Influence of Religion, Culture and Education on Perception of Climate Change and its Implications: Applying Structural Equation Modeling (SEM)
Daisuke Sasaki*1,, Irene Taafaki*2, Takuia Uakeia*3, Jennifer Seru*4, Yolanda McKay*2, and Hermon Lajar*4
*1International Research Institute of Disaster Science (IRIDeS), Tohoku University
468-1 Aza-Aoba, Aramaki, Aoba, Sendai, Miyagi 980-0845, Japan
*2Marshall Islands Campus, The University of the South Pacific, Majuro, Republic of the Marshall Islands
*3Kiribati Campus, The University of the South Pacific, Tarawa, Republic of Kiribati
*4College of the Marshall Islands, Majuro, Republic of the Marshall Islands
Currently, structural equation modeling (SEM) is widely used in the discipline of social sciences because of its capability in exploring causal relationships among variables. By applying SEM, this study aims to verify the hypothesis that there exist three fundamental factors (religion, culture, education) that influence the perception of climate change. The researchers took advantages of the output results of the questionnaire survey that had been conducted both in Majuro, the Republic of the Marshall Islands (RMI) and in Tarawa, Republic of Kiribati, in an international collaborative research project titled “How Religion, Culture and Education Influence the Perception of People about Climate Change.” The results indicated that the two cases, namely RMI and Kiribati, were similar; that is the basic structure of both cases in the background of climate change bears some resemblance. Meanwhile, it should be noted that the path coefficient from education to the perception of climate change in the case of Kiribati (0.86) is much higher than that in the case of RMI (0.47). Thus, it is implied that education may significantly influence the perception of people about climate change and its implications, both in RMI and Kiribati. Based on this finding, it is advocated that further efforts should be devoted to education so that the perception of people about climate change and its implications can get much clearer.
-  M. Nakayama, S. Drinkall, and D. Sasaki, “Climate Change, Migration, and Vulnerability: Overview of the Special Issue,” J. Disaster Res., Vol.14, No.9, pp. 1246-1253, 2019.
-  M. Nakayama, I. Taafaki, T. Uakeia, J. Seru, Y. McKay, and H. Lajar, “Influence of Religion, Culture and Education on Perception of Climate Change, and its Implications,” J. Disaster Res., Vol.14, No.9, pp. 1297-1302, 2019.
-  D. Sasaki and Y. Ono, “Overview of the Special Issue on the Development of Disaster Statistics,” J. Disaster Res., Vol.13, No.6, pp. 1002-1006, 2018.
-  D. Sasaki, K. Moriyama, and Y. Ono, “Hidden Common Factors in Disaster Loss Statistics: A Case Study Analyzing the Data of Nepal,” J. Disaster Res., Vol.13, No.6, pp. 1032-1038, 2018.
-  P. Tarka, “An overview of structural equation modeling: its beginnings, historical development, usefulness and controversies in the social sciences,” Qual Quant, Vol.52, No.1, pp. 313-354, 2018.
-  D. Sasaki, “Application of Structural Equation Modeling (SEM),” Presentation, Honolulu, 2019.
-  R. P. Bagozzi and Y. Yi, “Specification, evaluation, and interpretation of structural equation models,” J. of the Academy of Marketing Science, Vol.40, No.1, pp. 8-34, 2012.
-  J. L. Arbuckle, “Computer Announcement AMOS: Analysis of Moment Structures,” Psychometrika, Vol.59, No.1, pp. 135-137, 1994.
-  K. A. Bollen and J. S. Long, “Tests for Structural Equation Models: Introduction,” Sociological Methods & Research, Vol.21, No.2, pp. 123-131, 1992.
-  S. A. Mulaik, L. R. James, J. van Alstine, N. Bennett, S. Lind, and C. D. Stilwell, “Evaluation of goodness-of-fit indices for structural equation models,” Psychological Bulletin, Vol.105, No.3, pp. 430-445, 1989.
-  J. C. Anderson and D. W. Gerbing, “Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach,” Psychological Bulletin, Vol.103, No.3, pp. 411-423, 1988.