Tests Of Model Fit Research Articles

Binary Logistic Regression Modeling on Household Poverty Status in Bengkulu Province

Binary logistic regression is a statistical method used to analyze the relationship between one or more predictor variables and a binary or dichotomous response variable. Poverty is an issue in every province in Indonesia. One of the provinces with a relatively high poverty rate is Bengkulu Province, ranking seventh in Indonesia with a poverty rate of 14.62%. The Central Bureau of Statistics of Bengkulu Province (2023) explains that efforts to reduce poverty must involve all levels of society. Various government programs and policies in various fields such as health, social, and other areas are continuously being implemented to reduce the number of households classified as poor. Identifying the characteristics of households in Bengkulu Province by poverty status is important to study, as it serves as a reference to ensure that government programs are implemented according to the target. One method that can be used to identify household characteristics is binary logistic regression. This study aims to model the poverty status of households in Bengkulu Province using binary logistic regression and to identify the factors that influence it. The data used are social and economic household data from March 2022. The response variable used is household poverty status (poor and not poor), while the predictor variables include the ownership of toilet facilities, the source of lighting, floor area, family size, and per capita calorie consumption. Modeling is done using binary logistic regression with simultaneous and partial parameter significance tests, as well as model fit tests. The analysis results show that the factors significantly influencing household poverty status in Bengkulu Province are the ownership of toilet facilities, the source of household lighting, floor area, family size, and per capita calorie consumption. The formed binary logistic regression model has a classification accuracy of 89.98% with a sensitivity of 18.34% and a specificity of 98.61%.

Factor Structure and Validity of Composite Scores Resulting From a Computerized Cognitive Test Battery in Healthy Adults and Patients With Primary Brain Tumors.

Computerized neuropsychological test batteries (CNTs), such as Central Nervous System Vital Signs (CNS VS), are increasingly used for measuring cognitive functioning, but empirical evidence of how they measure cognition is scarce. We investigated the factor structure of CNS VS using exploratory factor analyses four samples: healthy adults (n = 169), patients with meningioma (392), low-grade glioma (99), and high-grade glioma (247). We tested model fit and investigated measurement invariance. Differences in factor interpretation existed between healthy participants and patients. Factor structures among patient groups were approximately the same but differed in non-zero loadings. Overall, factor structures largely did not support the "clinical domains" provided by CNS VS for clinical interpretation. Confirmatory models did not have a good fit, and measurement invariance could not be established. Our results indicate that (weighted) sum scores of CNS VS results may lack validity. We recommend researchers and clinicians to use scores on individual test measures.

BIM-181: Revising the Public Domain “Basic Interest Markers” Using Item Response Theory

The present study investigated the psychometric properties of the Basic Interest Markers (BIM), a public-domain broadband measure of basic interests. Unlike broader general interest frameworks such as Holland’s RIASEC types, basic interests represent more specific content domains and provide a granular view of vocational preferences and inclinations. We used Item Response Theory (IRT) to perform item analysis to identify the most informative items in each BIM scale and, by extension, reduce scale lengths. In addition, we used confirmatory factor analysis to test model fit for the BIM scales and previously suggested higher-order clusters. Data were drawn from a total of 2,009 participants, comprising two samples of undergraduate college students ( N = 910 and N = 120) and samples of 164 university alumni and 815 adults recruited through Amazon Mechanical Turk. Our analysis demonstrated that latent trait estimates obtained from the BIM-343 were similar to those of our proposed modified BIM-181, with model fit favoring the latter. Overall, we provide support for the use of a modified version of the BIM inventory that more accurately captures the intended construct while simultaneously reducing test length by nearly 50%.

UroARC: A novel surgical risk calculator for older adults undergoing pelvic organ prolapse and stress urinary incontinence surgery.

