (Bowman & Denson, 2014; Gilbreath et al., 2011) have claimed that measures of fit between a student and the institution they are attending should be useful for institutional decision-making. These student–institution fit instruments were originally developed for research with specific student populations in one institution, but have not been assessed at other institutions. Institutional research offices rarely assess surveys or other instruments that are developed at one institution to determine whether those same instruments would provide results that are reliable and valid in different institutional contexts. The purpose of this study was to determine if the factor structure of a measure of student–institution fit would be appropriate to use at another institution. A survey that included a student–institution fit instrument developed by Gilbreath et al. (2011) was administered to a random sample of first-year students and new external transfers at a large, public university in the Midwest. Confirmatory factor analysis was used to determine if the underlying factor structure in the original instrument provides an appropriate fit for data obtained from a new sample. In addition, open-ended comments were collected to gain insight into students’ interpretation of each item. The results suggested changes from the original scale to create a more robust measure. Specifically, Great Support Services was moved from the Social Environment fit to the Academic Environment fit, and covariances between multiple items were integrated. Similar methods could be used by institutional researchers at other institutions to gather data on students’ interpretation of survey items, to evaluate the underlying factor structure of external instruments, and to create appropriate modifications to account for their own institutional contexts.

Forecasting hourly WTI oil front monthly price volatility densities

<p>Financial commodity markets have an impact on company values and cash flows, where price movements within frequent time intervals can be both significant and random. Understanding highly frequent price movements is both important and difficult. In this paper, I measured and forecasted volatility for high-frequency (mostly twelve hours per day) WTI Oil front month price movements from 2012 to 2024 (about 40,500 observations). I created a new stochastic volatility model for extracting latent volatility using the non-linear Kalman filter. Stable and strictly ergodic hourly price series, along with the BIC optimal non-linear (generic) general method of moments model coefficients, enabled this process. The latent volatility seemed to separate into two volatility factors. One factor that was very persistent suggested slow mean reversion, and one choppy strongly mean-reverting factor. The data dependence found in the factor volatility series suggested forecasting ability. I applied classical static forecasts and three machine learning regression techniques to predict one-step-ahead volatility. The two volatility factors and one summarizing exponential factor were reported, along with several fit measures, including the root mean square error and Theil's measure of covariance.</p>

Experimental observation of two-dimensional phase in compressed FeF2

Iron difluoride (FeF2) has attracted considerable attention for its physical characteristics and practical applications, and its compression behaviors usually play a key role in the in-depth understanding of this compound. Since its high-pressure crystal structure evolution determining a more profound comprehension remains disputable, we carried out extensive experiments to focus on the pressure-induced structural phase transitions of FeF2. Through in situ high-pressure synchrotron x-ray diffraction measurements, we not only confirmed a reported high-pressure orthorhombic Pbca phase at 11 GPa but also identified an interesting two-dimensional structure with hexagonal close packed symmetry (P-3m1) that appears above 25 GPa at room temperature. Furthermore, the spontaneous strain fitting and electronic transport measurements suggest that its ambient rutile-type structure (P42/mnm) evolves into an orthorhombic structure (Pnnm) through a second-order phase transition at 5 GPa. These experimental results elaborate on the pressure-induced phase transitions of FeF2 on the order of P42/mnm → Pnnm → Pbca → P-3m1, shedding light on a rare three-dimensional to two-dimensional configuration transition in difluorides.

Use of Random Forest Regression Model for Forecasting Food and Commercial Crops of India

Agriculture is the backbone of Indian Economy. Proper forecast of food crops and cash crops are necessary for the government in policy making decisions. The present paper aims to forecast Wheat and Sugarcane yield using Random Forest Regression. For the development of Random Forest models, Yield has been taken as dependent variable and variables like Gross Cropped Area, Maximum Temperature, Minimum Temperature, Rainfall, Nitrogen, Phosphorous Oxide, Potassium Oxide, Minimum Support Price and Area under Irrigation are taken as independent variables for both Wheat and Sugarcane crop. Values of R2 for Wheat and Sugarcane is 0.995 and 0.981 which indicates that the model is a good fit and other performance measures are calculated and results are satisfactory.

