Model-data Fit Research Articles

Use of social media data, online reviews and wikipedia page views to measure visitation patterns of outdoor attractions

This study explores the suitability of activity counts extracted from social media platforms (Twitter, Flickr), review portals (TripAdvisor, Google Maps) and Wikipedia article views to model official visitor counts at selected outdoor attractions in Florida (U.S.) and Carinthia (Austria). It applies correlation analysis, multiple regression, and time series analysis to identify which of these user-generated content (UGC) sources and their combinations best match official monthly visitor count patterns for an analysis period of three years (2019–2021). With travel activities being severely hampered during 2020 due to the COVID-19 pandemic, the analysis also aims to analyze to which extent reduced visitor counts are reflected in the respective UGC sources. Results show that the number of Google Maps reviews combined with Wikipedia pageviews best explain the variability of monthly official visitor counts in Ordinary Least Squares (OLS) regression in both study areas. While the comparison analysis was conducted for monthly counts, data from some UGC sources can reflect shorter term activity fluctuations. Time series analysis detected a seasonality of 12 months for Wikipedia pageviews, Google Maps reviews, and official visitor counts for Austria due a clearly distinct summer season. As opposed to this, for Florida, due to a climate that facilitates all-year round park visitations, periodograms yielded different seasonality frequencies for UGC sources and official visitor counts. A short-term drop in visitor counts due to COVID-19 was evident in Florida both in UGC sources and official visitor counts in spring 2020, whereas for Austria attractions reduced activity is only somewhat reflected in UGC sources but not in actual visitor counts due to attractions still being closed in April. Management implicationsThis research explores multiple UGC sources for modeling official visitor counts at outdoor attractions. A combination of several sources has the advantage that it can help mitigate known limitations of UGC from individual sources, such as sparsity of geodata, data retrieval restrictions, sociodemographic bias, and varying popularity across regions. This research also revealed a better model fit of combined UGC data with official visitor counts than using any of the UGC sources alone. Data-rich UGC sources offer daily or weekly activity counts that provide a more refined temporal resolution than typical official visitor counts which are often limited to monthly data aggregations.

Calibrating Internal Communication Satisfaction within Organizations as an Auditing Index

Prior research regarding communication audits within organizations depicts a general understanding of various aspects of the communication process that augment productivity. The present study aimed at validating a newly developed scale that measures internal communication maturity within organizations through an employee-centric approach rather than a management-centric one. The present study employs a cross-sectional survey research design. A total of 2071 employees (94.4% male; 5.6% female) from the logistic industry across the Kingdom of Saudi Arabia were approached through convenience sampling. Based on the literature review and results derived from interviews, 16 items were generated with a 5-point Likert response format. Results indicated the measure is reliable and valid. Reliability analysis showed good alpha reliability coefficients (>0.75) between total internal communication satisfaction and its subscales (awareness, appreciation, relationship, engagement and maturity). The correlation matrix from EFA revealed the presence of coefficients of 0.3 and above, indicating the data is fit for factor analysis. Confirmatory factor analysis showed an acceptable model-data fit of the five-factor model. Data were collected only from the logistics industry; however, data can be gathered from other industries as well. Furthermore, cross-sectional data are used in the current study; therefore, they cannot be used to infer a causal relationship. The present study will be broadly helpful in diagnosing specific communication areas and formulating recommendations for improvement. The instrument will be worthwhile in communication audits of organizations.

A temperature-controlled cooling system for accurate quantitative post-mortem MRI.