Surgeries for pelvic organ prolapse (POP) and stress urinary incontinence (SUI) are commonly performed in older adults, many of whom are also frail. A surgical risk calculator for older adults undergoing POP/SUI surgeries that incorporates frailty, a factor known to increase the risk of surgical complications, would be helpful for preoperative counseling but currently does not exist. Medicare Carrier, Outpatient, and MedPAR files were examined for beneficiaries undergoing POP and SUI surgery between 2014 and 2016. A total of 15 POP/SUI categories were examined. The Claims-Based Frailty Index (CFI), a validated measure of frailty in Medicare data, and Charlson Comorbidity Index were deconstructed into their individual variables, and individual variables were entered into stepwise logistic regression models to determine which variables were most highly predictive of 30-day complications and 1-year mortality. To verify the prognostic accuracy for each model for surgical complications of interest, calibration curves and tests of model fit, including C-statistic, Brier scores, and Spiegelhalter p values, were determined. In total, 108 479 beneficiaries were included. Among these, 4.7% had CFI scores consistent with mild to severe frailty (CFI≥0.25). A total of 13 prognostic variable categories were found to be most highly predictive of postoperative complications. Calibration curves for each outcome of interest showed models were well-fit. Most models demonstrated high c-statistic values (≥0.7) and high Spiegelhalter p values (≥0.9), indicating good model calibration and excellent discrimination, and low Brier scores (<0.02), indicating high model accuracy. Urologic surgery for older Adults Risk Calculator serves as a novel surgical risk calculator that is readily accessible to both patients and clinicians that specifically factors in components of frailty. Furthermore, this calculator accounts for the heterogeneity of an aging population and can assist in individualized surgical decision-making for these common procedures.

UroARC: A Novel Surgical Risk Calculator for Older Adults Undergoing Suprapubic Tube Placement

ObjectivesTo develop a surgical risk calculator for older adults undergoing suprapubic tube (SPT) placement that specifically factors in frailty, a key predictor of surgical risk in this vulnerable and heterogenous population. MethodsMedicare MedPAR, Outpatient, and Carrier files for beneficiaries undergoing SPT placement between 2014-2016 were examined. The Claims-Based Frailty Index (CFI), a validated measure of frailty, was used to calculate baseline frailty for each beneficiary. Stepwise regression models were used for each variable within the CFI and Charlson Comorbidity Index to determine the variables most highly predictive of postoperative complications. The most highly predictive variables were then combined into parsimonious categories. To ensure the prognostic accuracy for each outcome, calibration curves and tests of model fit, including C-statistics, Brier scores, and Spiegelhalter p-values were calculated. ResultsA total of 26,999 beneficiaries were included. Among these, 39.1% were pre-frail, 36.8% were mildly frail, and 12.3% were moderately-to-severely frail. Thirteen prognostic variable categories were deemed highly predictive of postoperative complications of interest. All models demonstrated low Brier scores, indicating high model accuracy, and high C-statistic and Spiegelhalter p-values, consistent with excellent model discrimination and calibration, respectively. Excellent model fit was seen on calibration curves for each outcome. ConclusionsUroARC is a novel surgical tool for older adults undergoing SPT placement that specifically factors in frailty. This risk calculator has high accuracy, calibration, and discrimination, and serves as a valuable resource to patients and clinicians for those undergoing consideration for SPT placement.

Development of Algebra Test Using the Item Response Theory Approach for Junior High School Students

This research aims to develop valid and reliable measuring tool for students' algebraic abilities that can be used in schools and the general public. The research follows a structured test development design, including stages such as preparing test specifications and items, field testing, revising items, and test development. The questions are aligned with the 2013 curriculum syllabus, ensuring relevance to educational standards. The test was given to 662 junior high school students in Kendari City, Indonesia, and their responses were analyzed using the item response theory (IRT) model with two logistic parameters: item difficulty level and item discriminatory power. The BILOG MG program was employed to estimate item and ability parameters. Before conducting item analysis with IRT, essential assumption tests were conducted, including unidimensional and model fit tests. The results of the development process, based on item analysis using BILOG MG, yielded 15 items covering various aspects of algebraic abilities. These items were derived from indicators such as recognizing algebraic forms, identifying elements within these forms, performing addition, subtraction, multiplication, and division operations on algebraic forms, presenting and solving real-world problems in algebraic contexts, and addressing contextual problems involving algebraic operations. The items demonstrated good fit with the model and exhibited an appropriate level of item difficulty and discriminatory power, making them suitable for use as a reliable assessment tool. Consequently, these developed tests are deemed effective for measuring students' foundational algebraic abilities.

