Influence Of Predictor Variables Research Articles

Moderation of Perceived Usefulness and Ease of Online Learning Media on the Influence of Predictor Variables on Students' Level of Understanding

This study aims to determine the moderation ability of perceptions of the usefulness and convenience of online learning media on the influence of student computer anxiety on the level of understanding of accounting. To achieve the research objectives, primary data was collected through the distribution of questionnaires (which have passed the validity and reliability tests of the instrument) on samples determined by the purposive sampling method, namely selecting samples by determining specific characteristics that are in accordance with research objectives so that it is expected to be able to answer research problems. . The data collected was first tested for compliance with the classical assumption requirements, then analyzed using the Moderation Regression Analysis (MRA) technique. The results of this study are that computer anxiety has a negative effect on the level of student understanding, interest in learning has a positive effect on the level of student understanding, perceptions of usefulness and convenience do not moderate the effect of computer anxiety on the level of student understanding, perceived usefulness moderates the effect of interest in learning on the level of student understanding, and perceived ease does not moderate the effect of interest in learning on the level of student understanding.

MODELING DHF IN CENTRAL JAVA USING HYBRID NONPARAMETRIC SPLINE TRUNCATED-FOURIER SERIES APPROACH

Regression analysis aims to determine the relationship and influence of predictor variables on response variables through regression curve. The problem with nonparametric regression research so far is that it only uses one approach, causing the estimation results to be biased, even though each data sub-pattern has its own suitability depending on the approach method used. Therefore, the hybrid method emerged as a development of nonparametric regression. Hybrid models are models that combine approach methods, with the hope of increasing accuracy in modeling analysis. This research was carried out using two non-parametric approaches, namely Spline Truncated and Fourier Series. Dengue Hemorrhagic Fever (DHF) is a disease caused by the dengue virus. DHF is endemic and occurs throughout the year, especially during the rainy season because mosquitoes reproduce optimally. The aim of this research is to estimate the Hybrid Nonparametric Spline Truncated -Fourier Series model and apply the estimation results to data on DHF cases in Central Java. The data used to apply the hybrid nonparametric Spline Truncated-Fourier series regression model is DHF in the city/districts of Central Java. Estimation smoothing parameters uses the GCV (Generalized Cross Validation) method. The best model is selected based on largest R-Square and the smallest MSE. Modeling the disease of DHF cases in Central Java using the Spline Truncated-Fourier Series hybrid estimator produced the best model from the Spline Truncated model with two knot points for each predictor and the Fourier Series model with value of 9. Based on the results obtained, it can be compared that the Truncated Spline-Fourier Series hybrid model is better than the Spline Truncated model, this can be seen from the largest R-square, namely 99.94% and the smallest MSE.

Tobacco use and oral health related quality of life among Indian adolescents.

The present study was conducted to assess tobacco use and its effect on Oral Health-Related Quality of Life (OHRQoL) in Indian adolescents. The present study was conducted on 1,600 13-14 year old adolescents from the schools of Modinagar, western Uttar Pradesh, India selected using stratified random sampling technique. OHRQoL was assessed using Hindi version of Child Perceptions Questionnaire (CPQ). Information regarding demographic, socioeconomic and oral health measures was also collected from the study participants. Tobacco use was assessed through questions derived from Global Youth Tobacco Survey (GYTS) questionnaire. Influence of predictor variables on tobacco use was evaluated using multilevel Poisson regression model. The tobacco use among the study population was 8.1 % and CPQ scores were 9.15±0.32. Adolescents who used tobacco had worse OHRQoL scores. Low socioeconomic status, presence of dental caries, absence of regular dental visits (last 6months) were associated with increased regular consumption of tobacco products. The findings of the present study play an important role in planning public health strategies to improve adolescent OHRQoL and reduce tobacco use.

