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How collinearity affects mixture regression results

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  • Jan-Michael Becker
  • Christian Ringle
  • Marko Sarstedt
  • Franziska Völckner

Abstract

Mixture regression models are an important method for uncovering unobserved heterogeneity. A fundamental challenge in their application relates to the identification of the appropriate number of segments to retain from the data. Prior research has provided several simulation studies that compare the performance of different segment retention criteria. Although collinearity between the predictor variables is a common phenomenon in regression models, its effect on the performance of these criteria has not been analyzed thus far. We address this gap in research by examining the performance of segment retention criteria in mixture regression models characterized by systematically increased collinearity levels. The results have fundamental implications and provide guidance for using mixture regression models in empirical (marketing) studies. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Jan-Michael Becker & Christian Ringle & Marko Sarstedt & Franziska Völckner, 2015. "How collinearity affects mixture regression results," Marketing Letters, Springer, vol. 26(4), pages 643-659, December.
    • Handle: RePEc:kap:mktlet:v:26:y:2015:i:4:p:643-659
    DOI: 10.1007/s11002-014-9299-9
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    References listed on IDEAS

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    1. Bernard Dubois & Sandor Czellar & Gilles Laurent, 2005. "Consumer Segments Based on Attitudes Toward Luxury: Empirical Evidence from Twenty Countries," Marketing Letters, Springer, vol. 16(2), pages 115-128, April.
    2. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    3. Wayne DeSarbo & William Cron, 1988. "A maximum likelihood methodology for clusterwise linear regression," Journal of Classification, Springer;The Classification Society, vol. 5(2), pages 249-282, September.
    4. Sandor Czellar & Gilles Laurent & Bernard Dubois, 2005. "Consumer Segments Based on Attitudes Toward Luxury: Empirical Evidence from Twenty Countries," Post-Print hal-00458399, HAL.
    5. Hawkins, Dollena S. & Allen, David M. & Stromberg, Arnold J., 2001. "Determining the number of components in mixtures of linear models," Computational Statistics & Data Analysis, Elsevier, vol. 38(1), pages 15-48, November.
    6. Marko Sarstedt & Christian Ringle, 2010. "Treating unobserved heterogeneity in PLS path modeling: a comparison of FIMIX-PLS with different data analysis strategies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(8), pages 1299-1318.
    7. Carsten Hahn & Michael D. Johnson & Andreas Herrmann & Frank Huber, 2002. "Capturing Customer Heterogeneity Using A Finite Mixture Pls Approach," Schmalenbach Business Review (sbr), LMU Munich School of Management, vol. 54(3), pages 243-269, July.
    8. Gerda Claeskens & Jeffrey Hart, 2009. "Rejoinder on: Goodness-of-fit tests in mixed models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(2), pages 265-270, August.
    9. Kamel Jedidi & Harsharanjeet S. Jagpal & Wayne S. DeSarbo, 1997. "Finite-Mixture Structural Equation Models for Response-Based Segmentation and Unobserved Heterogeneity," Marketing Science, INFORMS, vol. 16(1), pages 39-59.
    10. Grün, Bettina & Leisch, Friedrich, 2008. "FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i04).
    11. Grewal, Rajdeep & Chakravarty, Anindita & Ding, Min & Liechty, John, 2008. "Counting chickens before the eggs hatch: Associating new product development portfolios with shareholder expectations in the pharmaceutical sector," International Journal of Research in Marketing, Elsevier, vol. 25(4), pages 261-272.
    12. Hutchinson, J Wesley & Kamakura, Wagner A & Lynch, John G, Jr, 2000. "Unobserved Heterogeneity as an Alternative Explanation for "Reversal" Effects in Behavioral Research," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 27(3), pages 324-344, December.
    13. Rajdeep Grewal & Joseph A. Cote & Hans Baumgartner, 2004. "Multicollinearity and Measurement Error in Structural Equation Models: Implications for Theory Testing," Marketing Science, INFORMS, vol. 23(4), pages 519-529, June.
    14. Gerda Claeskens & Jeffrey Hart, 2009. "Goodness-of-fit tests in mixed models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(2), pages 213-239, August.
    15. Cortiñas, Mónica & Chocarro, Raquel & Villanueva, María Luisa, 2010. "Understanding multi-channel banking customers," Journal of Business Research, Elsevier, vol. 63(11), pages 1215-1221, November.
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