University of Alberta School of Business
Marketing 710: Research Methodology in Marketing
Instructor: Adam Finn (Banister Professor of Marketing) Fall 2010
Phone: 780 492-5369 WWW: www.bus.ualberta.ca/afinn/
Fax: 780 492-3325 E-mail: [email protected]
Office hours: Drop in or by appointment to 3:40L-1 SoB
Note: Policy about course outlines can be found in Section 23.4(2) of the University Calendar.
Course Overview
The purpose of this seminar is to provide an introduction to three methodological perspectives that are important to the conduct of academic research in marketing. The first comes from the
philosophy of science. The second is the application of Structural Equation Modeling (SEM) (also called Covariance Structure Analysis - CSA) in marketing. This has arisen out of a convergence of econometrics, psychometrics, and path analysis. The third is a recognition of the multifaceted nature of marketing data, creating value of a generalizability theory perspective in measurement and a multilevel approach to theory testing. The objectives of the course are to enable the student to understand and begin to use these tools and perspectives to develop and test concepts, propositions and theories in marketing, and to evaluate methods used in research papers published in the
marketing literature.
Prerequisites
This course is a doctoral level course that assumes students have (or are going to obtain) a more comprehensive introduction to traditional Marketing Research topics (e.g., use of secondary data, primary data collection, etc.) and a more general grounding in Multivariate Statistics (e.g., MGTSC 705 and 706).
Major Paper: 30% of Grade
Each student will be required to write a research paper. The paper must conform to the format and style requirements of the Journal of Marketing and Journal of Marketing Research. While there is no formal length requirement, most research papers are about 20 - 25 pages of double spaced typing.
Papers of over 30 pages are usually poorly focused or need editing. The paper submitted at the end of the semester should be a final draft, i.e., tightly written and edited. Late papers will incur a ten percent penalty per day. Success in academia requires an ability to meet deadlines, even though academics always have numerous pressing commitments. Some class time will be available to first propose a paper topic and to later present what has been achieved. Appropriate papers could:
1. develop and justify an original theory (model) of a particular marketing phenomena.
2. critically respond to a recently published contribution to the marketing literature.
3. conduct an analysis or reanalysis of previously collected or published research data, taking a more rigorous theoretical or measurement perspective.
4. develop, adapt or re-evaluate a marketing scale for use in a new or more specific context.
5. conduct a rigorous evaluation of the development of an important marketing concept, marketing theory, or marketing practice, or evaluate the work of a important marketing theorist or pioneer.
6. examine the use (or misuse) of a specific research method in the marketing literature.
If you have identified an area of research specialization, you are advised to tackle a paper that complements that area of interest.
Short Literature Evaluations: 15% of Grade
Each student will be required to write and present a critique of a research article drawn from the marketing literature. These short papers (two double-spaced pages) are designed to develop your ability to review academic papers. This work should identify the key contribution(s) made by the paper and then present a critical evaluation of the contribution(s): its strengths and any important weaknesses.
Analytical Assignments: 15% of Grade
These analytical assignments are designed to develop your facility with the practical aspects of using a SEM/CSA estimation program. The major software programs vary in capabilities with respect to missing data, non-continuous data, multi-level capabilities, bootstrapping, etc and graphical interfaces:
LISREL: Developer: Joreskog Originally the dominant program, used in almost all papers published in the 1980s. Used an eight-matrix model syntax. Now includes PRELIS, a preprocessor for
computing input covariance and correlation matrices, etc, and SIMPLIS, a version of LISREL that uses a simpler form of model specification. Improved interface. Current version 8.8 for Windows includes additional multilevel modules. Available for US $495;
Free student edition for up to 15 variables. http://www.ssicentral.com/lisrel/downloads.html Rental versions ($75 US for 6 months, $130 for 12 months) are also available.
EQS: Developer: Bentler Was the second most popular program. Uses the Bentler-Weeks simultaneous regression equation approach to model specification. Current version 6.1 (demo) Available for US $195 (student version) or US $595 (academic) from www.mvsoft.com
AMOS: Developer Arbuckle and Smallwaters. Now only available through IBM SPSS. Provides a graphical user interface for modeling. Current version 18
U of A annual site licensed for $200.
MPlus: Developer: Muthén Newer more versatile program for conventional, multilevel and mixture modeling of continuous and categorical variables. Current version 6.0
Student version available for US $195 for base modeling. US $595 at demo version is limited to 2 independent and 6 dependent variables.
Sem in R: Free, but limited capability when reviewed by Fox (2006) in SEM 13(3).