10대 및 20대 학습자의 성취목표 잠재프로파일 분류 및 예측변인 탐색

Objectives This study classified potential profiles according to the level of achievement goal orientation targeting Korean learners in their teens and 20s, and investigated explicit and implicit should-thought, gender, subject, fear of failure, academic difficulty (comprehension and amount), and positive and negative emotional reaction to to study, explicit and implicit emotional exhaustion about schoolwork, and academic procrastination to predict potential group classification. Methods The study participants were 629 teenagers and 864 students in their 20s who participated online. Latent profile analysis was conducted through the results of the achievement goal orientation scale, and multinomial logistic regression analysis was performed to confirm the influence of predictor variables on the latent profile. Results As a result, teen learners were divided into 3 potential groups and learners in their 20s into 5 potential groups, and the three groups of High All Goals, High-intermediate All Goals, and Average All Goals were identified in both age groups. Low All Goals and Mastery Goals were identified in 20s only. It was confirmed that fear of failure, academic efficacy, and should-thinking, and positive emotions to studying were related to High All Goals, and academic efficacy, should-thought, and positive emotions to studying were found to be related to High-intermediate All Goals. In addition, factors that act differentially according to the age of the potential group were also found. Conclusions These results suggest the need to consider the learner's developmental level in understanding the latent group profile and predictive factors, and help the learner's academic adjustment by exploring the relevance of the profile by dealing with relevant variables that affect academic adjustment. It is meaningful in that it provided implications for the intervention necessary for this purpose.

What Factors Predict COVID-19 Vaccine Uptake Intention in College Students?

ABSTRACT Background Increasing COVID-19 vaccine intention in college students is essential to mitigate serious illness and transmission to higher risk populations. We currently do not have an adequate understanding of college students’ perceptions and likelihoods of getting vaccinated against COVID-19. Purpose To quantitatively examine the demographic and psychosocial factors that predict college students’ intent of COVID-19 vaccination. Methods Undergraduate students were recruited through general education courses at a public western US university to complete an online survey measuring their perceptions, experiences, and behaviors regarding COVID-19. Two binary logistic regression models ascertained the influence of predictor variables on college students’ vaccination intention. Results Participants (n = 241) were between ages 18 and 24, with a majority identifying as female (74.7%). The final binary logistic regression indicated that perceived susceptibility (Exp (B) = [1.167], p = .002, 95% CI (1.059, 1.286)) significantly predicted likelihood of obtaining a COVID-19 vaccine. Discussion University-based interventions focusing on college students should address perceived susceptibility regarding vaccination to promote uptake in this population. Translation to Health Education Practice Our study emphasizes opportunities for health educators in college settings to implement programming and communication campaigns focusing on perceived susceptibility to better mitigate the COVID-19 pandemic.

Test Efficiency Analysis of Parametric, Nonparametric, Semiparametric Regression in Spatial Data

Regression analysis has three approaches in estimating the regression curve, namely: parametric, nonparametric, and semiparametric approaches. Several studies have discussed modeling with the three approaches in cross-section data, where observations are assumed to be independent of each other. In this study, we propose a new method for estimating parametric, nonparametric, and semiparametric regression curves in spatial data. Spatial data states that at each point of observation has coordinates that indicate the position of the observation, so between observations are assumed to have different variations. The model developed in this research is to accommodate the influence of predictor variables on the response variable globally for all observations, as well as adding coordinates at each observation point locally. Based on the value of Mean Square Error (MSE) as the best model selection criteria, the results are obtained that modeling with a nonparametric approach produces the smallest MSE value. So this application data is more precise if it is modeled by the nonparametric truncated spline approach. There are eight possible models formed in this research, and the nonparametric model is better than the parametric model, because the MSE value in the nonparametric model is smaller. As for the semiparametric regression model that is formed, it is obtained that the variable X2 is a parametric component while X1 and X3 are the nonparametric components (Model 2). The regression curve estimation model with a nonparametric approach tends to be more efficient than Model 2 because the linearity assumption test results show that the relationship of all the predictor variables to the response variable shows a non-linear relationship. So in this study, spatial data that has a non-linear relationship between predictor variables and responses tends to be better modeled with a nonparametric approach.

SAT-738 Sources of Error in Estimation of Cortisol Half-Life Using Conventional, Single-Compartment Model: Bias Due to Variation in CBG Concentration

