Model-data Fit Research Articles

Psychometric Properties of the Basic Needs Satisfaction in General Scale: A Second-Order CFA Analysis

The study examined the psychometric structure of Basic Needs Satisfaction in General Scale (BNSG-S) with a sample of 495 college students. It compared the original 21-item version (Gagné, 2003) with the shortened 16-item version (Johnston & Finney, 2010), both with and without method effects. A series of confirmatory factor analyses (CFA) suggested a three-factor CFA model with method effect using the 16-item version showed acceptable model-data fit. However, the high correlations among factors posed threats to the discriminant validity. To address the multicollinearity among the factors, our study proposed and tested a second-order CFA model with method effect based on the 16-item shortened version of BNSG-S, which provided good model-data fit. Additionally, Cronbach’s alpha and omega coefficients demonstrated satisfactory reliability. These results supported a higher-order factor structure of the 16-item version, suggesting the needs satisfaction may be interconnected and influenced by a broader overarching factor.

Detecting latent subpopulations in international large-scale assessments by fitting MixIRT models using NUTS

The focus of this study is to use the mixture item response theory (MixIRT) model while implementing the no-U-turn sampler as a technique for investigating the presence of latent classes (i.e., subpopulations) among eighth-grade students who were administered TIMSS 2019 mathematics subtest in paper format from the gulf cooperation council (GCC) countries. One-, two-, and constrained three-parameter logistic MixIRT models with one to four classes were used to fit to the data, where the model data fit was assessed using Bayesian fit indices. The results indicate that multiple latent classes or subpopulations can better reflect the mathematical proficiency of eighth graders from the four GCC countries, and specifically the two-class constrained three-parameter MixIRT model provides a relatively better fit to the data. The results also indicate that when a mixture of several latent classes present, the conventional unidimensional IRT model is limited in providing information for multiple latent classes and shall be avoided. In addition to adding to the existing literature on MixIRT models for international large-scale assessments such as TIMSS on its heterogenous subpopulations from a fully Bayesian approach, this study sheds light on the limitation of conventional unidimensional IRT models and subsequently directs attention to the use of the more complex MixIRT model for such assessments.

Preparation and NH3 adsorption behavior of porous magnesium oxychloride cement-based SrCl2

In addressing challenges in Selective Catalytic Reduction (SCR) systems in vehicles, the development of novel SrCl2 composite materials with high ammonia adsorption and structural stability is crucial. In this context, we employed the porous magnesium oxychloride cement (PMOC) as a carrier material, successfully fabricating a mesoporous PMOC-SrCl2 material via solution impregnation technology. Simultaneously, we employed various characterization techniques such as XRD, full-pore analysis, TG, SEM, TEM, and EDS to examine the material. We conducted static adsorption assessments on numerous candidate materials at 0.1Mpa and 293.15 K. The synthesized C3 series material (Mass ratio of SrCl2 to PMOC = 10:3), solution impregnated for 1 h, exhibited exceptional ammonia adsorption capacity (up to 38.01 mmol·g−1). Through model fitting of experimental data, we discovered that the adsorption process of this material closely aligns with the pseudo-second-order kinetic model and the Langmuir model. Over an adsorption time span of 180 min, the adsorption rate of the composite material increased by 129 % compared to pure SrCl2. In the cycle performance testing phase, we observed that the material exhibited no significant degradation in performance after undergoing 10 cycles of adsorption/desorption. This structurally stable, cost-effective, and readily accessible high-efficiency ammonia adsorption material undoubtedly possesses immense potential to solve the application challenges of ammonia adsorption materials within automotive selective catalytic reduction systems.