SmartPLS: Software for (graphical) path modeling using the partial least squares (PLS)-method.
Class Participation: 10% of Grade
Contribution to the discussion of papers, marketing issues and questions from Hunt.
Final Exam: 30% of Grade
This exam provides an opportunity to answer questions like those that could be asked in a
comprehensive exam under similar exam conditions. It will require answering two out of three (or more) essay questions in a two-hour exam. The questions will be aimed at assessing your ability to evaluate and contribute to theory development in marketing.
Academic Integrity:
The University of Alberta is committed to the highest standards of academic integrity and honesty.
Students are expected to be familiar with these standards regarding academic honesty and to uphold the policies of the University in this respect. Students are urged to familiarize themselves with the provisions of the Code of Student Behaviour (online at avoid any behaviour which could potentially result in suspicions of cheating, plagiarism,
misrepresentation of facts and/or participation in an offence. Academic dishonesty is a serious offence and can result in suspension or expulsion from the University.” (GFC 29 SEP 2003)
Required Text:
Hunt, Shelby D. (2010), Marketing Theory: Foundations, Controversy, Strategy, Resource- Advantage Theory. M.E. Sharpe. (Hunt)
Other Useful Sources: Some are useful additions to a marketing academic's personal library.
A. SEM Textbooks
Hayduk, Leslie A. (1987), Structural Equation Modeling with LISREL. Baltimore: John Hopkins.
Textbook assuming very little background, but with an emphasis on path models. (Hay1) Loehlin, John (2004), Latent Variable Models. Fourth Edition. LEA Another good introduction
Raykov, Tenko and George Marcoulides (2006) A First Course in Structural Equation Modeling. 2nd Ed. LEA. (Intro with examples of program syntax for LISREL, EQS and MPlus)
Schumacker, Randall E. and Richard G. Lomax (2010) A Beginners Guide to Structural Equation Modeling 3rd Edition Routledge Intro with syntax for LISREL 8.8
Other Methods Textbooks
Brennan, Robert (2001), Generalizability Theory. New York: Springer-Verlag Comprehensive presentation of univariate and multivariate generalizability theory (Has associated free software)
Cardinet, Jean, Sandra Johnson & Gianreto Pini (2010) “ Applying Generalizability Thoery using EduG” Routledge. (Free software).
Hambleton, Ronald K, et al (1991), Fundamentals of Item Response Theory. Sage. Introduction.
Hox, Joop J. (2010) Multilevel Analysis: Techniques and Applications. Routledge.
Jaccard, James & Jacob Jacoby (2010), Theory Construction and Model-Building Skills. Guilford Press. Tools to assist in the practical process of constructing social science theories.
Monographs:
Bollen, Kenneth A. (1989), Structural Equations with Latent Variables. New York: Wiley-
Interscience. Comprehensive text with methodological emphasis. (Bol) (Reportedly being revised)
Bollen Kenneth A and Patrick J. Curran (2006) Latent Curve Models: A Structural Equation Perspective. New York: Wiley. Models of trajectories for longitudinal data.
Hayduck, Leslie A (1997), LISREL: Issues, Debates and Strategies. John Hopkins. An update on Leslie’s (U of A Sociology Prof.) unique perspective on use of SEM methods. (Hay2)
Hunter, John & Frank Schmidt (2004) Methods of Meta-Analysis. 2nd Ed. Sage. Useful textbook.
McDonald, Roderick (1999), Test Theory: A Unified Treatment. LEA An integrated treatment of test theory topics through the perspective of the nonlinear common factor model.
Mitchell, Joel (1999). Measurement in Psychology. Cambridge University Press. Philosophy of science based critique of the mainstream (Stevens) approach to measurement in the social sciences.
Mulaik, Stanley (2009) Linear Causal Modeling with Structural Equations. CRC Press.
Netemeyer, Richard G., William O. Bearden and Subash Sharma (2003) Scaling Procedures: Issues and Applications. Sage. Update of the traditional marketing (Churchill) scale development paradigm.
Pearl, Judea (2009), Causality: Models, Reasoning, and Inference. Second Edition. Cambridge University Press. Unifies approaches to causation and offers tools for studying the relationships between causal connections and statistical associations.
Rossiter, John (2010), Measurement for the Social Sciences: The C-OAR-SE Method and Why It Must Replace Psychometrics. Springer (forthcoming).
Saltzberger, Thomas (2009) Measurement in Marketing Research. Edward Elgar. Promotes the use of the Rasch model/IRT modeling for marketing measurement.