To develop a temperature-controlled cooling system to facilitate accurate quantitative post-mortem MRI and enable scanning of unfixed tissue. A water cooling system was built and integrated with a 7T scanner to minimize temperature drift during MRI scans. The system was optimized for operational convenience and rapid deployment to ensure efficient workflow, which is critical for scanning unfixed post-mortem samples. The performance of the system was evaluated using a 7-h diffusion MRI protocol at 7T with a porcine tissue sample. Quantitative T1 , T2 , and ADC maps were interspersed with the diffusion scans at seven different time points to investigate the temperature dependence of MRI tissue parameters. The impact of temperature changes on biophysical model fitting of diffusion MRI data was investigated using simulation. Tissue T1 , T2 , and ADC values remained stable throughout the diffusion MRI scan using the developed cooling system, but varied substantially using a conventional scan setup without temperature control. The cooling system enabled accurate estimation of biophysical model parameters by stabilizing the tissue temperature throughout the diffusion scan, while the conventional setup showed evidence of significantly biased estimation. A temperature-controlled cooling system was developed to tackle the challenge of heating in post-mortem imaging, which shows potential to improve the accuracy and reliability of quantitative post-mortem imaging and enables long scans of unfixed tissue.

Glass formulation and composition optimization with property models: A review

AbstractGlass is a versatile material with a remarkable history and many practical applications. It plays a critical role in our everyday lives, the advancement of science, and the development of many technologies. The Edisonian type trial‐and‐error method was commonly used for conventional design of glass compositions, which was time‐consuming and costly. With the urgent need to develop new glass compositions for technology applications rapidly, it has become necessary to develop precise property models with predictive powers using large databases and efficient formulation approaches. This paper reviews the design of glass compositions using these analytical and numerical models of composition–structure–property relations of glasses, some based on large databases and machine learning approaches. Aspects of data collection, model fitting, feature extraction, model evaluation, and uncertainty quantification will be covered. Furthermore, advances in the glass optimization framework and available tools are summarized with examples. The outlook and perspective for further glass property model development and formulation approaches are discussed.

EARLY TRAUMA INVENTORY-SELF REPORT SHORT FORM: TURKISH ADAPTATION AND PSYCHOMETRIC ANALYSIS FOR UNIVERSITY STUDENTS

The aim of this study is to adapt the Early Trauma Inventory-Self Report Short Form to Turkish and test its validity and reliability for university students. The inventory was translated, and the Turkish form was finalised. The first study group, whose data were used to conduct the confirmatory factor analysis regarding the scale, consisted of 350 undergraduate, and graduate students from 14 different universities in the fall semester of the 2021-2022 academic year; 270 of whom were female and 80 of whom were male. In data analysis, descriptive statistics were calculated; confirmatory factor analysis and reliability analyses were performed. Since Early Trauma Inventory Short Form is an inventory consisting of sub-scales, a separate confirmatory factor analysis was performed for each sub-scale of Turkish version. Confirmatory factor analysis showed that the model data fit of the general trauma subscale was “good,” and it was “perfect” for the physical, emotional, and sexual abuse subscales. Cronbach alpha internal consistency coefficients and test-re-test correlations were found to be satisfactory. Results show that the Early Trauma Inventory-Self Report Short Form is a valid and reliable tool to use in determining early childhood trauma.

An Examination of the Structural Validity of Instruments Assessing PE Teachers’ Beliefs, Intentions, and Self-Efficacy towards Teaching Physically Active Classes

Schools and, in particular, health and physical education (HPE) classes, has the potential to engage children and adolescents in health-enhancing physical activity. HPE teachers may enable or constrain this behaviour through appropriate classroom strategies and pedagogies that enhance not only student learning but also their engagement in physical activity. Such practices are a result of a teacher’s curricula beliefs, self-efficacy, and intention to teach physical activity as part of their HPE classes. The purpose of this study is to investigate the structural validity of six instruments designed to assess teachers’ beliefs, self-efficacy, and intentions regarding physical activity in physical education using the Rasch Measurement Model (RMM). Data suggest that three out of six instruments demonstrated multidimensional characteristics (curriculum beliefs, self-efficacy, and subjective norm). Model fit data were in the ranges of 0.5 to 1.61 for infit and −2.58 to 3.20 for outfit data. Differential item functioning was only present on one item in the curriculum beliefs instrument. Person reliability was >0.55 and item reliability was >0.73. Qualitative interpretation of Wright maps demonstrated a very good spread of items. Overall, each instrument demonstrated appropriate structural validity when weighed up against all of the components of the RMM.