Comment on Krüger, L. Decreasing Trends of Chinstrap Penguin Breeding Colonies in a Region of Major and Ongoing Rapid Environmental Changes Suggest Population Level Vulnerability. Diversity 2023, 15, 327

Historical data on chinstrap penguin (Pygoscelis antarctica) breeding population sizes are sparse and sometimes highly uncertain, making it hard to estimate true population trajectories. Yet, information on population trends is desirable as changes in population size can help inform conservation assessments. Recently, Krüger (2023) (Diversity 2023, 15, 327) used chinstrap penguin nest count data to predict breeding colony size trends between 1960 and 2020, to estimate whether the level of population change within three generations exceeded IUCN Red List Criteria for “Vulnerable” populations. Chinstrap penguin population trends are an important research topic, but we caution that Krüger (2023)’s statistical analyses (intended to form the foundation for drawing valid, evidence-based inferences from sparse data) contain fundamental errors that invalidate that paper’s findings. We discuss oversights in several key steps (data processing, exploratory data analysis, model fitting, model evaluation, and prediction) of that paper’s analysis to help others detect and avoid some of the pitfalls associated with estimating population trends via mixed models. We also show through reanalysis that improved statistical modelling can yield better predictions of chinstrap penguin population trends, at least within the range of observed data. This case study highlights (1) the profound influence that seemingly minor differences in modelling procedures (both unintentional errors and other decisions) can have on predictions of population trends, and (2) the substantial inherent uncertainty in population trend predictions derived from sparse, heterogenous data.

Assessment of Item Parameter and Model-Data Fit of 4IR Teachers' Effectiveness Scale in Secondary Schools

In secondary schools in Southwest Nigeria, the Fourth Industrial Revolution Chemistry Teachers Effectiveness Scale (4IRCTES) items were evaluated for discrimination item parameter and model-data fit to the underlying construct. In this study, a descriptive survey research design was employed. The survey encompassed 4,986 Chemistry teachers in the southwestern region of Nigeria. 35 Chemistry teachers made up the study’s sample. 4IRCTES was the instrument utilised to collect the data. Research question two was based on the absolute overall model-data fit, or M2 statistic, whereas research question one used the Multidimensional Graded Response Model (MGRM) of the Item Response Theory (IRT) to analyse the item parameter (item discrimination). Result showed that items of 4IRCTES discriminated well between teachers with low effectiveness and teachers with high effectiveness. Finally, in the study, result showed that the MGRM of the IRT is the substantial model-data fit for the 4IRCTES. The study concluded that the 4IRCTES items effectively distinguished between teachers who were ineffective and those who were effective.

Exploring Multivariate Statistics: Unveiling the Power of Eigenvalues in Wishart Distribution Analysis

This research examines how degrees of freedom and covariance matrix configurations affect Wishart distributed matrix eigenvalue distributions. We use the energy distance metric to compare eigenvalue distributions in Identity, Diagonal, and Structured covariance matrices. Through extensive simulations, we show that degrees of freedom and covariance matrix architectures greatly affect eigenvalue dispersion and energy distance distributions. Statistical models are more reliable with smaller, more stable distributions from higher degrees of freedom. We analyze the eigenvalue distributions of NextEra Energy (NEE), Enphase Energy (ENPH), First Solar (FSLR), SunPower (SPWR), and Brookfield Renewable Partners (BEP) stocks using this analytical methodology. Daily returns and covariance matrices were calculated using daily closing prices from January 1, 2018, to January 1, 2023. Our findings support simulation studies indicating larger degrees of freedom lead to more stable energy distance distributions.The findings of this study are useful in finance, genetics, and environmental studies, where stability and variability of the covariance structure are important. The requirement for higher-dimensional settings and real-world datasets to validate the theoretical framework are our research’s constraints. This research expands Wishart distribution knowledge and provides a solid analytical foundation for data analysis and model fitting in numerous scientific fields.

Layer-wise normalized deep incomplete multiview nonnegative matrix factorization

Deep (semi-)nonnegative matrix factorization based models have demonstrated promising performance in the multiview clustering tasks. However, they are all developed upon the assumption that the multiview datasets are complete in all views, and need to be pre-trained layer-by-layer to get reasonable results. Addressing these issues, an algorithm named layer-wise normalized deep incomplete multiview nonnegative matrix factorization (LWNdimNMF) is presented in this paper to tackle the multiview clustering problem with arbitrary missing views. Specifically, a novel layer-wise normalization strategy is developed to constrain the value distribution of the factor matrix in each layer to ensure the numerical stability of the model and make it robust to the initialization. Another benefit of this strategy is that it can effectively depress the objective function value and make the model fit data well. An instance selection alignment is adopted to fuse the incomplete multiple views by aligning the paired instances. To discover the high-order geometrical information in each incomplete view, a hypergraph regularization is incorporated into the proposed model. An effective iterative optimization algorithm based on the multiplicative updating rules is designed to solve the minimizing problem. Extensive experiments are conducted to evaluate the effectiveness of the proposed model by comparing with several state-of-the-art (SOTA) incomplete multiview clustering (IMC) methods on ten real-world multiview datasets. The source code is available at: https://github.com/GuoshengCui/LWNdimNMF.