Digitizing success: Leveraging digital human resource practices for transformative productivity in Chinese SMEs

Background& Purpose: In the rapidly evolving digital landscape, digital human resource (HR) practices in small and medium-sized enterprises (SMEs) have become a vital success factor. This study adopts the resource-based view of the firm to explore the complicated relationships among digital HR practices, digital transformation, innovative work behavior, disruptive innovation, and work productivity in the context of Chinese SMEs. MethodsThis study provides fundamental insights by drawing data from Jiangsu province, China. Data from 339 managerial staff members were collected and analyzed using SmartPLS (4.0) with a partial least squares structural equation model (PLS-SEM). Several measures, model fit, and hypothesis tests were conducted. FindingsDigital HR practices significantly affect digital transformation, innovative work behavior, and productivity. Furthermore, digital transformation is a powerful driver that positively influences innovative work behavior and productivity in SMEs. Interestingly, innovative work behavior positively influenced work productivity, exemplifying the vital role of inspired thinking and problem-solving abilities. This study also investigates the moderating role of disruptive innovation, highlighting its influence on the association between digital HR practices and digital transformation. However, disruptive innovation does not significantly moderate the association between digital transformation and innovative work behavior, suggesting fascinating nuances for SMEs. Conclusion/valueThis research extends our understanding of the interaction between digitalization, HR, and innovation, and proposes practical implications for SMEs aiming to harness the transformative potential of digitization. Several theoretical and practical implications are developed for future studies on related SMEs.

Exploratory and confirmatory factor analysis for validating a distributed leadership inventory for secondary schools in Ethiopia

This study aimed to validate the Ethiopian version of the distributed leadership inventory (DLI-Eth) and evaluate its psychometric properties. The survey was administered to 714 randomly selected teachers. The data were subjected to data cleaning and preliminary analysis for normality and multivariate assumptions using SPSS version 23 with an extension of AMOS. Exploratory and confirmatory factor analyses were performed to identify the cluster of items and test model fit. The principal component analysis extraction method with varimax rotation revealed a significant four-factor solution. The four-factor structure accounted for 70.04% of the cumulative variance. The results showed that the proposed model met the acceptable model fit. The study concluded that the DLI-Eth is a good tool for measuring distributed leadership functions in Ethiopia. Moreover, this paper makes a practical contribution to future leadership investigators by providing a validated inventory to broaden our understanding of distributed leadership in a non-Western context.

Human Talent Management in the Development of Work Skills in the Framework of The SDG in a Hospital in Peru

Objective: To determine the influence of human talent management on the development of labor competencies of the administrative staff of a hospital in Peru. This is associated with the Sustainable Development Goal (SDG) “Decent work and economic growth”. Theoretical framework: Globalization and digitalization generate constant changes that force institutions to orient human talent management towards the development and specialization of workers' skills, through training and feedback. Method: The basic study had a quantitative approach and a non-experimental cross-sectional design. The sample consisted of 86 employees, the technique used was the survey and a questionnaire was used for each variable. The ordinal logistic regression test was used for the inferential analysis, considering a significance level &lt; 0.05. Results and Discussion: The results of the model fit test showed a p-value of 0.000 &lt; 0.05. Likewise, the Nagelkerke coefficient yielded a value of 0.239, determining that there is a 23.9% influence of human talent management on the development of labor competencies. Implications of the research: Adequate human talent management will allow the development of the competencies of the hospital's administrative employees. Originality/value: Human talent management in the development of labor skills in Peruvian hospitals has been little explored, which makes this study an original contribution, given that most research has focused on other contexts.

Psychometric validation of the FACE-Q Aesthetics Satisfaction with Temples scale in individuals seeking minimally invasive treatment to improve temple hollowing.