Psychometric properties of the WHOQOL-BREF in a population of older adults in prison: measuring quality of life in a carceral setting

PurposeValid and reliable measurement is critical to the assessment and evaluation of health interventions. However, few scholars have examined the psychometric properties of “gold standard” measures in carceral settings, and no research has explored the reliability, validity and factor structure of the 26-item World Health Organization Quality of Life (WHOQOL) BREF among older adults incarcerated in prison, a large and growing population marked by substantial chronic and life-limiting illness. This study aims to examine the reliability, validity and factor structure of the WHOQOL-BREF.Design/methodology/approachSecondary data from a large-scale study with older adults (age 45+) incarcerated in a Midwestern state were used (N = 499). Floor and ceiling effects were examined, as was the reliability of the measure (Cronbach's alpha). The structural validity of a four-factor and second-order four-factor model of the WHOQOL-BREF was examined using confirmatory factor analysis. Model data fit was examined using chi-square, standardized root mean square residual, comparative fit indices, Akaike information criterion and the Bayesian Information Criterion. Pearson product-moment correlation coefficients were also used to assess validity.FindingsResults indicate adequate construct validity and reliability for the WHOQOL-BREF using the current sample. Model-data fit indexes also reveal adequate structure of the measure relative to other older adult samples. Non-random data and item exclusion are noteworthy limits, and future researchers are encouraged to co-conceptualize and operationalize life quality with older adults who are incarcerated.Originality/valueTo the best of the authors’ knowledge, this is the first study to explore the psychometric properties of the WHOQOL-BREF among older adults incarcerated in prison.

PyWitness 1.0: A python eyewitness identification analysis toolkit

pyWitness is a python toolkit for recognition memory experiments, with a focus on eyewitness identification (ID) data analysis and model fitting. The current practice is for researchers to use different statistical packages to analyze a single dataset. pyWitness streamlines the process. In addition to conducting key data analyses (e.g., receiver operating characteristic analysis, confidence accuracy characteristic analysis), statistical comparisons, signal-detection-based model fits, simulated data generation, and power analyses are also possible. We describe the package implementation and provide detailed instructions and tutorials with datasets so that users can follow. There is also an online manual that is regularly updated. We developed pyWitness to be user-friendly, reduce human interaction with pre-processing and processing of data and model fits, and produce publication-ready plots. All pyWitness features align with open science practices, such that the algorithms, fits, and methods are reproducible and documented. While pyWitness is a python toolkit, it can also be used from R for users more accustomed to this environment.

Improving modelled albedo over the Greenland ice sheet through parameter optimisation and MODIS snow albedo retrievals

Abstract. Greenland ice sheet mass loss continues to accelerate as global temperatures increase. The surface albedo of the ice sheet determines the amount of absorbed solar energy, which is a key factor in driving surface snow and ice melting. Satellite-retrieved snow albedo allows us to compare and optimise modelled albedo over the entirety of the ice sheet. We optimise the parameters of the albedo scheme in the ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) land surface model for 3 random years taken over the 2000–2017 period and validate over the remaining years. In particular, we want to improve the albedo at the edges of the ice sheet, since they correspond to ablation areas and show the greatest variations in runoff and surface mass balance. By giving a larger weight to points at the ice sheet's edge, we improve the model–data fit by reducing the root-mean-square deviation by over 25 % for the whole ice sheet for the summer months. This improvement is consistent for all years, even those not used in the calibration step. We also show the optimisation successfully improves the model–data fit at 87.5 % of in situ sites from the PROMICE (Programme for Monitoring of the Greenland Ice Sheet) network. We conclude by showing which additional model outputs are impacted by changes to the albedo parameters, encouraging future work using multiple data streams when optimising these parameters.

Lollipops Help Align Visual and Statistical Fit Estimates in Scatterplots with Nonlinear Models.

Scatterplots overlayed with a nonlinear model enable visual estimation of model-data fit. Although statistical fit is calculated using vertical distances, viewers subjective fit is often based on shortest distances. Our results suggest that adding vertical lines (lollipops) supports more accurate fit estimation in the steep area of model curves (https://osf.io/fybx5/).