Development of a glue bonded shear connections aimed for mass timber composites

Mass timber composites (MTCs) generally require stiff glued connections to meet serviceability design criteria; however, achieving stiff glued connections without conventional pressing infrastructure can necessitate alternative gap filling adhesives. A readily available construction adhesive with potential for use with alternative pressing methods is investigated to develop the needed information for its commercial application in MTCs. The glue line thickness, surface coverage, three contact pressures, and how these parameters influence the load-slip curve of the connection when loaded in pure shear are investigated. The average glue line thickness ranged from 0.76 to 0.33 mm, with lower pressures having thicker glue lines and higher pressures having thinner glue lines. The planning precision was found to be more critical than clamping pressure for glue line thickness. Based on glue bond thickness testing and fabrication experience it is recommended to use 1.2–1.4 mm2 of adhesive per mm width to create full coverage glue bonds without excessive adhesive squeeze out. When tested under pure shear, specimens were observed to fail with a mixture of wood and glue, with more glue failure present as the pressure was increased. The initial stiffness of the connection tended to increase with clamping pressure, while the strength tended to decrease. Two bi-linear data fit models are applied to determine the yield point and simplify the load-slip behaviour for use in design analysis. Overall, the 75% max load method better fit the behaviour and was simpler to apply. The shear strength and stiffness achieved experimentally across all pressures were comparable to other glue connection literature results, and when applied in the design of a 10 m long MTC ribbed panel all pressures achieved near fully composite action. Overall, the adhesive has shown to be an easy to apply and structurally viable connection alternative for MTCs.

Development of Distance Education Attitude Scale for Higher Education Students

Distance education has been constantly developed for decades and became the only choice for education during the COVID-19 pandemic. In the Turkish higher education, distance education practices were carried out for three semesters during the pandemic. Once again, distance education became the only means of education for higher education after the devastating earthquakes that hit Türkiye in February 2023. Assessing the students’ attitudes towards distance education gained importance in order to enhance the learning outcomes of distance education and to define the problems the students had faced. Thus, the researchers aimed to develop a Distance Education Attitude Scale for Higher Education Students in this study. The data were collected from 875 undergraduate students from Turkish state universities. Explanatory Factor Analysis (nEFA= 583) unfolded a two-factor structure with 16 items – explaining 57% of the total variance. For the scale and its two factors, the Cronbach Alpha internal consistency coefficient (.917, .914, .807) and McDonalds Omega coefficient (.920, .925, .811) were found satisfactory. Confirmatory Factor Analysis (nCFA= 292) supported the model-data fit and confirmed the reached structure. The "Distance Education Attitude Scale for Higher Education Students" is found to be valid and reliable. The scale is believed to contribute to the quality and efficiency of tertiary distance education practices.

A Fractional-Order Creep-Damage Model for Carbonaceous Shale Describing Coupled Damage Caused by Rainfall and Blasting

In order to better understand the shear creep behavior of weak interlayers (carbonaceous shale) under the coupling effect of the rainfall dry–wet cycle and blasting vibration, as well as quantitatively characterize the coupled damage of the rainfall dry–wet cycle and blasting vibration, a series of shear creep tests were carried out. The results show that the combined damage of the rainfall dry–wet cycle and blasting vibration greatly intensifies the creep effect of carbonaceous shale, leading to an increase in deceleration creep time, an increase in steady-state creep rate, and a decrease in long-term strength. The coupling damage of the rainfall dry–wet cycle and blasting vibration in carbonaceous shale was quantitatively characterized. Based on the fractional-order theory, a fractional-order creep-damage constitutive model (DNFVP) was established by introducing the Abel dashpot to describe the coupled damage of the rainfall wet–dry cycle and blasting vibration and the nonlinear creep acceleration characteristics. The three-dimensional creep equation of the model was derived. The effectiveness of the DNFVP model was verified through the inversion of model parameters and fitting of experimental data, providing a basis for in-depth research on the long-term stability of high slopes in mines with weak interlayers.