Efektivnost preduzeća - studija o strukturi, fokusu i ishodima preduzeća

To obtain and retain high performances enterprises are affected by various constituencies's' needs and expectations which are important part of their organizational operationalization. The research goal is to validate the psychometric properties of a widely used holistic view of enterprises performance defined by competing value framework in context of enterprises in transition economy such as Republic of Srpska, Bosnia and Herzegovina. To fulfil the aim of the research, a quantitative, research design based on cross-section data was carried out to collect and analyses data from 224 organizational representatives. Covariance-based structural equations modeling was used to test the competing values approach and, if needed, to modify a measurement scale that identifies the extent to which sampled enterprises use the measurement criteria of each aspect of effectiveness to validate the effectiveness of enterprises. All models of effectiveness have acceptable reliability and convergent validity, and discriminant validity of each model was established. Model fit measures indicate acceptable goodness-of-fit. Academics and managers may consider using these measures of effectiveness to better understand the performance/effectiveness phenomenon and have better insight into possibilities for enhancement of different aspects of effectiveness. This study represents the first attempt to investigate and validate the presence of mutual dependence of certain aspects of effectiveness based on a competing values approach in a specific research context.

A novel approach for precipitation modeling using artificial intelligence-based ensemble models

Precipitation is the primary component of the hydrologic water cycle and its accurate prediction plays a significant role for the planning, management and design of hydraulic structures. The objective of the study intended to explore a new approach to increase prediction efficiency of precipitation in the arid, semiarid and humid zones. The approach was implemented using relevant data from stations in Iraq and Nigeria. Support vector regression (SVR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system models (ANFIS) were applied for a single modeling of 30 years monthly average precipitation data. Thereafter, a nonlinear ensemble and 2 linear ensemble techniques were applied to improve prediction efficiency and reliability of the single models. Error measures as well as a goodness of fit measure were the criteria employed to assess the performance potential of the models. Based on the results of this work it was found that ensemble modeling could improve prediction accuracy of the single models as much as 38% in the validation phase. It was also confirmed that the performance of artificial intelligence-based models can efficiently be improved by the application of ensemble modeling in the arid, semiarid and humid study stations of Iraq and Nigeria.

Comparison of distance-based spatial weight matrix in modeling Internet signal strengths in Tasikmalaya regency using logistic spatial autoregressive model

To ensure that national development objectives in rural areas are achieved evenly and sustainably, the Government of Indonesia applies the principles of Village Sustainable Development Goals (SDGs), which are derivative programs of SDGs. One of the indicators in measuring the progress and independence of villages in Indonesia is the availability of cellular phone signal access. Cellular phone signals have a vital role because most internet users in Indonesia rely on mobile data connections from cellular operators. However, the signal emitted by a provider tower has a limited range. According to the data of the Developing Villages Index in 2022, Tasikmalaya Regency is one of the regencies with the highest number of villages that have weak signal strength in West Java Province, Indonesia. To examine the effect of distance and height difference between the placement of the nearest provider tower and the location of the Village Office on the internet signal strength category in Tasikmalaya Regency, Logistic Spatial Autoregressive modeling is needed. In this study, the Bayesian Markov-Chain Monte Carlo estimation method was used, because it has advantages in flexibility and computational efficiency. In spatial modeling, there is a spatial weight matrix determined by the researcher’s understanding of the observed phenomenon. The variable observed in this study is signal strength, which has an orientation at a distance. However, there are several types of distance-based spatial weight matrices, such as K-nearest neighbor, radial distance, power distance, and exponential distance. To determine the most suitable distance-based spatial weight matrix in internet signal strength modeling, the four (4) weight matrices were compared based on the goodness of fit measure models, calculated from the confusion matrix. The results of the analysis showed that the radial distance weight matrix with a threshold distance of d = 1.7km is the most suitable use of distance-based spatial weight matrix in internet signal modeling in Tasikmalaya Regency. The weight matrix exerted a positive spatial autocorrelation effect of 57.141%. In addition, the height difference factor between the location of the provider tower with the location of the village office has a greater effect than the horizontal distance.