Suppe, Frederick (1977), The Structure of Scientific Theories Second Edition. Urbana: University of Illinois Press. Introduction & Afterward to a 1969 Symposium provide a comprehensive introduction to philosophy of science.
B. Collections
Denzin, Norman and Yvonna Lincoln (Eds.) (2005), Handbook of Qualitative Research. 3rd Edition Thousand Oaks, CA: Sage Paradigms, perspectives & methods of collection and interpretation. (DL)
Eid, Michael and Ed Diener (Eds.) (2006) Handbook of Multimethod Measurement in Psychology.
Sage
Grover, Rajiv and Marco Vriens (Eds.) (2006) Handbook of Marketing Research. Sage. Reviews of the broader marketing research field.
Hancock, Gregory and Ralph Mueller (2006) Structural Equation Modeling: A Second Course.
Information Age. Review articles for many advanced topics.
Kaplan, David (Ed.) The Sage Handbook of Quantitative Methodology for the Social Sciences.
Marcoulides, George A and Randall E Shumacker (Eds.) (2001), New Developments and
Techniques in Structural Equation Modeling: Issues and Techniques. Hillsdale, NJ: LEA. Review papers for more advanced topics. (MS)
Shumacker, Randall E and George A Marcoulides (Eds.) (1998), Interaction and Nonlinear Effects in Structural Equation Modeling. Hillsdale, NJ: LEA. Survey of alternative approaches. (SM)
Peter, J. Paul and Michael L. Ray (Eds.) (1984), Measurement Readings for Marketing Research.
Chicago: American Marketing Association. Classic psychometric and marketing papers. (PR)
Esposito Vinzi et al (eds.) Handbook of Partial Least Squares. Springer Handbooks of Computational Statistics Springer-Verlag, (V)
C. Marketing Scales
Bearden, William O. et al. (1999) Handbook of Marketing Scales 2nd Edition. Newbury Park: Sage Summaries of scale development and validation publications in marketing and consumer behavior.
Bruner, Gordon C. II and Paul Hensel (various), Marketing Scales Handbook V1 to V5. Periodic compilations of the multi-item scales used in consumer behavior or marketing. See Bruner’s website:
www.siu.edu/departments/coba/osr/
Joint Committee of AERA, APA, NCME (1999) Standards for Educational and Psychological Testing. Current manual for test development and testing practice.
D. Important Marketing and/or Methodology Journals Periodicals:
IJRM Int. Journal of Research in Marketing JAMS Journal of Academy of Marketing Science JCR Journal of Consumer Research JM Journal of Marketing
JME Journal of Marketing Education JMR Journal of Marketing Research JR Journal of Retailing MBR Multivariate Behavioral Research MkSc Marketing Science MT Marketing Theory
ORM Organizational Res. Methods PM Psychological Methods P Psychometrika RinM Research in Marketing:
SEM Structural Equation Modeling SMR Sociological Methods & Research
E. Selected Electronic Sources
www.gsu.edu/~mkteer/semfaq.html (Ed Rigdon’s site for SEMNET FAQs)
http://www.msi.org/pdf/MSI_RP10-12.pdf Marketing Science Institute list of priority research topics for 2010-2012. Note they also provide research grant support.
http://www.docsig.org/ AMA Doctoral Students Special Interest Group site with numerous links.
Reading List and Weekly Course Schedule Note: * A paper available for critique.
Session 1: Introduction to the Course/Academic Marketing Research
Sirgy, Joseph, JS Johar & Tao Gao (2006). “Towards a Code of Ethics for Marketing Educators”
Journal of Business Ethics, 63 (January),
Summers, John O. (2001), “Guidelines for Conducting Research and Publishing in Marketing: From Conceptualization Through the Review Process,” JAMS, (Fall), 405-15.
Seggie, Steven and David Griffith (2009), “What Does It Take to Get Promoted in Marketing Academia?” JM 73 (January), 122-132.
Reibstein, David J., George Day and Jerry Wind (2009), “Is Marketing Academia Losing Its Way?”
JM 73 (July) 1-3.
Session 2: Introduction to Structural Equation Modeling: CVA and PLS
Rigdon (1998) “Structural Equation Modeling” and Chin (1998) “The Partial Least Squares Approach to Structural Equation Modeling” both in Marcoulides, George A. (Ed.) (1998) Modern Methods for Business Research. Mahwah NJ LEA.
