Use of Structural Equation Modeling Techniques to Overcome the Empirical Issues Associated With Quantification of Attitudes and Perceptions

  • Jayasinghe-Mudalige U
  • Udugama J
  • Ikram S
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Abstract

The social scientists work on economic problems associated with attitudes and perceptions of respondents to a survey on a given issue naturally face several challenges with regard to quantification of which to develop estimable variables to be used in further analyses. This article explores, using the special case of economic incentives for Sri Lankan agri-food processing firms to adopt enhanced solid waste management practices in the firm, the outcome of statistical methods employed to overcome such empirical issues, including: (a) "Mutual Exclusivity" and "Endogeneity" of incentives, i.e. prevalence of an individual incentive as an element of a system; (b) "Subjectivity", i.e. the management of firm perceives unpredictably on these incentives in terms of potential benefits and costs to the firm, and (c) "Unobservability", i.e. the management cannot directly observe the nature of incentives prevailing at the firm level. It uses the Structural Equation Modeling techniques with the aid of Analysis of Moment Structures (AMOS) statistical package to overcome these issues, where a family of statistical models that seek to explain the relationships among multiple variables were formulated by combining a Measurement Model [commonly referred to as Confirmatory Factor Analysis (CFA)] with Structural Model into a simultaneous statistical test. The outcome of analysis, which used data collected from 325 firms by means of a questionnaire-based survey comprising of 9 Constructs/latent variables (i.e. incentives considered in the analysis) and 51 Indicators (attitudinal statements), facilitate deriving an Incentive Index for each incentive reflecting its relative strength at the level of firm, and in turn, to use as explanatory variables in modeling.

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Jayasinghe-Mudalige, U., Udugama, J., & Ikram, S. (2013). Use of Structural Equation Modeling Techniques to Overcome the Empirical Issues Associated With Quantification of Attitudes and Perceptions. Sri Lankan Journal of Applied Statistics, 13(0), 15. https://doi.org/10.4038/sljastats.v13i0.5122

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