Rasch Calibration of the International Knee Documentation Committee Subjective Knee Form.

Among numerous knee-related patient-reported outcome measures, the International Knee Documentation Committee Subjective Knee Form (IKDC-SKF) has been used across a wide variety of knee pathologies. However, traditional validation procedures (classical test theory) and existing studies (estimating item parameters) have limitations in establishing the measurement properties of the IKDC-SKF. Rasch analysis reveals a strong validation approach to improve IKDC-SKF clinical interpretation with larger samples. To assess psychometric properties, including differential item functioning, of the IKDC-SKF as a patient-reported measure of knee function. Cross-sectional study. Secondary data. Data were extracted from the cloud-based orthopedic and sports medicine global registry Surgical Outcome System (Arthrex). A total of 1725 individuals who underwent an arthroscopic knee procedure and completed all items on the IKDC-SKF. Rasch analysis including model-data fit, rating scale's function, item-person map (distribution of item difficulty and person ability), and differential item functioning (sex and age groups) was used to evaluate the psychometric properties of the IKDC-SKF. Ten misfit items were found and removed. The 5-point Likert scale of the 9-item IKDC-SKF worked well. Item difficulty ranged from 0.58 to 0.81 logits, and person's knee function had ranged from -5.56 to 4.86 logits, with a wide distribution. The IKDC-SKF was found to function similarly for sex (male vs female) and age. Rasch analysis identified a unidimensional structure retaining 9 of the original IKDC-SKF items; however, a more comprehensive inventory is necessary to assess a wider range of knee function and improve measurement validity.

A simulation-based scaled test statistic for assessing model-data fit in least-squares unrestricted factor-analysis solutions

A shortcoming of least-squares unrestricted factor analysis (UFA) procedures, which are widely used in psychometric applications is that a test statistic for assessing model-data fit cannot be easily derived from the minimum fit function value. This paper proposes a chi-square type goodness-of-fit test statistic intended for the principal-axis, MINRES, and minimum-rank UFA procedures. The statistic is empirically obtained via intensive simulation based on a two-stage approach. First, a distribution of minimum fit function values is obtained from a scenario in which the null hypothesis of perfect model-data fit holds. Second, the obtained statistic is non-linearly transformed so that it has its first four moments equal to those of the theoretical reference chi-square distribution with the appropriate degrees of freedom. Extensions of the basic statistic are next proposed that include comparative and relative indexes based on it. Tests of close-fit and power assessment derived from the basic statistic are also proposed.

Exploring Psychometric Properties of the Turkish Version of the Academic Nurse Self-Efficacy Scale

Nursing students’ academic self-efficacy can be considered a significant factor in reducing their academic failure, which necessitates a valid measurement tool to reveal academic self-efficacy among undergraduate nursing students. In this sense, we carried out this study to adapt the Academic Nurse Self-Efficacy Scale (ANSES) into the Turkish context. The sample of this methodological study consisted of 235 undergraduate nursing students enrolled in a state university in the Marmara Region. We collected the data using a descriptive information form and the Turkish version of the ANSES. Following the translation-back-translation of the scale, we submitted the items to the views of 20 experts and calculated content validity ratios to be 0.80 and above for each item. After analyzing the data descriptively, we attempted to test the construct validity of the scale using confirmatory factor analysis (CFA) and sought test-retest reliability with Peason’s correlation analysis and internal consistency by calculating Cronbach’s alpha coefficient. According to the findings, the measurement model yielded an acceptable model-data fit. In addition, we found our measurement with the Turkish version of the ANSES showed high internal consistency (0.82). While item-total correlations varied between 0.32 and 0.74, test-retest reliability was found to be 0.81. Overall, we can propose that the Turkish version of the ANSES can validly and reliably be utilized to measure academic self-efficacy among undergraduate nursing students. Thus, we can recommend using the scale, brought in the Turkish literature on nursing, to reveal the self-efficacy of undergraduate nursing students and identify to what extent they have accomplished their learning goals.