The role of land inequality in the poverty-forest loss nexus patterns: A case study from Chile

The predominant school of thought argues that poverty is a major cause of deforestation, but several studies have begun to contest this assumption by putting forward that a complex web of factors influences this connection and that multi-patterned nexuses can exist. We explore these nexuses taking southern Chile as a case study and using a Path Analysis for modelling. We relied on secondary data at the land property and municipality levels to construct the exogenous (land inequality, farm and forestry subsidies, and firewood consumption) and endogenous variables of the model (poverty, native forest loss, non-native tree afforestation, ecosystem services supply), which runs at the municipality scale (n = 147). We obtained a very good model adjustment as indicated by common goodness of fit measures. The model's path coefficients showed that land inequality (measured with the Gini coefficient) had a positive direct effect on poverty and forest loss. In turn, poverty had a negative direct effect on forest loss, whereas forest loss had a total negative effect on poverty. Thus, poverty per se does not cause forest loss or vice versa, and rather than a single poverty-forest loss nexus, “many-patterned” nexuses arise. We discus three of these patterns, namely i) development that aggravates poverty, ii) land inequality that aggravates forest loss and poverty, and iii) land inequality that “helps” the cause of forest conservation. These results suggest that policies that seek to solve poverty (e.g., farm subsidies) will not necessarily help prevent forest loss, but neither will neoliberal forest and conservation policies alone. As the SDGs state, solving inequality seems to be a necessary condition for both alleviating poverty and ensuring forest conservation.

Validity and reliability evaluation of the Turkish version of the Clinical Interpersonal Reactivity Index.

This study was conducted to evaluate the validity and reliability of the Turkish version of the Clinical Interpersonal Reactivity Index, an instrument to assess nurses' empathy. A practical empathic approach is valuable for planning nursing interventions suitable for the patient's conditions and needs and increasing patient satisfaction. This is a methodological study. The study was carried out at Muğla Sıtkı Koçman University Training and Research Hospital, recruiting a total of 358 nurses between January and February 2023. Exploratory and confirmatory factor analyses were used to determine and validate the factor structure of the Clinical Interpersonal Reactivity Index. The exploratory factor analysis extracted three factors. According to the confirmatory factor analysis, all goodness of fit measures were within acceptable limits. Cronbach's α values were 0.89 for the total scale with 17 items and between 0.73 and 0.81 for the factors. In test-retest analyses, all intra-class correlation coefficients were higher than 0.7, and the correlations between the first and second applications of the scale were between 0.61 and 0.73. The Turkish version of the Clinical Interpersonal Reactivity Index can be applied as a valid and reliable measurement tool to evaluate empathy in Turkish nurses.

TECHNOLOGY ACCEPTANCE MODEL (TAM) FOR PHARMACEUTICAL MARKETING EXECUTIVES: VALIDATION AND IMPLICATIONS FOR HUMAN RESOURCE MANAGEMENT

The technology acceptance model (TAM) is a popular measure of user adoption and acceptance of technology. The pharmaceutical marketing industry has largely incorporated technology-based applications to enhance operational efficiency, effectiveness, and client engagement in the past decade. No study has explored user acceptance by pharmaceutical executives in the context of technology's impact on performance. The study aims to explore the relationship between perceived ease of use (PEOU), perceived usefulness (PU), and behavioral intention (BIU) in the context of the perceived impact of technology on performance (TechIMP). Hypotheses were tested using factor-based structural equation modeling. A random sample of 282 marketing executives was drawn from pharmaceutical companies in Nigeria using an online questionnaire. The developed model provided acceptable measures of fit and validity. Significant positive relationships exist between PEOU, PU, and BIU, explaining 58% of the variance in TechIMP. PEOU had a stronger impact on BIU compared to PU. BIU was a significant link between PEOU and PU to TechIMP. Multigroup analysis showed key differences between male and female executives. The study adds to the existing literature by extending TAM to include TechIMP. Managers should enhance positive user perception and acceptance by engaging in simulated training before introducing new technology and ensuring flexibility of technology use.