A New Approach in Requirements Elicitation Analysis

In requirements analysis the task of elicitation of stakeholder need has been a continuing source of error and frustration in systems development. To aid in the acquisition of a set of proper needs that are critical to the design of an effective system, the systems analyst is provided with a new tool to assist in determining when group consensus has been met with respect to the identification of one or more needs. A recently developed measurement tool for measuring subjective concepts like consensus, agreement, and dissent is described. Categorical data are frequently collected using an ordinal scale such as the Likert scale and a new method is available that gives the analyst a different perspective of group-think. The agreement measure is also extended to an agreement dis- tribution and used to calculate a mathematical distance be- tween two separate agreement distributions. With these measures it is easy to calculate the proximity of agreement between two or more groups of stakeholders. This measure is then applied to requirements analysis.

[1]  Mark J. Wierman,et al.  RANKING ORDINAL SCALES USING THE CONSENSUS MEASURE , 2005 .

[2]  M. J. Wierman,et al.  Placing the Dissonance Measure in the Context of Generalized Information Theory , 2006, NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society.

[3]  M. J. Wierman,et al.  Consensus and dissention: theory and properties , 2005, NAFIPS 2005.

[4]  W.J. Tastle,et al.  Consensus and dissention: a new measure of agreement , 2005, NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society.

[5]  Alan R. Dennis.  Systems Analysis and Design , 1995 .

[6]  Robert A. Saldarini.  Analysis and Design of Business Information Systems , 1990 .

[7]  M. J. Wierman,et al.  An information theoretic measure for the evaluation of ordinal scale data , 2006, Behavior research methods.

[8]  S. Jamieson.  Likert scales: how to (ab)use them , 2004, Medical education.

[9]  L. Benedictis,et al.  Economists' Views About the Economy: Evidence from a Survey of Italian Economists , 2011 .

[10]  Olivier Toubia.  Idea Generation, Creativity, and Incentives , 2006 .

[11]  M. Arbib,et al.  A Category-Theoretic Approach to Systems in a Fuzzy World , 1975 .

[12]  Hadi Nasseri.  Fuzzy Numbers: Positive and Nonnegative , 2008 .

[13]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[14]  R. Leon Price,et al.  Information Systems Solutions: A Project Approach , 2005 .

[15]  Mark J. Wierman,et al.  Consensus and Dissention: a Measure of Ordinal Dispersion , 2022 .

[16]  W.J. Tastle,et al.  Adjusting the Consensus Measure to Target Ordinal Scale Arguments , 2006, NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society.

[17]  S S Stevens,et al.  On the Theory of Scales of Measurement. , 1946, Science.

[18]  Leonard R. Sussman,et al.  Nominal, Ordinal, Interval, and Ratio Typologies are Misleading , 1993 .

[19]  W. Tastle.  Measuring Faculty Instructional Performance , 2009 .

[20]  Pedro Antunes,et al.  The use of genre analysis in the design of electronic meeting systems , 2006, Inf. Res..

[21]  Roelof K. Brouwer.  Fuzzy Clustering of Feature Vectors with Some Ordinal Valued Attributes Using Gradient Descent for Learning , 2008, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[22]  James D. Arthur,et al.  Problem Identification and Decomposition within the Requirements Generation Process , 2002 .

[23]  Robert A. Fleck,et al.  A cross-disciplinary model for improved information systems analysis , 2000, Ind. Manag. Data Syst..