The FACE-Q Aesthetics is used extensively to measure patient reported outcomes for minimally invasive and surgical facial aesthetic treatments. We recently developed a new FACE-Q scale to assess satisfaction with the appearance of the temples. The aim of this study was to field test the FACE-Q Satisfaction with Temples scale to examine its psychometric properties. The FACE-Q Satisfaction with Temples scale was administered to 171 adults (22 years or older) seeking minimally invasive treatment to improve temple hollowing as part of a clinical trial. The severity of temple hollowing was established through the clinician-reported Allergan Temple Hollowing scale (clinician-rated). The psychometric properties of the FACE-Q Satisfaction with Temples scale were established by testing Rasch Measurement Theory (RMT) assumptions and model fit; unidimensionality by principal component analysis; and construct validity by hypothesis testing. The study sample consisted of 171 adults (mean age 54.7 ± 9.9, range, 25-82 years). RMT analysis provided evidence for the scientific soundness of a 12-item Satisfaction with Temples scale. The data fit the Rasch model (χ2 = 20.47, df = 24, p = 0.67), all items had ordered thresholds, and good item fit. Scale reliability was high, with Person Separation Index and Cronbach alpha values with and without extremes ≥0.93. Principal component analysis revealed a single component with high factor coefficients. Construct validity was established as scores for the Satisfaction with Temples and Face Overall scales were correlated (r = 0.623, p < 0.001). The FACE-Q Satisfaction with Temples scale is a reliable and valid measure that can be used in clinical practice and research to measure outcomes following treatment for temple hollowing.

Predicting Postoperative Satisfaction with Breasts: How Important is the Preoperative BREAST-Q Score?

The role that preoperative Satisfaction with Breast plays in a patient's postoperative course after postmastectomy breast reconstruction (PMBR) is not understood. The aim of this study is to understand the impact of the preoperative score on postoperative outcome as an independent variable. We examined patients who underwent PMBR between 2017 and 2021 and who completed the BREAST-Q Satisfaction with Breasts at 1year postoperatively. Two multiple linear regression models (Model 1 with the preoperative Satisfaction with Breasts score and Model 2 without the preoperative score), likelihood ratio tests, simple t-statistics, and sample patient dataset to predict the 1year score were performed. Multiple imputation was used to account for missing preoperative scores. Overall, 2324 patients were included. Model 1 showed that the preoperative score is significantly associated with the postoperative score (β = 0.09, 95% confidence interval 0.04-0.14; p < 0.001). Comparing Model 1 and Model 2 demonstrated that including preoperative Satisfaction with Breasts in a regression significantly improves model fit (test statistic = 10.04; p = 0.0021). Using the absolute value of the t-statistics as a measure of variable importance in linear regression, the importance of the preoperative score was quantified as 3.39-more important than neoadjuvant radiation, mastectomy weight, body mass index, bilateral prophylactic mastectomy, and race, but less than adjuvant radiation, reconstruction type, and psychiatric diagnoses. Preoperative Satisfaction with Breasts scores are an important independent predictor of postoperative satisfaction after PMBR. Just as vital sign and work-up are carefully documented before surgery, preoperative scores should be collected to pre-emptively gauge patients' satisfaction and optimize postoperative outcomes.

The Relationship between information literacy, learning space and TPACK on prospective teachers' digital mathematics Literacy

This study aims to examine the relationship between information literacy, optimized learning spaces, and TPACK Skills on the mathematical digital literacy skills of prospective mathematics teachers. The method is non-experimental research with a causal correlation design and a quantitative approach using the Structural Equation Modeling (SEM) analysis method. The population in this study were undergraduate mathematics teacher candidates. The sample was obtained from as many as 216 respondents using purposive sampling from public and private universities in Riau, West Sumatra, Bengkulu, Banten, Jakarta, West Java, Yogyakarta, and East Java. The instrument used is a Likert scale on a scale of 1-5, which consists of 24 indicators representing 4 variables. Based on the results of the assumption test, validity test, and model fit test, it can be concluded that the SEM model in this study is suitable and feasible and can describe the theory and findings in the field. Based on the inner model test, it can be concluded that information literacy (LT), Optimize Learning Spaces (LS), and TPACK Skills (TK) have significant effects on mathematical digital literacy (DL) in terms of estimates and P &lt;0.05. The results of this research indicate that the increasing need for the application of learning technology, especially in mathematics learning, requires increasing the competence of teachers and students in integrating technology in mathematics learning and digital mathematics literacy needs to be the focus of further research.