Useful follow-up source CVA: Raykov Ch. 1, 2 Useful follow-up source PLS
Esposito Vinzi, Vincenzo, Laura Trinchera and Silvano Amoto (2010) “PLS Modeling: From Foundations to Recent Developments and Open Issues for Model Assessment and Improvement” V 47-82.
Session 3: Domain of Marketing Theory Assignment 1 Hunt: Ch.1, 2
Shaw, Eric & Brian Jones (2005) “A History of Schools of Marketing Thought,” MT 5 (Sept) 239- 281.
Wilkie, William & Elizabeth Moore (2003) “Scholarly Research in Marketing: Exploring the “4 Eras” of Thought Development” J Public Policy & Marketing 22 (Fall) 116-146.
Armstrong, Scott (2002), “Discovery & Communication of Important Marketing Findings: Evidence
& Proposal,” JBR at http://marketing.wharton.upenn.edu/people/faculty/armstrong/armstrong2.cfm
Baumgartner, Hans & Rik Pieters (2003), “Structural Influence of Marketing Journals: A Citation Analysis of the Discipline and Its Subareas Over Time” JM 67 (April), 123-139.
Useful follow-up sources: ZLH, Ch 1,2.
Lichtenthal, J David and Leland L. Beik (1984), "A History of the Definition of Marketing," RinM, 7, 133-163.
Bartels, Robert (1976), The History of Marketing Thought. 2nd Ed. Columbus, Oh.: Grid.
Session 4: Research Methods & Existing Knowledge Critique 1
Golder, Peter N. (2000) “Historical Method in Marketing Research with New Evidence on Long Term Market Share Stability,” JMR, 37 (May), 156-72.
Rosenthal, R. and M.R. DiMatteo (2001), "Meta Analysis: Recent Developments in Quantitative Methods for Literature Reviews," Annual Review of Psychology, 59-82.
Bonoma, Thomas V. (1985), "Case Research in Marketing: Opportunities, Problems, and a Process,"
JMR, 22 (May), 199-208.
Guba, Egon and Yvonna Lincoln (1994), “Competing Paradigms in Qualitative Research,” in DL.
Fern, Edward F. and Kent B. Monroe (1996), “Effect-Size Estimates: Issues and Problems in Interpretation,” JCR 23 (September), 89-105
* Rust, Roland T. et al (2004), “Measuring Marketing Productivity: Current Knowledge and Future Directions,” JM 68 (October), 76-89.
* Low, George S. and Ronald Fullerton (1994), “Brands, Brand Management and the Brand Manager
System: A Critical-Historical Evaluation,” JMR 31 (May), 173-190.
* Geyskins, Inge, Jan-Benedict Steenkamp and Nirmalys Kumar (1999), “A Meta-Analysis of Satisfaction in Marketing Channel Relationships,” JMR 36 (May), 223-238.
* Peterson, Robert (2001) “On the Use of College Students in Social Science Research: Insights from a Second-Order Meta-Analysis,” JCR 28 (Dec.), 450-461.
* Hollenbeck, Candice R, Cara Peters and George Zinkhan (2008) “Retail Spectacles and Brand Meaning: Insights from a Brand Museum Case Study,” JR 84 (3) 334-353.
* Narayandas, Das and V. Kasturi Rangan (2004) “Building and Sustaining Buyer-Seller Relationships in Mature Industrial Markets, JM 68 (July), 63-77.
Session 5: Measurement: CTT Reliability and Construct Validity Hunt: Ch. 3, 4
Churchill, Gilbert A. Jr. (1979), "A Paradigm for Developing Better Measures of Marketing Constructs," JMR 16 (Feb) 64-73 (also in PR). (Influence still seen in current marketing practice)
Peter, J. Paul (1979), "Reliability: A Review of Psychometric Basics and Recent Marketing Practices," JMR 16 (Feb.), 6-17 (also in PR)
Peter, J. Paul (1981), "Construct Validity: A Review of Basic Issues and Marketing Practices," JMR 18 (May.), 133-145 (also in PR)
Useful follow-up sources: PR, HDK Ch. 1-3, Bol Ch. 6
Cronbach, Lee J. and Paul E. Meehl (1955), “Construct Validity in Psychological Tests," PB 52 (July), 281-302 (also in PR). (Historic value)
Campbell, Donald T. and Donald W. Fiske (1959), "Convergent and Discriminant Validation by The Multitrait-Multimethod Matrix," PB, 56 (March), 100-122 (also in PR) (Historic value)
Session 6: Construct Validity: Marketing Applications Critique 2
Gerbing, David W. and James C. Anderson (1988), "An Updated Paradigm for Scale Development Incorporating Unidimensionality and Its Assessment," JMR 25 (May), 186-192.
