Multi-Criteria Data Evaluation Strategy for Development Scales: An Example Study

Abayomi Ibiyemi, Olayinka Ogungbemi, Martins Adenipekun


This study illustrates checks using data from the sustainability and Appraisers' scale designed to make causal analysis between perceived industrial sustainability benefits and Appraisers' support. It obtained questionnaire responses from 267 Real Estate Appraisers. The study objectives are to assess the pattern and extent of the missing data; the
assumptions of the multivariate normality with the bootstrap resampling; the consistencies of the slopes of change, and identify the costs that may arise from poor data quality. The work also investigates convergent, discriminant, cross boundary validities and highlights their relevance to the validity of research findings. The missing values at <10%; the normality assumption holds for the SUP, BPG, BLR constructs. Bollen-Stine bootstrap
analysis could not validate the normality models for BQL, BCS, BHBV; 60 of the 109 slopes of change are consistent (p>.05). The factor loadings reliably represent the unobserved variables (p>.35) and there is sufficient evidence for convergent and discriminant validities, but cross boundary validity is not proven. Nevertheless, with appropriate data transformation, there could be no necessity for further data collection, nor a new survey of
data. The study contributes to the accuracy of assessments and the interpretability of scores for testing theories.

Keywords: Data exploration, sustainability, real estate appraisers, evaluation, benefits.

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