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Evidence of the presence of bias in subjective metrics: Analysis within a family of experiments

Abstract:

Context: Measurement is crucial and important to empirical software engineering. Although reliability and validity are two important properties warranting consideration in measurement processes, they may be influenced by random or systematic error (bias) depending on which metric is used. Aim: Check whether, the simple subjective metrics used in empirical software engineering studies are prone to bias. Method: Comparison of the reliability of a family of empirical studies on requirements elicitation that explore the same phenomenon using different design types and objective and subjective metrics. Results: The objectively measured variables (experience and knowledge) tend to achieve more reliable results, whereas subjective metrics using Likert scales (expertise and familiarity) tend to be influenced by systematic error or bias. Conclusions: Studies that predominantly use variables measured subjectively, like opinion polls or expert opinion acquisition, must take every care to prevent bias that can result in incorrect results. Copyright 2014 ACM.