The effect of fat model variation on muscle fat fraction quantification in a cross-sectional cohort.

Spectroscopic imaging, rooted in Dixon's two-echo spin sequence to distinguish water and fat, has evolved significantly in acquisition and processing. Yet precise fat quantification remains a persistent challenge in ongoing research. With adequate phase characterization and correction, the fat composition models will impact measurements of fatty tissue. However, the effect of the used fat model in low-fat regions such as healthy muscle is unknown. In this study, we investigate the effect of assumed fat composition, in terms of chain length and double bond count, on fat fraction quantification in healthy muscle, while addressing phase and relaxometry confounders. For this purpose, we acquired bilateral thigh datasets from 38 healthy volunteers. Fat fractions were estimated using the IDEAL algorithm employing three different fat models fitted with and without the initial phase constrained. After data processing and model fitting, we used a convolutional neural net to automatically segment all thigh muscles and subcutaneous fat to evaluate the fitted parameters. The fat composition was compared with those reported in the literature. Overall, all the observed estimated fat composition values fall within the range of previously reported fatty acid composition based on gas chromatography measurements. All methods and models revealed different estimates of the muscle fat fractions in various evaluated muscle groups. Lateral differences changed from 0.5% to 5.3% in the hamstring muscle groups depending on the chosen method. The lowest observed left-right differences in each muscle group were all for the fat model estimating the number of double bonds with the initial phase unconstrained. With this model, the left-right differences were 0.64% ± 0.31%, 0.50% ± 0.27%, and 0.50% ± 0.40% for the quadriceps, hamstrings, and adductors muscle groups, respectively. Our findings suggest that a fat model estimating double bond numbers while allowing separate phases for each chemical species, given some assumptions, yields the best fat fraction estimate for our dataset.

Identifying the Careless Responders: A Cross-Country Comparison on PISA 2018 Dataset

Careless responding (CR) is one of the major concerns in self-report measures since it affects the quality of collected data. In this research, it was aimed to examine the dataset in terms of CR, to investigate the effects of CR on the psychometric properties of data, and to examine the effectiveness of CR detection methods in a cross-cultural context. Specifically; response time, outlier and long-string indices were used to flag CR and efficiency of using these were compared on PISA 2018 reading related attitude scales for Singapore and Turkey. As a result, it was indicated that the amount of CR was higher for Singapore than Turkey at long-string and response time methods. Furthermore, after removal of CR from dataset, enjoyment level of reading, perception of competence and perception of difficulty of PISA tests scores increased. Another result was that long-string and response time analyses provided significant improvement in model fit and long-string had the highest improvement level. Moreover, removing respondents according to outlier analyses didn’t provide any improvement on model-data fit. In general, results illustrated that careless responding behavior may have important effects on psychometric properties of self-report and screening the data for CR is strongly suggested in future studies.

Brief instruments for measuring nutrition literacy - the Nutrition Health Literacy Scale and the Self-Perceived Food Literacy Scale Short Form

BackgroundA healthy diet is a critical factor in maintaining long-term health. In addition to a health-promoting food environment, the nutrition health literacy (NHL) and food literacy (FL) of the population are important in this context. This paper describes the development and validation of two short instruments to measure the nutrition literacy of the population, used in the Austrian Nutrition Literacy Survey 2021.MethodsAn instrument to measure NHL (Nutrition Health Literacy Scale; NHLS) has been adapted and further developed. To measure FL, the Self-perceived Food Literacy Scale by Poelman et al. has been modified and shortened (SPFL-SF). Validation of the instruments was based on data from a web survey conducted in Austria in 2021 with almost 3,000 participants aged 18 years and older. Exploratory and confirmatory factor analyses were performed to assess the factorial validity/dimensionality of the instruments. Additionally, internal consistency was assessed using Cronbach’s alpha, ordinal alpha, and McDonald’s omega.ResultsBoth instruments demonstrate excellent data-model fit. The NHLS also shows excellent internal consistency (α = 0.91), while the SPFL-SF displays a sufficient internal consistency for all (α between 0.70 and 0.89) but one sub-dimension (resisting temptation α = 0.61). Furthermore, the distribution of the items indicates that the measures are understandable and suitable, as evidenced by the absence of missing values in the sample. In addition, the items of both instruments differ in their level of difficulty or agreement.ConclusionsThe NHLS and SPFL-SF are reliable and valid instruments for measuring NHL and FL in the general adult population. The brief instruments measuring the different aspects of nutrition literacy can be easily used in nutritional or evaluation studies. Further work is required to investigate other aspects of validity.

