Development of Affective Self-Assessment Instrument of Chemistry for High School Student as the Daily Assessment Guideline

<p style="text-align: justify;">The study objectives were (1) developing a valid and reliable Affective Self-assessment Instrument of Chemistry for High School Student and (2) discovering the chemistry affective domain ability trend of high school students based on gender. The current development study utilized 10 non-test instrument development procedures from Mardapi. The study population was all high school students in Yogyakarta Special Region. The sample size was 405 students categorized into two stages and sampling techniques, i.e., the trial stage using cluster random sampling and the measurement stage using simple random sampling. The data analysis techniques were validity test using the Aiken index and construct validity and reliability using the second-order Confirmatory Factor Analysis model. The study findings were (1) the Affective Self-assessment Instrument of Chemistry for High School Student had 15 valid and reliable items and 15 available items to be utilized by teachers to measure students’ affective in the learning process and (2) the chemistry affective domain ability trend of male high school students was dominated by the “good” category and “very good” category for female students.</p>

Structural Equation Modeling using Stata

In this tutorial, you will learn how to fit structural equation models (SEM) using Stata software. SEMs can be fit in Stata using the sem command for standard linear SEMs, the gsem command for generalized linear SEMs, or by drawing their path diagrams in the SEM Builder. After a brief introduction to Stata, the sem command will be demonstrated through a confirmatory factor analysis model, mediation model, group analysis, and a growth curve model, and the gsem command will be demonstrated through a random-slope model and a logistic ordinal regression. Materials and datasets are provided online, allowing anyone with Stata to follow along.

The Basic Empathy Scale in Chinese College Students: Adaptation and Psychometric Properties of a Revised Form

This study aimed to revise the Chinese version of the Basic Empathy Scale for college students. The cluster random sampling method was used to select 805 college students from two universities to conduct confirmatory factor analysis, correlation analysis, reliability analysis, and an independent samples t-test. The confirmatory factor analysis model illustrated that the two-factor model failed to fit the data, and the two-factor model with methodological effect was finally accepted. Therefore, the questionnaire exhibits a strong methodological effect among Chinese college students which requires further study. Emotional and cognitive empathy had a significant positive correlation with gratitude and Internet altruism behavior, which showed good convergent validity. The gender difference test revealed that the emotional empathy level of girls was significantly higher than that of boys. The revised Basic Empathy Scale showed acceptable reliability and validity.

Psychometric Evaluation of the Diabetes Distress Scale in Patients with Type 2 Diabetes in Turkey

The objective of the research was to evaluate the validity and reliability of the Diabetes Distress Scale in patients with type 2 diabetes in Turkey. Materials and Methods. Our study was conducted between September 2016 and January 2017 and included 170 patients with type 2 diabetes. The Personal Information Form and Diabetes Distress Scale were used as a data collection tool. Statistical analysis was performed using SPSS 23.0 and SAS package program. Results. According to the results of the research, Cronbach’s alpha reliability for the total scale was 0.91. The model fit indices for the revised confirmatory factor analysis model failed to meet the criteria for acceptability: the GFI was 0.8185, the CFI was 0.9316, the Bentler - Bonett (1980) NFI was 0.9005, and the RMSEA was 0.1067. In our study, exploratory factor analysis provided support for the three-factor model: [I] emotional and regimen-related distress, [II] health professional-related distress, [III] diabetes-related interpersonal distress. Conclusions. When the translation and cultural adaptation process have been considered, the Diabetes Distress Scale is a valid and reliable tool for the Turkish community. It is recommended to be used in the studies and clinical trials.

Influence of Digital Finance on Efficacy of Entrepreneurship by Returning Migrant Workers

Digital finance provides an ideal entrepreneurial environment for returning migrant workers (RMWs). From the perspective of entrepreneurs, many scholars have quantified the factors affecting entrepreneurship, as well as the entrepreneurial environment, theorized the importance, motives, and internal/external impactors of RMW entrepreneurship, and put forward quite a lot of countermeasures. This paper innovatively evaluates how digital finance influences the efficacy of RMW entrepreneurship. Firstly, the authors established an influencing factor analysis model and an RMW entrepreneurship model and explained principles for the structural equation modeling of the influence of digital finance on RMW entrepreneurship efficacy. Next, the traditional partial least squares (PLS) regression was optimized, the optimal initial iteration values (IIVs) were obtained, and the algorithm convergence was achieved. Finally, a multilayer structural equation model (SEM) was constructed to evaluate the influence of digital finance on RMW entrepreneurship efficacy. The proposed algorithm and model were proved valid and feasible through experiments.

