Search

College students experience great stress due to many factors, such as an uncertain future, academic responsibilities, and pressures imposed by social communication. Many institutions of higher education are focusing on how to mediate stressful situations and increase the subjective well-being (SWB) of students to sustain a lifestyle focused on wellness. The online survey used for this study focused on testing an exploratory SWB model by implementing partial least squares structural equation modeling (PLS-SEM) techniques. The participants were 470 college-aged students enrolled in seven different institutions in six cities across China. Findings yielded a good model fit (the Standardized Root Mean Squared Residual [SRMR] = .054) with the validity of manifest variables, reliability of the latent variables (LVs), and overall SWB model indicating moderate predictiveness (GoF R2 = .476) by the LVs. Additionally, all of the direct path coefficients and indirect path coefficients that consisted of four partial mediators and one full mediator yielded statistically significant results via bootstrapping. Furthermore, path coefficients for utilization of emotion to life satisfaction for the cognitive exercise group were significantly higher than for the non-cognitive exercise group. The findings illustrated a good model fit for an exploratory SWB model that can predict an individual’s SWB, and cognitive and non-cognitive exercises had different effects on the individuals’ SWB.

Aim: A combination of football becoming highly commercialized, technological advances made, and increasing amounts of data becoming available has made it possible for researchers to conduct statistical analyses of the various aspects of the game with an ultimate focus on determining the key factors for team success.Methods: This quasi-experimental study used an ex-post facto design to develop a model for team success. The sample consisted of 18 teams which played 306 matches in a 9-month long association football league format. A PLS-SEM path analysis was conducted using 11 latent variables. Results: Findings yielded a substantial overall model fit (GoF R2=0.811) for the measurement and structural models. The latent variables (LVs) of offence (β= 0.630, p< .001) and defence (β= 0.489, p

Background and PurposeThe purpose of this study was to evaluate Mishel Uncertainty in Illness Scale—Community (MUIS-C), used to gauge level of uncertainty among baby boomers with hepatitis C virus (HCV) infection, as a reliable two-factor instrument.MethodsA CFA was conducted to test MUIS-C. There were minor deviations from normality. Subsequently, 130 participants were used to examine the factor structure and the model fit. A robust maximum likelihood (ML) estimation using the Wishart distribution was implemented in R version 3.3.1.ResultsA very good model fit was obtained (χ2(101) = 118.32, p = .115, TLI = 0.977, CFI = 0.983, RMSEA = 0.036, 90%CI(0.000, 0.061), and SRMR = 0.057). All indicators showed significant positive factor loadings, with standardized coefficients ranging from 0.511 to 0.868.ConclusionsThe MUIS-C was a reliable two-factor instrument and suitable for use as such in baby boomer population with HCV.

Many faculty members, as well as students, in the area of educational research methodology, sometimes have a need for generating data to use for simulation and computation purposes, demonstration of multivariate analysis techniques, or construction of student projects or assignments. As a great teaching tool, using simulated data helps us understand the intricacies of statistical concepts and techniques. The process of generating multivariate normal data is a nontrivial process and practical guides without dense mathematics are limited in the literature Nissen and Saft, 2014 . Hence, the purpose of this paper is to offer researchers a practical guide for and a quick access to generating multivariate random data with a given mean and variance covariance structure. A detailed outline of simulating multivariate normal data with a given mean and variance covariance matrix using Eigen or spectral and Cholesky decompositions is presented and implemented in statistical computing platform R version 3.4.4 R Core Team, 2018 . Mehmet Turegun "Simulating Multivariate Random Normal Data using Statistical Computing Platform R" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23987.pdf

Background Baby boomers account for two out of every three cases of hepatitis C infection in the U.S. Purpose To conduct an exploratory factor analysis directed at supporting the use of the MUIS-C as a reliable instrument in measuring illness uncertainty among baby boomers with hepatitis C. Methods The steps of conducting a typical principal component analysis (PCA) with an oblique rotation were used on a sample of 146 participants, the sampling adequacy of items was examined via the Kaiser–Meyer–Olkin (KMO) measure, and the Bartlett's sphericity test was used for appropriateness of conducting a factor analysis. Results A two-factor structure was obtained by using Horn's parallel analysis method. The two factors explained a cumulative total of 45.8% of the variance. Conclusions The results of the analyses indicated that the MUIS-C was a valid and reliable instrument and potentially suitable for use in baby boomer population diagnosed with hepatitis C.

Science, technology, engineering, and mathematics (STEM) programs serving students from low socioeconomic areas are understudied in the literature. More research studies need to be conducted to make informed instructional decisions for students who may be at a disadvantage compared to their peers from higher socioeconomic areas. The purpose of this research study was to determine the effects of traditional science instruction and blended learning on STEM achievement of elementary school students from low socioeconomic areas. Third, fourth and fifth grade students (N = 129) from a low-socioeconomic school were randomly assigned to receive traditional science instruction or a blended learning science curriculum approach. The science, technology, engineering, and mathematics (STEM) achievement scores were analyzed by conducting a one-way two-group Multiple Analysis of Variance (MANOVA) implemented in R statistical computing platform (R Core Team, 2018). The results indicated that the teaching method had a statistically significant effect on the linear combination of the science, technology, mathematics and engineering scores (F(4,124) = 80.27, p < .0001, Pillai’s Trace = .721, partial η2 = .721), in favor of the blended learning approach.

O objetivo deste artigo é descrever uma abordagem de modelagem para ajudar os alunos a desenvolver uma compreensão conceitual dos níveis de significância estatística por meio de descoberta com base em suas próprias observações quando eles desempenham o papel de detetives de dados. Gostaria de poder promover esta ideia inteligente como minha, mas, na verdade, Eckert (1994) delineou uma atividade similar. Alguns anos mais tarde Roxy Peck demonstrou uma versão ligeiramente diferente dessa atividade em um encontro sobre Métodos Estatísticos Avançados. Tenho utilizado várias versões desta abordagem de modelagem ao longo dos anos em meus vários cursos de estatísticas e pesquisa quantitativa nos níveis de graduação e pós-graduação.