Prediction of suicidal thoughts and behaviors based on the diurnal cortisol pattern and THC dosage in continued cannabis users, a 5 year population-based matched cohort study

It appears that the THC dosage is the link between dysregulation of the hypothalamic pituitary adrenal (HPA) axis and suicidal thoughts and behaviors (STB). We proposed a new model to understand the underlying pathophysiological mechanism of STB based on the interaction of cortisol and THC dosage.From September 1, 2019, to January 1, 2024, we conducted a population-based, matched-pair, nested case-control study resulting from a three-wave complete longitudinal, multicenter cohort study on a sample of congress 60 clients. A total of 368 male continued cannabis users (CCu) were allocated to four categories, including low, moderate and high THC dosages and relapse, using optimal matching. Several HPA axis measures were analyzed in the saliva using liquid chromatography with tandem mass spectrometry (LC-MS-MS), and carboxylic acids levels in the urine were assessed via gas chromatography/mass spectrometry (GC–MS). We used structural equation modeling (SEM) to examine the relationship between the variables of interest and the model fit test, and used the Akaike information criterion (AIC) to compare the model fit and select the best-fitting model. Population attributable fractions (PAFs) and cumulative risk score were also calculated for the best-fitting pattern.The analysis showed that the likelihood of STB in individuals with a cortisol awakening response (CAR) and a blunted diurnal cortisol slope (DCS) and higher area under the curve (AUC) who reported heavy cannabis use was more than three times higher than the control group (OR 3.2, 95 % CI 2.4–4.1).These findings indicate the importance of the specific cortisol secretion pattern in the increased clinical expression of STB and may be an important factor for guiding preventive efforts in this area.

Fit Indices in Structural Equation Modeling and Confirmatory Factor Analysis: Reporting Guidelines

This research explores the essential aspects of reporting fit indices in Structural Equation Modeling (SEM), focusing on their significance, methodologies for evaluation, and implications for model validity. The aim is to provide a comprehensive understanding of how fit indices contribute to the rigor and reliability of SEM studies. Methodologically, the study reviews prominent fit indices such as Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), Standardized Root Mean Residual (SRMR), and Chi-Square Test of Model Fit (χ²). Each index is defined, and specific threshold values are discussed to guide researchers in interpreting their findings effectively. Originality in this study lies in synthesizing current literature to emphasize the importance of transparent reporting practices in SEM, enhancing methodological clarity and promoting replicable research outcomes. Contributions include a structured approach to understanding fit indices’ roles in model assessment and validation, aiding researchers in advancing theoretical frameworks with robust empirical support.

Regeneration of rural rice farmers in Central Java Province

Central Java Province is one of the rice production centers in Java Island which plays a role in supporting national food availability. The phenomenon of a decrease in the number of agricultural business households over 10 years by 16.3 % indicates a shift in the interest of people working in the agricultural sector, switching to other sectors outside the agricultural sector. The lack of interest of the younger generation in farming indicates the phenomenon of a shift in community interest in the agricultural sector. This is inseparable from the regeneration of farmers in rural areas. This research uses the basic method of descriptive analysis. The research location was determined by purposive method so that the district with the largest average harvest area and rice production, namely Grobogan District and the location that reflects the adoption of technology, namely Sukoharjo District. The number of research samples was 250 samples of rice farmers in Central Java Province. Factors affecting the regeneration of rice farmers in Central Java Province were analyzed by SEM PLS. The results of the inner model analysis showed (1) latent variables of individual characteristics, socio-cultural environment, and farmer empowerment significantly positive effect on the performance of rice farmers, (2) economic environment variables and farm management had, a significantly negative effect on the performance of rice farmers, (3) latent variables of socio-cultural environment, economic environment, and performance of rice farmers have a significant positive effect on the regeneration of rice farmers in Central Java Province and (4) variable farmer empowerment has a significant negative effect on the regeneration of rice farmers in Central Java Province. The results of the model fit analysis and quality indices show that the model fit test has been fit (ideal).

