Guide to Advanced Empirical

Quality Attributes for Classifying Theories

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• 2.2.1. Applicability for Quantitative Data

2.2. Quality Attributes for Classifying Theories Before we take a detailed look at how different approaches instantiate the general process steps, we introduce some quality attributes that apply to theory building approaches. These quality attributes can be used to. Characterize the specific aspects of a given theory building approach. Classify and compare the different theory building approaches so as to select the most suitable Based on our experiences with decision support and technology transfer, we choose the following eight quality attributes as most relevant to robust and useful theories (1) Applicability for qualitative data, (2) applicability for quantitative data, (3) scal- ability, (4) objectivity, (5) fairness, (6) ease of use, (7) openness, and (8) cost. Since we are only intending to give tendencies on how these quality attributes are met by different approaches to theory building, we will rate each approach for each attribute as either +, ±, or −. In this scheme a + indicates that the given approach can produce output that is rated well for this attribute, while a − definitely indicates that the approach is not well suited for users to whom this attribute is important. A ± is used in the case where no clear tendencies can be identified. 2.2.1. Applicability for Quantitative Data This attribute indicates whether or not an approach makes use of quantitative data such as numeric measures of cost, quality, or schedule impact. Approaches that explicitly do not include such information will be indicated by awhile others which explicitly include them will be indicated by ab. Applicability for Qualitative Data This attribute indicates whether or not an approach makes use of qualitative data such as lessons learned, whitepapers, or expert statements and interviews. Some approaches explicitly do not include such information (which will be indicated by awhile others explicitly include them (indicated by a +). 346 F. Shull and R.L. Feldmann Download 1.5 Mb. Share with your friends: