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Structural Analysis of Models with Composite Dependent Variables

Author

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  • Paul W. Farris

    (University of Virginia)

  • Mark E. Parry

    (University of Virginia)

  • Kusum L. Ailawadi

    (University of Virginia)

Abstract

Composite variables are those that may be mathematically decomposed into additive and/or multiplicative component variables. Several researchers have noted that the relationship between a composite variable and its components may be a mathematical artifact, but the effect of their inclusion as independent variables on the coefficients of the remaining variables in the model has not been recognized, nor has a formal expression for the resulting bias been presented. Structural analysis of composite dependent variables, as presented here, provides the key to understanding the nature and extent of this bias. It separates higher and lower level components from one another and also separates these components from other antecedent variables in the model. The advantages of this hierarchical decomposition are that (1) it reduces problems of misspecification and omitted variables, (2) by separately estimating antecedent effects on each component, it offers some insights into the underlying causal mechanisms that are not available from other techniques and (3) it ensures that regression coefficients can be interpreted in the standard way: the expected change in the dependent variable associated with a change in the independent variable, holding other independent variables in the equation constant. Moreover, hierarchical decomposition of the dependent variable can reproduce all information available from techniques that mix levels of analysis, but the converse is not true.

Suggested Citation

  • Paul W. Farris & Mark E. Parry & Kusum L. Ailawadi, 1992. "Structural Analysis of Models with Composite Dependent Variables," Marketing Science, INFORMS, vol. 11(1), pages 76-94.
    • Handle: RePEc:inm:ormksc:v:11:y:1992:i:1:p:76-94
    DOI: 10.1287/mksc.11.1.76
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    Cited by:

    1. Putsis, William Jr. & Dhar, Ravi, 2001. "An empirical analysis of the determinants of category expenditure," Journal of Business Research, Elsevier, vol. 52(3), pages 277-291, June.
    2. William P. Putsis Jr. & Ronald W. Cotterill, 1999. "Share, price and category expenditure-geographic market effects and private labels," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 20(4), pages 175-187.
    3. You, Wen & Davis, George C. & Nayga, Rodolfo M., Jr. & McIntosh, Alex, 2005. "Parental Time and Children's Obesity Measures," 2005 Annual meeting, July 24-27, Providence, RI 19386, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    4. Buzzell, Robert D., 2004. "The PIMS program of strategy research: A retrospective appraisal," Journal of Business Research, Elsevier, vol. 57(5), pages 478-483, May.
    5. Annacker, Dirk & Hildebrandt, Lutz, 2002. "Unobservable effects in structural models of business performance," SFB 373 Discussion Papers 2002,22, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    6. Plewa, Carolin, 2009. "Exploring organizational culture difference in relationship dyads," Australasian marketing journal, Elsevier, vol. 17(1), pages 46-57.
    7. Annacker, Dirk & Hildebrandt, Lutz, 2004. "Unobservable effects in structural models of business performance," Journal of Business Research, Elsevier, vol. 57(5), pages 507-517, May.
    8. Christian Fieberg & Kerstin Lopatta & Thomas Tammen & Sebastian A. Tideman, 2021. "Political affinity and investors' response to the acquisition premium in cross‐border M&A transactions — A moderation analysis," Strategic Management Journal, Wiley Blackwell, vol. 42(13), pages 2477-2492, December.

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    Keywords

    marketing; structural analysis;

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