Development of the UroARC Surgical Calculator: A Novel Risk Calculator for Older Adults Undergoing Surgery for Bladder Outlet Obstruction.

Bladder outlet obstruction (BOO) is common in older adults. Many older adults who pursue surgery have additional vulnerabilities affecting surgical risk, including frailty. A clinical tool that builds on frailty to predict surgical outcomes for the spectrum of BOO procedures would be helpful to aid in surgical decision-making but does not currently exist. Medicare beneficiaries undergoing BOO surgery from 2014 to 2016 were identified and analyzed using the Medicare MedPAR, Outpatient, and Carrier files. Eight different BOO surgery categories were created. Baseline frailty was calculated for each beneficiary using the Claims-Based Frailty Index (CFI). All 93 variables in the CFI and the 17 variables in the Charlson Comorbidity Index were individually entered into stepwise logistic regression models to determine variables most highly predictive of complications. Similar and duplicative variables were combined into categories. Calibration curves and tests of model fit, including C statistics, Brier scores, and Spiegelhalter P values, were calculated to ensure the prognostic accuracy for postoperative complications. In total, 212,543 beneficiaries were identified. Approximately 42.5% were prefrail (0.15 ≤ CFI < 0.25), 8.7% were mildly frail (0.25 ≤ CFI < 0.35), and 1.2% were moderately-to-severely frail (CFI ≥0.35). Using stepwise logistic regression, 13 distinct prognostic variable categories were identified as the most reliable predictors of postoperative outcomes. Most models demonstrated excellent model discrimination and calibration with high C statistic and Spiegelhalter P values, respectively, and high accuracy with low Brier scores. Calibration curves for each outcome demonstrated excellent model fit. This novel risk assessment tool may help guide surgical prognostication among this vulnerable population.

Model of Interest in Buying Fashion Products on Tiktok Shop Which is Influenced by Influencer Marketing and Price Perception

In the rapidly developing digital era, competition between companies in the e-commerce sector is increasingly competitive. Social media is a supporting tool for consumers in sharing text, video, image and audio information, and currently technology is developing more quickly, because of the large number of internet users, therefore all activities can be done online, one of which is online shopping. one example is TikTok Shop. TikTok Shop is a social commerce feature that advertises and sells their products via TikTok. This shopping feature is present on TikTok as a response to increasing sales of various products after brands carried out promotions via social media platforms. Influencers are netizen actors who have social media accounts and continue to spread messages or campaign content to other netizens to strengthen the message or content. Influencer marketing is a marketing strategy in product sales by promoting a product by someone who is considered to have influence. Price is one component of a company's marketing strategy, with the aim of providing added value to consumers so that they continue to consume the products and services being sold. Consumers will tend to prefer a more affordable price to obtain an item. Consumers tend to prefer more affordable prices to get the goods they want. The aim of this research is to determine the formation of the Purchase Interest model which is influenced by Influencer Marketing and Price Perception. This research uses a side purposive method which involves selecting samples from the population studied based on certain criteria. Structural Equation Modeling (SEM) analysis was used to analyze the data. The results of the validity test output show that this research data is declared valid and the construct reliability test shows that each variable in this research is declared consistent. The statistical assumption test shows that it has a positive and significant effect on influencer marketing which can provide a stimulus to prices and purchasing interest which encourages customers to make purchases. The path analysis model fit test shows the similarity of several samples which can substitute for the population as a whole

