Application of Sensitivity & Principal Component Analyses for Modelling of Safety Parameters for Oil & Gas Companies in Niger Delta
Authors: T. Sala, Lazarus , Ify L. Nwaogazie, , N. Ugbebor, John , J. Inyang, Ubong , O. Onofeghara, Caleb , V. Fowode, Kayode , O. Ozobu, Cynthia , N. Eyenike, 
Published: February 2025

This study assesses the sensitivity analysis in classical multiple regression models as well as those of the principal component regression, PCR models. The study is a cross-sectional research design and data collection was through questionnaire distribution to employees of Oil & Gas indigenous (OGI) and multinational (OGM) companies in Niger Delta. The questionnaire was designed to obtain information from respondents on 7 safety constructs as independent variables against employee productivity as dependent variable. The questionnaire was designed using 5-point Likert scale, strongly agree, agree, disagree, strongly disagree and undecided with corresponding weighting of 5 – 1. The descriptive statistics featured the calculation of weighted arithmetic mean which served as input data for the calibration of both simple linear regression models, multiple principal component regression models and classical multiple regression models, respectively. SPSS was employed to calculate Cronbach's alpha coefficients, assessing the internal consistency of the research constructs; while XLSTAT, was used for principal component analysis, PCA & principal component regression, PCR. The relationship between the slope of simple linear regression equation for 7 independent variables/constructs were examined with respect to the corresponding goodness of fit. The result showed that the simple linear regression models of the Employee Productivity as dependent variable against each of the 7 independent variables (Employee Involvement, Management Commitment, Safety Compliance, Safety Knowledge, Safety Participation, Safety Promotional Policies & Safety Training) yielded positive slope values for both OGI & OGM. In contrast to the classical multiple regression model, for which the total goodness of fit value for the 7 constructs for OGI = 44.9 & 48.4% for OGM; the simple linear models for the same 7 construct yielded total goodness of fit of 81.2 & 171.1% for OGI & OGM, respectively. The total goodness of fit values for the simple linear regression were not only greater than those of classical multiple regression models, but exceeded 100% limit in classical multiple regression models. The principal component regression models established in this study is a variable reduction technique. Given that all the R2 values of PCR models (i.e. 3 & 4 constructs) are superior to the equivalent R2 values extracted from sensitivity values of classical multiple regression models; the reason is on the manner of solving the resulting simultaneous equations arising from those of PCR and classical multiple regression. The PCR modelling adopts the optimization technique, using XLSTAT version 16 in this study; while the classical multiple regression modelling utilizes the Gaussian method of solving the simultaneous linear equations. Therefore, the optimization method of solution of simultaneous equations using XLSTAT software is recommended.