Influence Factor Analysis for Strain Gage’s Gage Factor Calibration

2019 ◽  
Vol 07 (02) ◽  
pp. 19-27
Author(s):  
志鹏 晏
Keyword(s):  
Factor Analysis ◽  
Influence Factor ◽  
Gage Factor

Influence factor analysis of soil heavy metal Cd based on the GeoDetector

2020 ◽  
Vol 34 (6) ◽  
pp. 921-930
Author(s):  
Yunting Jiang ◽  
Yingjun Sun ◽  
Liping Zhang ◽  
Xvlu Wang
Keyword(s):  
Heavy Metal ◽  
Factor Analysis ◽  
Influence Factor ◽  
Soil Heavy Metal ◽  
Heavy Metal Cd

scholarly journals Human Factor Analysis Framework for Ghana’s Mining Industry

2020 ◽  
Vol 20 (2) ◽  
pp. 60-76
Author(s):  
T. Joe-Asare ◽  
N. Amegbey ◽  
E. Stemn
Keyword(s):  
Factor Analysis ◽  
Data Analysis ◽  
Human Factor ◽  
Influence Factor ◽  
Mining Industry ◽  
Analysis Framework ◽  
Causal Factors ◽  
Effective Application ◽  
Investigation Methods ◽  
Analysis Models

In an attempt to incorporate human factors into technical failures as accident causal factors, researchers have promoted the concept of human factor analysis. Human factor analysis models seek to identify latent conditions within the system that influence the operator’s action to trigger an accident.  For an effective application of human factor analysis models, a domain-specific model is recommended. Most existing models are developed with category/subcategory peculiar to a particular domain. This presents challenges and hinders effective application outside the domain developed for. This paper sought to propose a human factor analysis framework for Ghana’s mining industry. A comparative study was carried out between three dominated accident causation models and investigation methods in literature; AcciMap, HFACS, and STAMP. The comparative assessment showed that HFACS is suitable for incident data analysis based on the following reason; ease of learning and use, suitability for multiple incident analysis and statistical quantification of trends and patterns, and high inter and intra-coder reliability. A thorough study was done on HFACS and its derivative. Based on recommendations and research findings on HFACS from literature, Human Factor Analysis, and Classification System – Ghana Mining Industry (HFACS-GMI) was proposed. The HFACS-GMI has 4 tiers, namely; External influence/factor, Organisational factor, Local Workplace/Individual Condition and, Unsafe Act. A partial list of causal factors under each tier was generated to serve as a guide during incident coding and investigation. The HFACS-GMI consists of 18 subcategories and these have been discussed. The HFACS-GMI is specific to the Ghanaian Mines and could potentially help in identifying causal and contributing factors of an accident during an incident investigation and data analysis.   Keywords: Human Factor Analysis, Causal Factor, Causation Model, Mining Industry

scholarly journals Confirmatory Factor Analysis on Influence Measures of Investment

2019 ◽  
Vol 8 (3) ◽  
pp. 3977-3980
Keyword(s):  
Factor Analysis ◽  
Path Analysis ◽  
Financial Literacy ◽  
Influence Factor ◽  
Investment Decision ◽  
Primary Objective ◽  
The People ◽  
Confirmatory Factor ◽  
Standard Deviations ◽  
Investment Process

Background of the study: In an investment decision, several factors are responsible for a investors to make their investment or chose their preferred investment avenues. Factors such as, Benefit derived from the chosen investment, Objective of the particular investment, Financial literacy over the proposed investment, and influence of investment measures were mostly consider by the investors while making their investment measurement. Objective: This article explores the investment influence measures consider by the people for making their investment decision. This research paper has a primary objective to highlight the most and least influential measures consider by the peoples while making their investment. Methodology: This study is confirmatory by the way of proving the six investment influence measures of peoples’ investment decision emerged under influence factor by using the path analysis. There are two hundred sample respondent were identified under the purposive sampling in the urban area of Tiruchirrappli District. All the issued questionnaire were collected and scrutinized by using SPSS 20 statistical package and AMOS 20 to derive the path analysis. Result: All the six influence measures significantly influence investor’s investment decision. It is estimated that When influences dimension goes up by 1 standard deviation, Safety of the particular investment goes up by 0.769 standard deviations and prevailing Tax saving on the investment goes up by 0.733 standard deviations and Simplicity of the investment process goes up by 0.768 standard deviations

Sign in / Sign up

Export Citation Format

Share Document