EVALUASI EFEKTIVITAS SISTEM DETEKSI PENIPUAN BERBASIS AI MENGGUNAKAN METODE REGRESI LOGISTIK UNTUK MENINGKATKAN KEAMANAN TRANSAKSI PADA STARTUP FINANCE

This research evaluates the effectiveness of an AI-based fraud detection system in a finance startup using logistic regression methods. The primary objective is to assess whether this model is feasible for enhancing transaction security. The research methodology employed is quantitative, utilizing historical transaction data. The test results indicate that the logistic regression model can accurately predict the company's financial conditions, with an accuracy of 95%. However, there are challenges in detecting fraudulent transactions, as evidenced by the low recall for fraudulent transactions. The model evaluation was conducted using several performance metrics such as accuracy, precision, recall, and F1-score, as well as model fit tests including Hosmer and Lemeshow’s Goodness of Fit Test, Omnibus Test of Model Coefficients, and -2 log-likelihood. In conclusion, this model has the potential to enhance transaction security, but it still requires improvements to more effectively detect fraudulent transactions

The Influence of Paylater and Affiliate Features on Consumer Buying Interest in Shopping on the Shopee Aplication

The paylater feature is a digital payment feature that uses an e- commerce platform, purchasing goods among teenagers is very lacking due to the economic conditions that are currently unstable. The method used in this research is quantitative method. The population in this study was conducted at the unswagati Cirebon campus, level 1-4 management study program as many as 147 respondents. The sampling technique used by researchers is non-probability sampling with purposive sampling technique and data analysis used in this researcher is regression and descriptive analysis then the analysis methods used in this study include validity test, cross tabulation, reliability test, normality test, multicollinearity test, hypothesis testing, path analysis fit model test using SPSS and amos Softwer analysis tools to test the effect of paylater and affiliate features on consumer buying interest. The results of this study indicate that the paylater feature has a positive effect on consumer buying interest, affiliates have an effect on consumer buying interest. Because the validity and reliability test results are less than 0.1362, the normality test is less than 0, 01, the multicoronality test is less than 10, hypothesis testing, the results show that all hypotheses are accepted, because the C.R value is more than 1.98, namely with a C.R value of 7.875 and 11.532 respectively. And the P value also shows the same result if the P value is less than 0.05 or symbolized * (below 0.01), it means that the two hypotheses have a significant effect or are accepted. path analysis fit model test is accepted because the GFT measure shows a P-value of more than 0.05, RMSE less than 0.08, CMIN less than 2.00, GFI less than 0.90, AGFI less than, 0.90, CFI more than 0.90, TLI more than 0.90. So it can be stated that the path analysis model fit test is accepted.

Assessing methane emissions from paddy fields through environmental and UAV remote sensing variables.

Concerns about methane (CH4) emissions from rice, a staple sustaining over 3.5 billion people globally, are heightened due to its status as the second-largest contributor to greenhouse gases, driving climate change. Accurate quantification of CH4 emissions from rice fields is crucial for understanding gas concentrations. Leveraging technological advancements, we present a groundbreaking solution that integrates machine learning and remote sensing data, challenging traditional closed chamber methods. To achieve this, our methodology involves extensive data collection using drones equipped with a Micasense Altum camera and ground sensors, effectively reducing reliance on labor-intensive and costly field sampling. In this experimental project, our research delves into the intricate relationship between environmental variables, such as soil conditions and weather patterns, and CH4 emissions. We achieved remarkable results by utilizing unmanned aerial vehicles (UAV) and evaluating over 20 regression models, emphasizing an R2 value of 0.98 and 0.95 for the training and testing data, respectively. This outcome designates the random forest regressor as the most suitable model with superior predictive capabilities. Notably, phosphorus, GRVI median, and cumulative soil and water temperature emerged as the model's fittest variables for predicting these values. Our findings underscore an innovative, cost-effective, and efficient alternative for quantifying CH4 emissions, marking a significant advancement in the technology-driven approach to evaluating rice growth parameters and vegetation indices, providing valuable insights for advancing gas emissions studies in rice paddies.