FlexCDMs: A Web-based Platform for Cognitive Diagnostic Data Analysis

Cognitive diagnosis is an important component of modern measurement theory and has received widespread attention from researchers in the fields of education and psychological measurement. Existing cognitive diagnosis analysis tools rely on professional software packages (such as R packages), which creates significant challenges for users, especially those who are not familiar with computer programming. To remove this technical barrier, our team has developed a web-based, user-friendly platform, named flexCDMs, for cognitive diagnosis data analysis. This article describes the features of the platform, the functional modules, the implemented cognitive diagnosis models (CDMs) and algorithms, and illustrates the operations of the platform. This platform can be used to analyze data based on various cognitive diagnosis models, carry out Q-matrix theory, model-data fit tests, parameter estimation, quality analysis of cognitive diagnostic tests, differential item functioning (DIF) detection, and Q-matrix modification. It produces various charts and graphs to report results. It is a powerful, yet easy to use cognitive diagnosis data analysis tool. The website for the flexCDMs platform is: http://111.230.233.68:1001/?Id=false&Block

Thermodynamic and kinetic analyses of Janus membrane scaling in membrane distillation for zero liquid discharge engineering

Scaling phenomena are the principal obstacle to the factual application of membrane distillation (MD). In this study, we fabricated a Janus membrane composed of a commercial polytetrafluoroethylene substrate, a superhydrophobic surface layer, and a hydrophilic reverse side coated by MXene nanosheets via a simple spraying method. The virgin and modified membranes were carried out and compared in zero liquid discharge (ZLD) MD experiments. MD flux analysis showed that all the membranes experienced severe flux decline during the ZLD process. Saturation index, Gibbs free energy and nucleation energy barrier were calculated to explain the observed difference in scaling behavior. Screenage assessment of the homogeneous nucleation and heterogeneous nucleation showed that the severe scaling developed from some small clusters of the new phase, wherein the thermodynamic energy barrier played a critical role in determining the anti-scaling behavior for these unsmooth surfaces. Model analysis and data fitting were used to conclude the kinetic behavior of the virgin and modified membranes in the ZLD engineering. Finally, we evaluated the reversible and irreversible scaling after twice washing. Therefore, this study may provide instructive insights to design novel anti-fouling membranes and promote practical applications such as industrial brine treatment and ZLD engineering.

Matematik Başarısında Grup Farklılıklarının İncelenmesi: Açıklayıcı Madde Tepki Modeli Uygulaması

Students take many different exams throughout their educational lives. In these exams, various individual and item characteristics can affect the responses of individuals to the items. In this study, it was aimed to examine the effects of person and item predictors on the mathematics common exam results of 365 9th grade students with explanatory item response models. Gender and school type as person variables and cognitive domain, content domain and booklet type as item variables were added to the models due to their widespread inclusion in the literature. When the predicted item parameters were examined, it was seen that the smallest parameter values were obtained for all items with the Rasch model. When the model data fit values of four different models were examined, it was concluded that the latent regression and latent regression linear logistic test models showed better fit than the Rasch model. By adding person and item predictors to the model, the parameters obtained for each variable group were compared, and differences were observed between the groups for school type, cognitive domain, and content domain variables. It was concluded that the item parameters did not differ for the variables of gender and booklet type. It is thought that it would be beneficial to use these models more widely in studies to be conducted in the field of education and psychology since they provide more detailed information about the reasons for the differences in the estimated parameters.