Impact of Violating Unidimensionality on Rasch Calibration for Mixed-Format Tests

ABSTRACT Mixed-format tests that include both multiple-choice (MC) and constructed-response (CR) items have become widely used in many large-scale assessments. When an item response theory (IRT) model is used to score a mixed-format test, the unidimensionality assumption may be violated if the CR items measure a different construct from that measured by MC items. In this simulation study, we explore the impact of different degrees of violation on the unidimensionality assumption on Rasch item parameter recovery and model-data fit statistics. Our results suggest that, when the unidimensionality assumption is violated for mixed-format tests, both estimated Rasch item parameters and model-data fit statistics are distorted, with greater impact corresponding to higher degrees of violations. The findings from this study may provide guidance for test practitioners for working with mixed-format tests within the Rasch context.

NIMO: A graphical user interface‐based R package for species distribution modelling

Abstract Species distribution modelling (SDM) is essential for understanding and predicting biodiversity patterns globally. However, the complexities of data preparation, algorithm selection and model evaluation can present significant challenges. This paper introduces NIMO, an R package with a user‐friendly graphical interface aimed at streamlining the entire SDM workflow. In SDM, data preparation is a crucial and time‐consuming process, which requires the acquisition of spatiotemporal occurrence records. The Global Biodiversity Information Facility (GBIF) database plays a key role in addressing data scarcity by providing extensive community science‐based repositories. However, compiling, aligning and verifying spatial references can be laborious. NIMO addresses these challenges by offering a flexible workflow that allows users to more easily query biodiversity data. NIMO organizes its features into three main steps: pre‐modelling, modelling and post‐modelling. It empowers users to optimize data for modelling with a variety of algorithms. NIMO allows for default hyperparameter values or extensive tuning, providing users with flexibility in selecting models. Practical implication: To demonstrate the effectiveness of NIMO, we presented a case study focusing on the critically endangered white‐backed vulture (Gyps africanus) in Kruger National Park, South Africa. We used NIMO to collect GBIF occurrence data and integrated environmental predictors to map suitable habitat (probability of occurrence). The distribution modelling workflow included area calibration, collinearity check, generation of training and testing data sets, model fitting and model combination. The results showcase the discriminative ability of fitted models, especially the generalized linear model, support vector machine and random forest, that constituted the ensemble model. Spatial prediction using the ensemble model revealed that the species has a higher occurrence probability close to roads, indicating a possible increased risk of mortality for the vultures in Kruger National Park from motor vehicle collisions.

Evaluating a National Traditional Chinese Medicine Examination via Cognitive Diagnostic Approaches

The current research utilized diagnostic classification models (DCMs), an advanced psychometric theory, to evaluate the examination’s quality using psychometric methods for a more precise and comprehensive understanding of health professionals’ competence. Data was gathered from 16,310 fourth-year Traditional Chinese Medicine undergraduates who completed the Standardized Competence Test for Traditional Chinese Medicine Undergraduates (SCTTCMU) comprising 300 multiple-choice items. The study examined the fundamental assumptions, model-data fit, and cognitive diagnostic theory models’ item and test properties. The generalized deterministic input, noisy, “and” gate model applied in this research demonstrated a strong alignment with the real response data, meeting all the necessary assumptions. Cognitive diagnostic analysis indicated that all items exhibited satisfactory psychometric characteristics, and the reported scores offered insights into candidates’ proficiency in cognitive skills. It is expected that the advent of modern psychometric technology will contribute to the improvement of refined diagnostic information for health professional candidates. Furthermore, this research holds the potential to significantly enhance sustainability in healthcare practices, knowledge, economics, resource use, and community resilience.