Steenkamp, J-B. E. M. and H.C.M. van Trijp (1991), "The Use of LISREL in Validating Marketing Constructs," IJRM, 8 (November), 283-299.
Podsakoff, P.M. et al (2003) “Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies,” JAP 88 (5) 879-903.
* Churchill, Gilbert A. and J. Paul Peter (1984), "Research Design Effects on the Reliability of
Rating Scales: A Meta Analysis," JMR (Nov.), 360-375.
* Peter, J. Paul and Gilbert A Churchill, Jr. (1986), "Relationship Among Research Design Criteria and Psychometric Properties of Rating Scales: A Meta Analysis," JMR (Feb.), 1-10.
*Andrews, Frank M. (1984), "Construct Validity and Error Components of Survey Measures: A Structural Modeling Approach," Public Opinion Quarterly, 48 (2),
* Parasuraman, A., Valerie Zeithaml, and Leonard L. Berry (1988), "SERVQUAL: A Multiple-Item Scale for Measuring Customer Perceptions of Service Quality," JR, 64 (Spring), 12-40.
* Walsh, Gianfranco and Sharon E. Beatty (2007), “Customer-based Corporate Reputation of a Service Firm: Scale Development and Validation,” JAMS 35 (Spring), 127-143
* Seiders, Kathleen, et al (2007), “SERVCON: Development and Validation of a Multidimensional Service Convenience Scale,” JAMS 35 (Spring), 144-156.
Session 7: Reliability Reconsidered: Generalizability Theory Assignment 2
Rentz, Joseph O. (1987), "Generalizability Theory: A Comprehensive Method for Assessing and Improving the Dependability of Marketing Measures," JMR, 24 (Feb.), 19-28
Finn, Adam and Ujwal Kayandé (1997), “Reliability Assessment and Optimization of Marketing Measurement,” JMR, 34 (May), 262-275.
Finn, Adam and Ujwal Kayande (2004) “Scale Modification: Alternative Approaches and Their Consequences” JR. 80 (Jan.) 37-52.
Other useful sources
Shavelson, Richard J. and Noreen M. Webb (1991), Generalizability Theory: A Primer. Newbury Park: Sage. Brief introduction to the use of univariate G-theory.
Brennan, Robert (2001), Generalizability Theory. (Ch 1-8). New York: Springer-Verlag More detailed presentation of univariate G-theory concepts.
Rust, Roland and Bruce Cooil (1994), “Reliability Measures for Qualitative Data: Theory and Implications,” JMR 31 (Feb), 1-14.
Session 8: Scientific Laws and Causal Relations Paper proposal Hunt: Ch. 5, 6
Judea Pearl (2010), “The Causal Foundations of Structural Equation Modeling” Tech Report R-370.
Available at: http://bayes.cs.ucla.edu/csl_papers.html
Scheines, R., et al. (1998), "The TETRAD Project: Constraint Based Aids to Causal Model Specification." Multivariate Behavioral Research 33: 65-117. (also see comments)
Freedman, David A. (2004), “On Specifying Graphical Models for Causation, and the Identification Problem,” Evaluation Review, 28 (August), 267-93.
Bullock, Heather E. et al (1994), “Causation Issues in Structural Equation Modeling Research,” SEM 1 (3), 253-267.
Evanschitzky, Helner, Carsten Baumgarth, Raymond Hubbard, & J. Scott Armstrong (2007),
“Replication Research’s Disturbing Trend,” JBR 60 (April), 411-415.
Useful follow-up sources: Bol. Ch. 3
Uncles, Mark amd Malcolm Wright (2004), “Empirical Generalisation in Marketing,” Australasian Marketing Journal, 14 (3) 5-18
Bass, Frank M. and Jerry Wind (1995), “Introduction to the Special Issue: Empirical Generalizations in Marketing,” and various papers in the special issue of Marketing Science 14, No 3 Part 2
Session 9: Theory: Nature and Development Hunt: Ch. 7
Bagozzi, Richard P. (1984), "A Prospectus for Theory Construction in Marketing," JM, 48 (Winter), 11-29.
Greenwald, Anthony G et al (1986), "Under What Conditions Does the Theory Obstruct Research Progress" Psychological Review, 93:2 216-229.
Borsboom, Denny, Gideon J. Mellenburgh and Jaap van Heerden (2003) “The Theoretical Status of Latent Variables,” Psychological Review, 110: 2 203-219.
