Statistical and judgmental criteria for scale purification

Purpose “Scale purification” – the process of eliminating items from multi-item scales – is widespread in empirical research, but studies that critically examine the implications of this process are scarce. The goals of this research are threefold: (1) to discuss the methodological underpinning of scale purification, (2) to critically analyze the current state of scale purification in supply chain management (SCM) research, and (3) to provide suggestions for advancing the scale purification process. Design/methodology/approach A framework for making scale purification decisions is developed and used to analyze and critically reflect on the application of scale purification in leading SCM journals. Findings This research highlights the need for rigorous scale purification decisions based on both statistical and judgmental criteria. By applying the proposed framework to the SCM discipline, a lack of methodological rigor and coherence is identified when it comes to current purification practices in empirical...

[1]  Roy Suddaby,et al.  Construct Clarity in Theories of Management and Organization , 2010 .

[2]  Susan Greener,et al.  Business Research Methods , 2008 .

[3]  Scott B. MacKenzie,et al.  Recommendations for Creating Better Concept Definitions in the Organizational, Behavioral, and Social Sciences , 2016 .

[4]  J. Dawes Do Data Characteristics Change According to the Number of Scale Points Used? An Experiment Using 5-Point, 7-Point and 10-Point Scales , 2008 .

[5]  W. Balzer,et al.  ISSUES AND STRATEGIES FOR REDUCING THE LENGTH OF SELF‐REPORT SCALES , 2002 .

[6]  Cheryl Burke Jarvis,et al.  A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research , 2003 .

[7]  James C. Anderson,et al.  STRUCTURAL EQUATION MODELING IN PRACTICE: A REVIEW AND RECOMMENDED TWO-STEP APPROACH , 1988 .

[8]  Radhika Puri,et al.  Measuring and Modifying Consumer Impulsiveness: A Cost-Benefit Accessibility Framework , 1996 .

[9]  Carl Marcus Wallenburg,et al.  Dealing with supply chain risks , 2012 .

[10]  M. Sarstedt,et al.  A new criterion for assessing discriminant validity in variance-based structural equation modeling , 2015 .

[11]  Terry L. Childers,et al.  Understanding mail survey response behavior: A meta-analysis. , 1991 .

[12]  Samuel B. Bacharach,et al.  Organizational Theories: Some Criteria for Evaluation , 1989 .

[13]  R. Bagozzi,et al.  On the evaluation of structural equation models , 1988 .

[14]  Izak Benbasat,et al.  Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation , 1991, Inf. Syst. Res..

[15]  T. Harris,et al.  Don’t Forget the Items , 2016 .

[16]  Maria Nieves Perez-Arostegui,et al.  The impact of ambidexterity on supply chain flexibility fit , 2016 .

[17]  J. Mentzer,et al.  DEVELOPING AND MEASURING SUPPLY CHAIN MANAGEMENT CONCEPTS , 2004 .

[18]  Klaas Sijtsma,et al.  Invited discussion of Cronbach (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297-334 (26,889 citations in Google Scholar as of 1/1/2016) , 2016 .

[19]  Jesús J. Cambra‐Fierro,et al.  Creating satisfaction in the demand‐supply chain: the buyers' perspective , 2008 .

[20]  Carl Marcus Wallenburg,et al.  Dealing with supply chain risks Linking risk management practices and strategies to performance , 2017 .

[21]  Baruch Nevo,et al.  Face validity revisited. , 1985 .

[22]  M. Hitt,et al.  Construct measurement in strategic management research: illusion or reality? , 2005 .

[23]  Kevin E. Voss,et al.  Measuring the Hedonic and Utilitarian Dimensions of Consumer Attitude , 2003 .

[24]  V. Daniel R. Guide,et al.  Notes from the Editors: Redefining some methodological criteria for the journal ☆ , 2015 .

[25]  John A. Johnson The Impact of Item Characteristics on Item and Scale Validity , 2004, Multivariate behavioral research.

[26]  Scott B. MacKenzie,et al.  Construct Measurement and Validation Procedures in MIS and Behavioral Research: Integrating New and Existing Techniques , 2011, MIS Q..

[27]  Markham T. Frohlich,et al.  e-Integration in the Supply Chain: Barriers and Performance , 2002, Decis. Sci..

[28]  R. Suddaby Editor's Comments: Construct Clarity in Theories of Management and Organization , 2010 .

[29]  W. Bearden,et al.  The use of expert judges in scale development: Implications for improving face validity of measures of unobservable constructs , 2004 .

[30]  K. G. Jöreskog,et al.  Statistical analysis of sets of congeneric tests , 1971 .

[31]  Peter Filzmoser,et al.  Exploratory factor analysis revisited: How robust methods support the detection of hidden multivariate data structures in IS research , 2010, Inf. Manag..

[32]  Stephan M. Wagner,et al.  Boundary Conditions: What They are, How to Explore Them, Why We Need Them, and When to Consider Them , 2015 .

[33]  R. Kline Principles and practice of structural equation modeling, 2nd ed. , 2005 .

[34]  Richard G. Netemeyer,et al.  Handbook of Marketing Scales , 1999 .

[35]  Gilbert A. Churchill A Paradigm for Developing Better Measures of Marketing Constructs , 1979 .

[36]  John R. Rossiter,et al.  The C-OAR-SE procedure for scale development in marketing , 2002 .

[37]  Marsha L. Richins The Material Values Scale: Measurement Properties and Development of a Short Form , 2004 .

[38]  J. Avery Critical review. , 2006, The Journal of the Arkansas Medical Society.

[39]  L. Cronbach Coefficient alpha and the internal structure of tests , 1951 .

[40]  C. H. Lawshe A QUANTITATIVE APPROACH TO CONTENT VALIDITY , 1975 .

[41]  Clay M. Voorhees,et al.  Discriminant validity testing in marketing: an analysis, causes for concern, and proposed remedies , 2016 .

[42]  J. Zaichkowsky The Personal Involvement Inventory: Reduction, Revision, and Application to Advertising , 1994 .

[43]  C. Fornell,et al.  Evaluating structural equation models with unobservable variables and measurement error. , 1981 .

[44]  R. Bagozzi An Examination Of The Validity Of Two Models Of Attitude. , 1981, Multivariate behavioral research.