IDEAS home Printed from https://ideas.repec.org/a/spr/advdac/v16y2022i4d10.1007_s11634-021-00469-0.html
   My bibliography  Save this article

Quantile composite-based path modeling: algorithms, properties and applications

Author

Listed:
  • Pasquale Dolce

    (University of Naples Federico II)

  • Cristina Davino

    (University of Naples Federico II)

  • Domenico Vistocco

    (University of Naples Federico II)

Abstract

Composite-based path modeling aims to study the relationships among a set of constructs, that is a representation of theoretical concepts. Such constructs are operationalized as composites (i.e. linear combinations of observed or manifest variables). The traditional partial least squares approach to composite-based path modeling focuses on the conditional means of the response distributions, being based on ordinary least squares regressions. Several are the cases where limiting to the mean could not reveal interesting effects at other locations of the outcome variables. Among these: when response variables are highly skewed, distributions have heavy tails and the analysis is concerned also about the tail part, heteroscedastic variances of the errors is present, distributions are characterized by outliers and other extreme data. In such cases, the quantile approach to path modeling is a valuable tool to complement the traditional approach, analyzing the entire distribution of outcome variables. Previous research has already shown the benefits of Quantile Composite-based Path Modeling but the methodological properties of the method have never been investigated. This paper offers a complete description of Quantile Composite-based Path Modeling, illustrating in details the method, the algorithms, the partial optimization criteria along with the machinery for validating and assessing the models. The asymptotic properties of the method are investigated through a simulation study. Moreover, an application on chronic kidney disease in diabetic patients is used to provide guidelines for the interpretation of results and to show the potentialities of the method to detect heterogeneity in the variable relationships.