Most reports of cortisol half-life in the literature report a range of 90-130 min, which results are based on descriptive model that assumes mono-exponential decay of a single, total cortisol compartment. Free cortisol half-life has been similarly assessed using a descriptive single compartment model (1). However, the descriptive model is not physiologic in view of the rapid exchange between protein-bound and free cortisol compartments and evidence that metabolic elimination is restricted to the free cortisol compartment. In the present study, we sought to explore potential limitations of the descriptive, single-compartment model for cortisol elimination by assessing the influence of CBG concentration ([CBG]) on cortisol half-life estimates obtained using the descriptive model. We studied the influence of [CBG] and other variables on descriptive cortisol half-life using a Monte Carlo simulation of cortisol concentration decay curves developed using data from healthy controls (1). Total cortisol concentration ([TF]) curves were generated on the basis of 4 predictor variables: (i) [CBG], (ii) albumin concentration, (iii) [TF] at time zero following iv bolus (total cortisol at time 0, y-intercept), and (iv) free cortisol half-life central to a mechanistic (dynamic, 3-compartment) model (2). Simulations used a multivariable normal distribution and selected means, SDs, and correlation structure among these 4 variables in healthy controls. After generation of a series of cortisol decay curves (n=1000), half-lives for total and free cortisol were solved using the conventional (descriptive, single-compartment) model. The influence of predictor variables on conventional half-life estimates were assessed using standardized beta (STB) coefficients, which represent change in the SD of the outcome (numerator, i.e. total or free cortisol half-life obtained by descriptive model) for each SD change in a predictor (denominator) in a multivariable context (3). For total cortisol half-life (descriptive model) STBs were 0.91 ([CBG]), 0.73 (free cortisol half-life), -0.37 (y-intercept), and 0.04 ([albumin]) (all P<0.001). For free cortisol half-life (descriptive model), STBs were 0.98 ([CBG]), 0.73 (free cortisol half-life), -0.78 (y-intercept), and 0.11 [albumin]) (all P<0.001). We conclude that the conventional descriptive model for estimation of cortisol has significant limitations, including inaccuracy and systematic bias related to the influence of CBG concentration on half-life estimates. By inference, a similar bias confounds interpretation of the half-life obtained using conventional single-compartment model of other hormones associated with high-affinity serum binding proteins.

Skeletal open bite cranial base characteristics in young Latin-American individuals with class I, II and III malocclusions: An observational study

This study aimed to compare cranial base linear and angular measurements between individuals with skeletal open bite and different sagittal skeletal relationships. This observational and cross-sectional study included 101 lateral radiographs of young Latin-American individuals with skeletal open bite that met the inclusion criteria and were collected from a private radiological centre. Three groups were formed. Groups 1, 2 and 3 included individuals with skeletal open bite (negative overbite and FMA>30°) and Class I (n=31), II (n=35) and III (n=35) sagittal skeletal relationship, respectively. The anterior and posterior cranial bases (SN and SBa), and cranial base angles (BaSN and ArSN) were measured. Groups were compared with one-way analysis of variance and post-hoc Scheffé tests. The influence of predictor variables on the cranial base were evaluated with linear regressions (α=0.05). The cranial base angles were significantly smaller (approximately 3° to 5°) in the skeletal open bite Class III group (BaSN=127.97°±5.86°, ArSN=120.19°±6.12°) when compared with the other groups. BaSN angle, Class I versus Class III (p<0.001) and Class II versus Class III (p<0.001). ArSN angle, Class I versus Class III (p=0.005) and Class II versus Class III (p=0.026). Multiple linear regressions showed that sex had a significant influence on both cranial base dimensions with men showing larger values than women. Skeletal open bite Class III individuals show a smaller cranial base angle than Skeletal open bite Class I or II individuals.

Titanium Ions Release from an Innovative Titanium-Magnesium Composite: an in Vitro Study.

The innovative titanium-magnesium composite (Ti-Mg) was produced by powder metallurgy (P/M) method and is characterized in terms of corrosion behavior. Two groups of experimental material, 1 mass% (Ti-1Mg) and 2 mass% (Ti-2Mg) of magnesium in titanium matrix, were tested and compared to commercially pure titanium (CP Ti). Immersion test and chemical analysis of four solutions: artificial saliva; artificial saliva pH 4; artificial saliva with fluoride and Hank balanced salt solution were performed after 42 days of immersion, using inductively coupled plasma mass spectrometry (ICP-MS) to detect the amount of released titanium ions (Ti). SEM and EDS analysis were used for surface characterization. The difference between the results from different test solutions was assessed by ANOVA and Newman-Keuls test at p<0.05. The influence of predictor variables was found by multiple regression analysis. The results of the present study revealed a low corrosion rate of titanium from the experimental Ti-Mg group. Up to 46 and 23 times lower dissolution of Ti from Ti-1Mg and Ti-2Mg, respectively was observed compared to the control group. Among the tested solutions, artificial saliva with fluorides exhibited the highest corrosion effect on all specimens tested. SEM micrographs showed preserved dual phase surface structure and EDS analysis suggested a favorable surface bioactivity. In conclusion, Ti-Mg produced by P/M as a material with better corrosion properties when compared to CP Ti is suggested.