Dinlediğini Anlama Testinin Türk Kültürüne Uyarlanması

Introduction: Listening, the first language skill developed by many children, serves as the basis of subsequent language skills. In addition, listening comprehension skills affect reading success in many different groups such as typically developing readers, children with learning disabilities and children with specific language disorders. The purpose of this research is to adapt the listening comprehension measurement tool to Turkish culture. Method: The purpose of this study is to examine the psychometric properties of the measurement tool within the framework of item response theory (IRT) through two-parameter logistic model (2PLM). Among model assumptions, confirmatory factor analysis (CFA) for unidimensionality Q3 statistics and item characteristic curv for the monotonic increase assumption showing the response probabilities conditional to the ability continuum were examined, respectively. The results for the assumptions were satisfactory. Then, general model-data fit, item-based model-data fit, discrimination parameters, difficulty parameters and information functions were examined with factor loadings. Findings: The results showed that item parameters were in the expected ranges in practical applications, and test information function peaked at lower ability level similar to the original measurement tool. Examining factor loadings, it was shown that they were higher than generally acceptable limit except for 23rd item. When contribution of this item to the model was tested statistically, it was concluded that it would be appropriate to include it in the measurement tool to keep the other items of associated with story block. Moreover, marginal reliability coefficient shows that the measurement results were highly reliable. Discussion: It was concluded that sufficient evidence of validity and reliability was obtained for one-dimensional structure of 36-item form of the measurement tool.

Diagnosing Chinese EFL learners’ writing ability using polytomous cognitive diagnostic models

Cognitive diagnostic assessment (CDA) intends to identify learners’ strengths and weaknesses in latent cognitive attributes to provide personalized remedial instructions. Previous CDA studies on English as a Foreign Language (EFL)/English as a Second Language (ESL) writing have adopted dichotomous cognitive diagnostic models (CDMs) to analyze data from checklists using simple yes/no judgments. Compared to descriptors with multiple levels, descriptors with only yes/no judgments were considered too absolute, potentially resulting in misjudgment of learners’ writing ability. However, few studies have used polytomous CDMs to analyze graded response data from rating scales to diagnose writing ability. This study applied polytomous CDMs to diagnose 1166 EFL learners’ writing performance scored with a three-level rating scale. The sG-DINA model was selected after comparing model-data fit statistics of multiple polytomous CDMs. The results of classification accuracy indices and item discrimination indices further demonstrated that sG-DINA had good performance on identifying learners’ strengths and weaknesses. The generated diagnostic information at group and individual levels was further synthesized into a personalized diagnostic report, although its usefulness still requires further investigation. The findings provided evidence for the feasibility of applying polytomous CDM in EFL writing assessment.

The Impact of Live Commerce and Short Video on College Students Actual Purchase

The emergence of new media has led to live commerce becoming a novel, widely adopted trend, and live commerce has also become a new business model. This study has put a lot of work into creating an integrated model that combines the theories of planned behavior and media exposure to explain the connection between watching short videos and in-person shopping. We looked at the connections between attitude, subjective norm, perceived behavioral control, frequency, duration, and attention to live commerce viewing, as well as real purchases. To check the accuracy of the fictitious model, 402 college students in China participated in a questionnaire study that yielded empirical data. The structural equation model is shown to have a strong model fit for empirical data, while the measurement model demonstrates that the theoretical constructs have appropriate reliability and validity. The results of the study demonstrate that customers attitudes toward live commerce and their perceived behavioral control over it greatly influenced their actual purchases. But the effect of subjective norms on actual purchases has not been verified. This research addressed the knowledge gap in the application of TPB to explain offline and online purchasing behavior. Also, this study has its limitation; since the research is primarily focused on China, it is difficult to extrapolate the findings to other countries.

A Generalized Multiple Criteria Data-Fitting Model With Sparsity and Entropy With Application to Growth Forecasting

In this article, we present an extended data-fitting model which involves different and conflicting criteria, and we propose an algorithm based on a scalarization technique to solve it. Our model integrates in a unique framework three different criteria, namely, a data-fitting term, and the entropy and the sparsity of the set of unknown parameters. This model can be analyzed by means of multiple criteria decision-making techniques. We then validate the proposed modified algorithm using two computational experiments: We analyze the problem of handwritten digit recognition using a logistic regression model and a deep neural network model, respectively. In the final part of the article, we employ this methodology to forecasting instead. Given the importance of forecasting techniques to predict the future, which in turn can lead to positive impacts on firm performance, we propose two numerical experiments focusing on the forecast of the US GDP. In the first one, we proceed by means of a modified iterated function system with grayscale maps-type fractal operator, and, in the second one, we implement a modified neural network-based model.