Suggested Citation

  • Pasquale Dolce & Cristina Davino & Domenico Vistocco, 2022. "Quantile composite-based path modeling: algorithms, properties and applications," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(4), pages 909-949, December.
    • Handle: RePEc:spr:advdac:v:16:y:2022:i:4:d:10.1007_s11634-021-00469-0
    DOI: 10.1007/s11634-021-00469-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11634-021-00469-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11634-021-00469-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pasquale Dolce & Natale Lauro, 2015. "Comparing maximum likelihood and PLS estimates for structural equation modeling with formative blocks," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 891-902, May.
    2. TENENHAUS, Michel, 2008. "Component-based structural equation modelling," HEC Research Papers Series 887, HEC Paris.
    3. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    4. Allen Fleishman, 1978. "A method for simulating non-normal distributions," Psychometrika, Springer;The Psychometric Society, vol. 43(4), pages 521-532, December.
    5. Hair, Joe F. & Howard, Matt C. & Nitzl, Christian, 2020. "Assessing measurement model quality in PLS-SEM using confirmatory composite analysis," Journal of Business Research, Elsevier, vol. 109(C), pages 101-110.
    6. Rainer Schlittgen & Marko Sarstedt & Christian M. Ringle, 2020. "Data generation for composite-based structural equation modeling methods," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(4), pages 747-757, December.
    7. Koenker, Roger & Bassett, Gilbert, Jr, 1982. "Robust Tests for Heteroscedasticity Based on Regression Quantiles," Econometrica, Econometric Society, vol. 50(1), pages 43-61, January.
    8. Giuseppe Lamberti & Tomas Banet Aluja & Gaston Sanchez, 2016. "The Pathmox approach for PLS path modeling segmentation," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 32(4), pages 453-468, July.
    9. C. Vale & Vincent Maurelli, 1983. "Simulating multivariate nonnormal distributions," Psychometrika, Springer;The Psychometric Society, vol. 48(3), pages 465-471, September.
    10. Michel Tenenhaus, 2008. "Component-based Structural Equation Modelling," Working Papers hal-00580149, HAL.
    11. Cristina Davino & Vincenzo Esposito Vinzi, 2016. "Quantile composite-based path modeling," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(4), pages 491-520, December.
    12. Chao Xu & Jinchuan Ke & Xiaojun Zhao & Xiaofang Zhao, 2020. "Multiscale Quantile Correlation Coefficient: Measuring Tail Dependence of Financial Time Series," Sustainability, MDPI, vol. 12(12), pages 1-24, June.
    13. Roger W. Koenker & Vasco D'Orey, 1987. "Computing Regression Quantiles," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(3), pages 383-393, November.
    14. Tenenhaus, Michel & Vinzi, Vincenzo Esposito & Chatelin, Yves-Marie & Lauro, Carlo, 2005. "PLS path modeling," Computational Statistics & Data Analysis, Elsevier, vol. 48(1), pages 159-205, January.
    15. Cristina Davino & Pasquale Dolce & Stefania Taralli & Vincenzo Esposito Vinzi, 2018. "A Quantile Composite-Indicator Approach for the Measurement of Equitable and Sustainable Well-Being: A Case Study of the Italian Provinces," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 136(3), pages 999-1029, April.
    16. He X. & Zhu L-X., 2003. "A Lack-of-Fit Test for Quantile Regression," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 1013-1022, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gyeongcheol Cho & Heungsun Hwang & Marko Sarstedt & Christian M. Ringle, 2020. "Cutoff criteria for overall model fit indexes in generalized structured component analysis," Journal of Marketing Analytics, Palgrave Macmillan, vol. 8(4), pages 189-202, December.
    2. Cristina Davino & Pasquale Dolce & Stefania Taralli & Vincenzo Esposito Vinzi, 2018. "A Quantile Composite-Indicator Approach for the Measurement of Equitable and Sustainable Well-Being: A Case Study of the Italian Provinces," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 136(3), pages 999-1029, April.
    3. Cristina Davino & Pasquale Dolce & Stefania Taralli & Domenico Vistocco, 2022. "Composite-Based Path Modeling for Conditional Quantiles Prediction. An Application to Assess Health Differences at Local Level in a Well-Being Perspective," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 161(2), pages 907-936, June.
    4. Hao Cheng, 2023. "Composite quantile estimation in PLS-SEM for environment sustainable development evaluation," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 6249-6268, July.
    5. Immacolata Di Napoli & Pasquale Dolce & Caterina Arcidiacono, 2019. "Community Trust: A Social Indicator Related to Community Engagement," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 145(2), pages 551-579, September.
    6. Basco, Rodrigo & Hair, Joseph F. & Ringle, Christian M. & Sarstedt, Marko, 2022. "Advancing family business research through modeling nonlinear relationships: Comparing PLS-SEM and multiple regression," Journal of Family Business Strategy, Elsevier, vol. 13(3).
    7. Florian Schuberth & Jörg Henseler & Theo K. Dijkstra, 2018. "Partial least squares path modeling using ordinal categorical indicators," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(1), pages 9-35, January.
    8. Naifar, Nader & Hammoudeh, Shawkat, 2016. "Do global financial distress and uncertainties impact GCC and global sukuk return dynamics?," Pacific-Basin Finance Journal, Elsevier, vol. 39(C), pages 57-69.
    9. Sener, Abdurrezzak & Barut, Mehmet & Oztekin, Asil & Avcilar, Mutlu Yuksel & Yildirim, Mehmet Bayram, 2019. "The role of information usage in a retail supply chain: A causal data mining and analytical modeling approach," Journal of Business Research, Elsevier, vol. 99(C), pages 87-104.
    10. Michel Tenenhaus & Arthur Tenenhaus & Patrick J. F. Groenen, 2017. "Regularized Generalized Canonical Correlation Analysis: A Framework for Sequential Multiblock Component Methods," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 737-777, September.
    11. Joseph F. Hair & G. Tomas M. Hult & Christian M. Ringle & Marko Sarstedt & Kai Oliver Thiele, 2017. "Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods," Journal of the Academy of Marketing Science, Springer, vol. 45(5), pages 616-632, September.
    12. Zhu, Min, 2013. "Return distribution predictability and its implications for portfolio selection," International Review of Economics & Finance, Elsevier, vol. 27(C), pages 209-223.
    13. Arthur Tenenhaus & Michel Tenenhaus, 2011. "Regularized Generalized Canonical Correlation Analysis," Psychometrika, Springer;The Psychometric Society, vol. 76(2), pages 257-284, April.
    14. Karin Schermelleh-Engel & Christina Werner & Andreas Klein & Helfried Moosbrugger, 2010. "Nonlinear structural equation modeling: is partial least squares an alternative?," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 94(2), pages 167-184, June.
    15. Chevapatrakul, Thanaset, 2015. "Monetary environments and stock returns: International evidence based on the quantile regression technique," International Review of Financial Analysis, Elsevier, vol. 38(C), pages 83-108.
    16. Cristina Gimenez & Vicenta Sierra, 2013. "Sustainable Supply Chains: Governance Mechanisms to Greening Suppliers," Journal of Business Ethics, Springer, vol. 116(1), pages 189-203, August.
    17. Hao Cheng, 2023. "Environmental Effect Evaluation: A Quantile-Type Path-Modeling Approach," Sustainability, MDPI, vol. 15(5), pages 1-21, March.
    18. Yun-Xia Liu & Chun-Kun Pang & Yanxun Liu & Xiu-Bin Sun & Xin-Xu Li & Shi-Wen Jiang & Fuzhong Xue, 2015. "Association between Multidrug-Resistant Tuberculosis and Risk Factors in China: Applying Partial Least Squares Path Modeling," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-13, May.
    19. Fernando Gimeno-Arias & José Manuel Santos-Jaén & Mercedes Palacios-Manzano & Héctor Horacio Garza-Sánchez, 2021. "Using PLS-SEM to Analyze the Effect of CSR on Corporate Performance: The Mediating Role of Human Resources Management and Customer Satisfaction. An Empirical Study in the Spanish Food and Beverage Man," Mathematics, MDPI, vol. 9(22), pages 1-21, November.
    20. Love Westin & Jacob Hallencreutz & Johan Parmler, 2022. "Sustainable Development as a Driver for Customer Experience," Sustainability, MDPI, vol. 14(6), pages 1-18, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:advdac:v:16:y:2022:i:4:d:10.1007_s11634-021-00469-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.