A data quality management framework for equipment failure risk estimation: Application to the oil and gas industry

In engineering asset management, accurate failure risk estimation is essential for averting equipment breakdowns and optimizing risk-based maintenance strategies. Data quality and model fitting are two critical sources of prediction uncertainty in risk estimation. While much attention has been devoted to model fitting for risk estimation, the critical role of data quality has often been overshadowed. To bridge this research gap, this paper presents a novel data quality management framework tailored for industrial equipment failure risk estimation. The framework covers the steps from data to model. It consists of the following main phases: data development, data quality assessment, data quality requirement decision-making, data quality improvement, and risk estimation model development. The framework provides detailed guidelines that can facilitate data practitioners to build individualized data quality requirement decision-making models for failure risk estimation of their equipment. The decision-making model can measure the adequacy of existing data to build a risk estimation model that meets the specified requirements and further determines the best risk estimation model given the available data. A case study using actual data collected during oil well drilling operations from multiple oil fields demonstrates the practicality and effectiveness of the framework. In this case study, four risk estimation models are compared, including two baseline models (mean time to failure and median time to failure) and two machine-learning models (quantile regression and hidden Markov model). In addition, a decision tree-based decision model is developed to determine whether the data quality meets the requirements and the best risk estimation model in case the data quality meets the requirements.

스쿠버다이빙투어 참가자의 사전지식과 애착도, 정보탐색, 성과 및 만족의 구조적 관계

The purpose of this study was to investigate the structural relationship among prior knowledge, attachment, information search, performance, and satisfaction of scuba diving tour participants. The subjects of this study were 239 scuba diving tour participants. With the collected data model fit tested and structural equation model were performed by AMOS 22.0. The results of the analysis were summarized as follows. First, the prior knowledge impacts positively(+) influence on attachment and information search. Second, the prior knowledge impacts no significantly on performance. Third, the attachment and information search impacts positively(+) influence on performance. Fourth, the performance impacts positively(+) influence on satisfaction. These results mean that improved their attachment and information search through prior knowledge of scuba diving tour participants, and the improved their performance through of attachment and information search. It also means that improving their satisfaction through performance of scuba diving tour participants.

Development of the imagination-creativity process scale in design

In this study, the undergraduate students’ Imagination–Creativity Process Scale (ICPS) in design was developed with reference to the Imagination–Creativity Process in design (Wang & Ho, 2021), and the scale reliability and validity were tested. Two samples of undergraduate students participated in test development, one for item revision and the other for validation. Sample 1 and 2 consisted of 285 and 488 students, respectively. Five to ten items were created for each scale dimension, resulting in a total of 31 items scored using a five-point Likert scale. A multidimensional Rasch partial-credit model was employed to verify the content and structural validity, generalizability, substantive validity, interpretive validity, and external validity of the scale. 30 items were retained for the final version of ICPS in design. The person separation reliabilities were 0.84, 0.83, 0.86, and 0.83, respectively for the four dimensions of the scale: (1) ideation and analysis, (2) generation and revision, (3) implementation and evaluation, and (4) communication and optimization. The results indicated that the ICPS had good model–data fit and was suitable for measuring undergraduate students’ Imagination–Creativity Process in design. Recommendations for future studies were proposed regarding the development and application of the ICPS in design.

A study of the validity of Spanish version of the Self-Estimated Functional Inability because of pain (SEFIP) in pre-professional dancers

ABSTRACT The aim of this research was to analyse the psychometric properties of the Spanish version of the Self-Estimated Functional Inability due to Pain Questionnaire (SEFIP) in Dance Conservatory students. For this purpose, the Self-Estimated Functional Inability Because Of Pain (SEFIP) instrument was used, translated into Spanish, validated by experts and administered to a total of 451 students from the Professional and Superior Dance Conservatories of Andalusia (418 females = 92.7%; 33 males = 7.3%) aged 10 and 48 years (M = 18.66; SD = 5.296). The structure of the instrument was then analysed using confirmatory procedures. The Spanish version of the instrument was named Cuestionario de Incapacidad Funcional Autoestimada debido al Dolor. The results show that the model that presents correct fits in the confirmatory analysis is the one-factor model. The model fit data were very good: χ2/gl = 3.37, p = .000; CFI = .94; NFI = .95; GFI = .91; RMSEA = .07 (90% CI = .603; .821). The results support the validity and reliability of this instrument in this population to be used to prevent injuries in dancers.