A geospatial approach to the prediction of indoor radon vulnerability in British Columbia, Canada.

Radon is a carcinogenic radioactive gas produced by the decay of uranium. Accumulation of radon in residential structures contributes to lung cancer mortality. The goal of this research is to predict residential radon vulnerability classes for the province of British Columbia (BC) at aggregated spatial units. Spatially referenced indoor radon concentration data were partitioned into low, medium, and high classes of radon vulnerability. Radon vulnerability classes were then linked to environmental and housing data derived from existing geospatial datasets. A Balanced Random Forests algorithm was used to model environmental predictors of indoor radon vulnerability and values at un-sampled locations across BC. A model was generated and evaluated using accuracy, precision, and kappa statistics. The influence of predictor variables was investigated through variable importance and partial dependence plots. The model performed 34% better than a random classifier. Increased probabilities of high vulnerability were associated with cold and dry winters, close proximity to major river systems, and fluvioglacial and colluvial soil parent materials. The Kootenays and Columbia-Shuswap regions were most at risk. Here, we present a novel method for predictive radon mapping that is broadly applicable to regions throughout the world.

Prevalência de dor associada ao transporte de material escolar

The aim of this study was to determine the prevalence of pain and its association with the transportation of school supplies among university students. It was a cross-sectional study with 373 university students between February and September 2012 in which sociodemographic and academic data, as well as those regarding the transportation of school supplies and the presence of pain were collected through a semi-structured interview. Further analysis of anthropometric data was conducted and the volumes transported were weighted. Data were analyzed using the SPSS 17.0(r)software using the model of Hierarchical Binary Logistic Regression, Backward LR method, in which the power of influence of predictor variables was tested in the presence of musculoskeletal pain. Prevalence of pain of 82.8%, with overall average of 5.21 points on the Visual Analogue Scale (VAS) for pain assessment was found. Among women, the influence of the relative weight of volumes on the presence of pain was 45.1% higher than among men (PR = 0.689, CI 95% = 0.503 to 0.942) for each 1% increment. The transport time, in turn, increased by 22.9% the likelihood of occurrence of pain, every 15 minutes (PR = 1.229, CI 95% = 1.090 to 1.386). A high prevalence of pain related to the transportation of school supplies was observed, as well as the influence of predictor variables such as relative weight and transport time, especially in females.

Mixed effect frameworks in the analysis of longitudinal data

Longitudinal research generates data with correlated measurements and clustered data structures. The main interest in longitudinal studies is to find the change in outcome over time and to analyze the influence of predictor variables on the outcome. Model-based approach is a powerful tool to integrate the multiple measurements and complexity of such data. Mixed effect models provide the framework to identify the role of individual differences in responses, while incorporating information from multifactorial measures at the individual and group levels to advance our understanding of the underlying components influencing response. This methodology is flexible, could be extended to different data patterns and computes more accurate and stable estimates. Using a multilevel modeling approach, the hierarchical structure can be explicitly modeled. With the availability of standard statistical software for both classical and Bayesian approaches, its applicability has increased in various fields such as genome-wide association studies, understanding of disease/recovery process, disease-marker association and behavioral studies to understand personality traits and health outcomes. This review seeks to focus on the methodological approaches to model multiple failure time data, conceptual issues, arising due to correlation, heterogeneity, and clustering and estimation procedures. The article also emphasizes on their application in data analysis especially in immunology and rheumatology studies.

Snag Benchmarks and Treatment Options for Mixed‐Pine Forest Restoration in Eastern Upper Michigan

AbstractSnags (dead‐standing trees) are biological legacies that remain after disturbances in forests. We enhanced the ecological underpinnings of snag management within the context of mixed‐pine forest restoration in the northern Lake States by quantifying characteristics of live trees and snags within eighty-five 500‐m2 plots at Seney National Wildlife Refuge (NWR) in eastern Upper Michigan. Study plots represented reference conditions (i.e. no past harvesting, relatively unaltered fire regime) and altered (i.e. harvested, altered fire regime) conditions. We also compared three treatments for creating snags from live trees. Snags were found in 87% of the reference plots and 85% of the altered plots. The only snag variables that differed between plot types were mean snag basal area, which was greater in altered plots (Student's t‐test, p = 0.04), and mean percent total basal area of snags (greater in reference plots, p = 0.06). The composition of snags differed only in the 10‐ and 25‐cm diameter classes (Multi‐Response Permutation Procedure, p &lt; 0.10). The percentage of snags that developed into the most advanced decay class (DC) differed among treatments after 4 years (χ2 = 16.49, p &lt; 0.01), with 26% of girdled trees, 3% of prescribed fire trees, and zero topped trees reaching DC5. Logistic regression illustrated that the influence of predictor variables on DC development varied by species and treatment. The findings from this study, past studies, and ongoing projects at Seney NWR are directly applicable to innovative management of snags in mixed‐pine forests.