Useful follow-up sources: JJ
Session 10: Theory: Evaluation Issues Hunt: Ch. 8, 13
Anderson, James C. and David W. Gerbing (1988), "Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach," PB, Vol. 103, No. 3, 411-423.
MacCallum, Robert C., Michael Browne and Hazuki M Sugawara (1996), “Power Analysis and Determination of Sample Size for Covariance Structure Modeling,” PM 1 (2), 130-149.
Follow-up: Modeling Approaches Debate
Fornell, Claes and Youjae Yi (1992), “Assumptions of the Two-Step Approach to Latent Variable Modeling,” SMR (Feb.), 291-320 (with comments & response)
Also see Hayduk/Mulaik debate and comments in SEM Vol 7, No. 1 2000) Follow-Up: Model Fit Issues
Hu & Bentler (1999) “Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria versus New Alternatives” SEM (6) 1-55.
Marsh, Hau and Wen (2004) In Search of Golden Rules: Comment . . . “ SEM 11 (3), Also see Paul Barrett and comments in Personality and Individual Differences 42 (2007).
Specialized applications: Experiments. Panels – Latent growth curve models, Non-linear relations
Bagozzi, Richard P. and Youjae Yi (1989), "On the Use of Structural Equation Models in Experimental Designs," JMR 26 (Aug.), 271-284.
Willett, J. B., & Sayer, A. G. (1996). “Cross-domain analyses of change over time: Combining growth modeling with covariance structure modeling.” MaS. 125-157.
Rigdon, Edward E. (1998) “A Comparative Review of Interaction and Nonlinear Modeling,” in SM
Session 11: Theory Testing: Applications Critique 3
Baumgartner, H and C. Homburg (1996), “Applications of Structural Equation Modeling in Marketing and Consumer Research: A Review,” IJRM 13 (April), 139-161.
Hulland J., Y.H. Chow and S. Lam (1996), “Use of Causal Models in Marketing Research: A Review,” IJRM 13 (April), 181-197.
McDonald, Roderick P. and Moon-Ho Ringo Ho (2002), “Principles and Practice in Reporting Structural Equation Analyses,” PM, 7 (1), 64-82.
Flora, D. B.; Curran, P. J. (2004), “An Empirical Evaluation of Alternative Methods of Estimation for Confirmatory Factor Analysis With Ordinal Data,” PM, 9 (4), 466-491.
*McFarland, Richard G., James M. Bloodgood and Janice M. Payan (2008) “Supply Chain Contagion,” JM 72 (March), 63-79.
*Wang, Liz C. et al (2007), “Can a Retail Web Site be Social?” JM 71 (July) 143-157.
*Narver, John C. and Stanley F. Slater (1990) "The Effect of a Market Orientation on Business Profitability," JM 54 (October), 20-35.
*Mentzer, John T. et al (2001), “Logistics Service Quality as a Segment-Customization Process,” JM 65 (October), 82-104.
*Chaudhuri, Arjun and Morris B. Holbrook (2001), “The Chain of Effects from Brand Trust and Brand Affect to Brand Performance: The Role of Brand Loyalty,” JM 65 (April), 81-93.
Session 12: Current Measurement Issues & Multifaceted Data Assignment 3
Diamantopoulus, Adamantios Petra Riefer and Katharina P. Roth (2008), “Advancing Formative Measurement Models,” JBR 61 (December), 1203-18.
Rossiter, John R. (2002) “The C-OAR-SE Procedure for Scale Development in Marketing,” IJRM 19 (Dec.) 305-335.
Diamantopoulos, Adamantios (2004) “The C-OAR-SE Procedure . . : A Comment” IJRM 22 (1).
Finn, Adam and Ujwal Kayande (2005) “How Fine is COARSE? A Generalizability Theory Perspective on Rossiter’s Procedure” IJRM, 22 (1), 11-21.
Eid, Michael, et al (2008) “SEM of Multitrait-Multimethod Data: Different Models for Different Types of Methods,” PM 13 (Sept), 230-253.
Wang, Luming and Adam Finn (2010), Consumer-based Brand Equity Measurement: Multi-facet Item Response Theory Perspective. Working Paper.
Useful follow-up sources:
Brennan, Robert (2001), Generalizability Theory. (Ch 9). New York: Springer-Verlag
Session 13: Theory Testing Reconsidered Paper Presentations Finn, Adam and Ujwal Kayande (2006) “The Generalizability of Marketing Theory Testing,”
Working paper.
