Univariate and multivariate normality in data cleaning and bias analysis: A case study on mobile recycling framework
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Abstract
The survey questions and results serve as the primary tools for testing hypotheses. However, the accuracy and quality of the final data analysis results depend on the accuracy of the generated data. Therefore, data normalization and cleaning are vital steps in the data processing phase of any analysis. This study focuses on new methods and techniques currently employed for data normalization, with a particular emphasis on univariate normality and multivariate normality analysis, as well as an examination of common-method bias (CMB), especially about data used in PLS-SEM analysis. Researchers should consider data screening methods during the survey design process and select appropriate data normality techniques based on theoretical principles. This paper provides a comprehensive guideline for conducting data normalization and measuring common method bias. Additionally, the paper highlights outdated methods that should be avoided.
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