Assessing Intermediate Outcomes of a Faith-Based Residential Prisoner Reentry Program

Objective: This study examined intermediate outcomes of a faith-based prisoner reentry program by assessing how client spirituality related to client- and program-level characteristics, investigating differences between completers and terminators, and examining how religious preference, religiosity/spirituality, religious salience, and incarceration's impact on spirituality influenced program completion, satisfaction, and perceived progress. Method: The study employed independent t tests, chi-square tests, and regression models to examine the influence of predictor variables. Results: Religious preference was positively associated with progress and satisfaction. In addition, individuals reporting decreases in spirituality during prison were 34 times as likely to drop out of the program compared to those who had more positive in-prison spiritual changes. Salience, general religiosity/spirituality, and spiritual experiences did not predict outcomes. Implications of the findings are discussed.

Influence of biological and ecological factors on hematological values in wild Little Penguins, Eudyptula minor

Clinical hematology and biochemistry are recognized as useful aids in health diagnosis in birds. The Little Penguin (Eudyptula minor) is endemic to Australia and numbers are declining due to a number of factors including declining health. This 2-year study on both Bowen and Lion Island populations aimed at assessing the potential causes of their declining health. Blood was collected from a total of 294 adult Little Penguins. R.B.C., Hb, P.C.V., M.C.V., M.C.H., M.C.H.C., W.B.C. and differential counts, thrombocytes and T.P.P. were measured. Multiple regression was used to identify relationships between hematological values and the following predictor variables: site, season, sex, time held prior to sampling and body condition. Values obtained showed some differences compared to the values for other penguin species reported in the literature. P.C.V., R.B.C., Hb, M.C.H.C. and T.P.P. showed major differences in comparison with a previous study on little penguins. Considering the influence of predictor variables, it appeared that: P.C.V., R.B.C., M.C.V., M.C.H., M.C.H.C. varied according to seasons; P.C.V., R.B.C., M.C.H., T.P.P. were correlated to body condition; P.C.V. was higher in birds on Bowen Island compared to birds on Lion Island; and T.P.P. was higher in females. These ecological and biological predictor variables have a strong influence upon hematological values. As a consequence, they must be taken into consideration when interpreting results of future studies.

Insurance status and the treatment of myocardial infarction at academic centers.

Numerous studies have documented treatment disparities in patients with acute coronary syndromes based on race and gender. Other causes for treatment disparities may exist. To determine if insurance status affects quality of care in patients with acute myocardial infarction (AMI) presenting to academic health centers. The Internet Tracking Registry for Acute Coronary Syndromes (i*trACS), a prospective multicenter registry of patients with chest pain presenting to the emergency department who receive an electrocardiogram, was used as the database (N = 17,737). A subset of patients who were diagnosed as having AMI were selected from the database (n = 936). Patients were classified as having either ST-segment elevation MI (n = 178) or non-ST-segment elevation MI (n = 758). Insurance status, age, race, and gender were extracted as predictor variables. The influence of predictor variables on treatment modality was investigated using logistic regression, adjusted for clustering within sites. The odds of a self-pay patient with ST-segment elevation MI receiving fibrinolytics were 3.23 (95% CI = 1.56 to 6.69) times higher than for other patients. Patients with Medicare coverage were less likely to receive fibrinolytics (odds ratio [OR] 0.35, 95% CI = 0.19 to 0.65) and tended to undergo percutaneous coronary intervention less often (OR 0.60, 95% CI = 0.36 to 1.01). The odds of a privately insured patient's receiving coronary artery bypass grafting (OR 2.76, 95% CI = 1.62 to 4.72) or percutaneous coronary intervention (OR 1.47, 95% CI = 1.03 to 2.11) were higher than for other patients. Insurance coverage appears to affect treatment in patients with AMI, with self-pay patients more likely to receive less-expensive therapies and insured patients more likely to receive invasive treatments.