Nexus between entrepreneurship education, motivations, and intention among Indian university students: The role of psychological and contextual factors

This study assesses the impact of different psychological and contextual factors on entrepreneurial motivations and the role of entrepreneurial motivations in determining the entrepreneurial intention of students at Indian universities. The paper also explores whether gender acts as a moderator in the entrepreneurial motivation–intention relationship. Cross-sectional data were collected by administering a questionnaire to 329 students who had received entrepreneurship education during their course program. A confirmatory factor analysis model was run to ensure the model’s fitness, reliability, and validity, while structural equation modelling was used to test the hypotheses. The study validates the following key findings. First, the psychological factors of perceived cultural support and entrepreneurship education foster entrepreneurial motivations, which help determine students’ entrepreneurial intention, whereas fear of failure negatively affects students’ entrepreneurial motivations. Second, the contextual factors of government support policies and access to entrepreneurial finance do not influence entrepreneurial motivations. Third, entrepreneurial motivations significantly enhance the entrepreneurial intention of students. Fourth, gender negatively moderates the entrepreneurial motivation–intention relationship. The study contributes to the literature on the entrepreneurial motivation–intention relationship using psychological and contextual factors and also explores the interaction effect of gender on that relationship.

Application of Deep Neural Network Factor Analysis Model in Operating Room Management Nursing Analysis of Postoperative Infection Nursing after Thoracic Surgery

Thoracic surgery is the main surgical method for the treatment of respiratory diseases and lung diseases, but infections caused by improper care are prone to occur during the operation, which can induce pulmonary edema and lung injury and affect the effect of the operation and the subsequent recovery. Therefore, it is necessary to control the disease in time and adopt more scientific and comprehensive nursing measures. Based on the neural network algorithm, this paper constructs a neural network-based factor analysis model and applies the operating room management nursing to postoperative infection nursing after thoracic surgery and verifies the effect through the neural network model. The statistical parameters in this article mainly include the postoperative infection rate of thoracic surgery, patient satisfaction, postoperative rehabilitation effect, and complications. Through statistical analysis, it can be known that operating room management and nursing can play an important role in postoperative infection nursing after thoracic surgery, effectively reducing postoperative infection nursing after thoracic surgery, and improving the recovery effect of patients after infection.

Analysis of Driving Factors of Innovation and Entrepreneurship Based on Time Series Analysis

In order to analyze the driving factors of innovation and entrepreneurship, based on the time series analysis algorithm, this paper combines the analysis requirements of innovation and entrepreneurship driving factors to improve the time series, uses decomposition methods to decompose the complex original data into relatively simple components and reconstruct them, and predicts the reconstructed components to integrate the final predicted value. Moreover, this paper introduces entrepreneurial attitude as an intermediary variable and verifies it through data collection and statistical analysis, so that entrepreneurial traits influence entrepreneurial propensity through entrepreneurial attitude. The test results show that entrepreneurial attitude can better explain the influence of entrepreneurial traits on entrepreneurial propensity. In addition, this paper constructs an analysis model of driving factors for innovation and entrepreneurship, obtains experimental data through questionnaire survey methods, and conducts experimental research in combination with mathematical statistics. From the statistical results, it can be seen that the innovative and entrepreneurial driving factor analysis model based on time series analysis proposed in this paper is effective.

RGLS and RLS in Covariance Structure Analysis

This paper assesses the performance of regularized generalized least squares (RGLS) and reweighted least squares (RLS) methodologies in a confirmatory factor analysis model. Normal theory maximum likelihood (ML) and GLS statistics are based on large sample statistical theory. However, violation of asymptotic sample size is ubiquitous in real applications of structural equation modeling (SEM), and ML and GLS goodness-of-fit tests in SEM often make incorrect decisions on the true model. The novel methods RGLS and RLS aim to correct the over-rejection by ML and under-rejection by GLS. Proposed by Arruda and Bentler (2017), RGLS replaces a GLS weight matrix with a regularized one. Rediscovered by Hayakawa (2019), RLS replaces this weight matrix with one that derives from an ML function. Both of these methods outperform ML and GLS when samples are small, yet no studies have compared their relative performance. A confirmatory factor analysis Monte Carlo simulation study with normal data was carried out to examine the statistical performance of these two methods at different sample sizes. Based on empirical rejection frequencies and empirical distributions of test statistics, we find that RLS and RGLS have equivalent performance when N≥70; whereas when N&lt;70, RLS outperforms RGLS. Both methods clearly outperform ML and GLS with N≤400.