Multilevel factor analysis based online financial credit system

2018 ◽  
Vol 52 ◽  
pp. 466-472
Author(s):  
Fu-You Li ◽  
Wan-Nan Zhao
Keyword(s):  
Factor Analysis ◽  
Credit System ◽  
Multilevel Factor Analysis ◽  
Financial Credit

scholarly journals Give us a little social credit: to design or to discover personal ratings in the era of Big Data

2020 ◽  
Vol 16 (3) ◽  
pp. 369-387 ◽  
Author(s):  
Abigail Devereaux ◽  
Linan Peng
Keyword(s):  
Big Data ◽  
Credit Rating ◽  
Credit Ratings ◽  
Personal Characteristics ◽  
The State ◽  
State Council ◽  
Credit System ◽  
Personal Credit ◽  
Social Credit ◽  
Financial Credit

AbstractIn 2014, the State Council of the Chinese Communist Party announced the institution of a social credit system by 2020, a follow-up to a similar statement on the creation of a social credit system issued by the State Council in 2007. Social credit ratings of the type being developed by the State Council in partnership with Chinese companies go beyond existing financial credit ratings in an attempt to project less-tangible personal characteristics like trustworthiness, criminal tendencies, and group loyalty onto a single scale. The emergence of personal credit ratings is enabled by Big Data, automated decision-making processes, machine learning, and facial recognition technology. It is quite likely that various kinds of personal and social credit ratings shall become reality in the near future. We explore China's version of its social credit system so far, compare the welfare and epistemological qualities of an ecology of personal ratings emanating from polycentric sources versus a social credit rating, and discuss whether a social credit system in an ideologically driven state is less a tool to maximize social welfare through trustworthiness provision and more a method of preventing and punishing deviance from a set of party-held ideological values.

scholarly journals A New Method for Computing Letter of Credit Risks

SAGE Open ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 215824402097021
Author(s):  
Cheng Zhang ◽  
Ni Hu
Keyword(s):  
Factor Analysis ◽  
Quantitative Method ◽  
Credit System ◽  
Letter Of Credit ◽  
Credit Risks ◽  
Confirmatory Factor ◽  
System Risk ◽  
Chinese Export ◽  
Main Factors ◽  
And Control

This study targets problems in the risk assessment and control processes of letter of credit settlements for Chinese export enterprises. It applies the quantitative method of exploratory factor analysis to extract the main factors and uses a confirmatory factor analysis to test the validity these constructs. VENSIM software is used to design the system dynamics causal tree and flowchart of the letter of credit system. The equation sets of DANAMO parameters are then constructed using the software. Finally, through analysis of the system risk fluctuation diagram with system simulation, it offers enterprises advice on how to identify potential risk points to prevent and control letter of credit risks in advance.

scholarly journals Multilevel factor analysis of flipped classroom in dental education: A 3-year randomized controlled trial

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257208
Author(s):  
Zuo Wang ◽  
Eiko Yoshida Kohno ◽  
Kenji Fueki ◽  
Takeshi Ueno ◽  
Yuka Inamochi ◽  
...  
Keyword(s):  
Randomized Controlled Trial ◽  
Factor Analysis ◽  
Learning Outcomes ◽  
Flipped Classroom ◽  
Dental Education ◽  
Controlled Trial ◽  
Confounding Factors ◽  
Instructional Style ◽  
Randomized Controlled ◽  
Multilevel Factor Analysis

Purpose Previous studies have rarely attempted to test the confounding factors that may affect learning outcomes of the flipped classroom. The purpose of this study was to assess how flipped classrooms affect the acquisition of knowledge in clinical dental education based on multilevel factor analysis. Method The authors conducted a 3-year (2017, 2018, and 2019) randomized controlled trial in a series of introductory prosthodontics courses in dental education. A total of 137 participants were randomly assigned to flipped classroom (n = 70, 51%) or lecture (n = 67, 49%) formats. The flipped group was instructed to self-learn knowledge-based content through online preparation materials, including videos and text, while the lecture group was given text only. Both groups were provided with the same study content and opportunities for different styles of learning. The session attendance rate and number of times the materials were accessed were monitored. Individual and team readiness assurance tests (IRAT/TRAT) were conducted to evaluate knowledge acquisition. A multilevel linear regression analysis was conducted on both instructional styles (flipped vs. lecture) as an intervention factor, and confounding factors that could affect the outcomes were implemented. Results The average number of online accesses was 2.5 times per session in the flipped group and 1.2 in the lecture group, with a significant difference (p < .05). The average IRAT score was significantly higher in the flipped than in the lecture group (effect size [ES] 0.58, p < .001). The number of online accesses was significantly and positively correlated with IRAT scores (0.6 [0.4, 0.8]). The instructional style was significantly and positively correlated with TRAT scores (coefficient [95% confidence interval]: 4.6 [2.0, 7.3]), but it was not correlated with IRAT (4.3 [-0.45, 9.0]). Conclusions The flipped classroom was more effective than the lecture format regarding knowledge acquisition; however, the decisive factor was not the instructional style but the number of individual learning occasions. The employment of the flipped classroom was the decisive factor for team-based learning outcomes.

An Illustration of Multilevel Factor Analysis

2005 ◽  
Vol 84 (2) ◽  
pp. 126-136 ◽  
Author(s):  
Steven P. Reise ◽  
Joseph Ventura ◽  
Keith H. Nuechterlein ◽  
Kevin H. Kim
Keyword(s):  
Factor Analysis ◽  
Multilevel Factor Analysis
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