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Articles

IPLSL and IPLSQ: Two types of imputation PLS algorithms for hierarchical latent variable model

Pages 2493-2513 | Received 10 Jul 2020, Accepted 09 Jul 2021, Published online: 08 Aug 2021

References

  • Becker, J. M., K. Klein, and M. Wetzels. 2012. Formative hierarchical latent variable models in PLS-SEM: Recommendations and guidelines. Long Range Planning 45 (5–6):359–94. doi:10.1016/j.lrp.2012.10.001.
  • Bollen, K. A. 1989. Structural equations with latent variables. New York: Wiley.
  • Chatelin, Y. M., V. V. Esposito, and M. Tenenhaus. 2002. State-of-art on PLS path modeling through the available software. http://www.hec.fr/Recherche/Cahiers-de-recherche/State-of-arton-PLS-Path-Modeling-through-the-available-software/.
  • Chen, X. R., T. K. A. Wan, and Y. Zhou. 2015. Efficient quantile regression analysis with missing observations. Journal of the American Statistical Association 110 (510):723–41. doi:10.1080/01621459.2014.928219.
  • Cheng, H. 2020. A class of new partial least square algorithms for first and higher order models. In Communications in Statistics - Simulation and Computation.
  • Cheng, H. 2019. An Application Research of inverse probability weighted multiple imputation method on factors of residents income in China. Statistics and Information Forum 7:26–34.
  • Cheng, H., and Y. Wei. 2018. A fast imputation algorithm in quantile regression. Computational Statistics 33 (4):1589–603. doi:10.1007/s00180-018-0813-z.
  • Ciavolino, E., and M. Nitti. 2013a. Simulation study for PLS path modeling with high-order construct: A job satisfaction model evidence. In Advanced dynamic modeling of economic and social systems, edited by A. N. Proto, M. Squillante, and J. Kacprzyk, 185–207. Berlin Heidelberg: Springer.
  • Ciavolino, E., and M. Nitti. 2013b. Using the hybrid two-step estimation approach for the identification of second-order latent variable models. Journal of Applied Statistics 40 (3):508–26. doi:10.1080/02664763.2012.745837.
  • Davino, C., and V. E. Vinzi. 2016a. Quantile composite-based path modelling. Advances in Data Analysis and Classification 10 (4):491–520. doi:10.1007/s11634-015-0231-9.
  • Davino, C., P. Dolce, and S. Taralli. 2017. Quantile composite-based model: A recent advance in PLS-PM. In Partial least squares path modeling. Basic concepts, methodological issues and applications, edited by H. Latan and R. Noonan, 81–108. Cham, Switzerland: Springer International Publishing AG.
  • Davino, C., P. Dolce, S. Taralli, and V. E. 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 136 (3):999–1029. doi:10.1007/s11205-016-1453-8.
  • Davino, C., V. E. Vinzi, and P. Dolce. 2016. Assessment and validation in quantile composite-based path modeling. In The Multiple Facets of Partial Least Squares and Related Methods. Springer Proceedings in Mathematics and Statistics, 169–85. New York: Springer Verlag.
  • Edwards, J. R., and R. Bagozzi. 2000. On the nature and direction of relationships between constructs and measures. Psychological Methods 5 (2):155–74. doi:10.1037/1082-989x.5.2.155.
  • Esposito, V. V., W. W. Chin, J. Henseler, and H. Wang. 2010. Handbook of partial least squares. Concepts, methods and applications. New York: Springer Handbooks of Computational Statistics.
  • Hair, J. F., G. T. M. Hult, C. M. Ringle, and M. Sarstedt. 2017. A primer on partial least squares structural equation modeling (PLS-SEM). 2nd ed. Thousand Oaks, CA: SAGE Publications.
  • Hair, J. F., M. Sarstedt, T. Pieper, and C. M. Ringle. 2012a. The use of partial least squares structural equation modeling in strategic management research: A review of past practices and recommendations for future applications. Long Range Planning 45 (5–6):320–40. doi:10.1016/j.lrp.2012.09.008.
  • Hair, J., M. Sarstedt, C. M. Ringle, and J. Mena. 2012b. An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science 40 (3):414–33. doi:10.1007/s11747-011-0261-6.
  • Han, P. S., L. L. Kong, J. W. Zhao, and X. C. Zhou. 2019. A general framework for quantile estimation with incomplete data. Journal of the Royal Statistical Society. Series B, Statistical Methodology 81 (2):305–33. doi:10.1111/rssb.12309.
  • Horvitz, D. G., and D. J. Thompson. 1952. A generalization of sampling without replacement from a finite population. Journal of the American Statistical Association 47 (260):663–85. doi:10.1080/01621459.1952.10483446.
  • Jarvis, C. B., S. B. Mackenzie, and P. M. Podsakoff. 2003. A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research 30 (2):199–218. doi:10.1086/376806.
  • Johnson, R. E., C. C. Rosen, C. H. Chang, E. Djurdjevic, and M. U. Taing. 2012. Recommendations for improving the construct clarity of higher-order multidimensional constructs. Human Resource Management Review 22 (2):62–72. doi:10.1016/j.hrmr.2011.11.006.
  • Judge, T. A., and C. L. Hulin. 1993. Job satisfaction as a reflection of disposition: A multiple-source causal analysis. Organizational Behavior and Human Decision Processes 56 (3):388–421. doi:10.1006/obhd.1993.1061.
  • Kim, J. K. 2011. Parametric fractional imputation for missing data analysis. Biometrika 98 (1):119–32. doi:10.1093/biomet/asq073.
  • Koenker, R. 2005. Quantile regression. New York: Cambridge University Press.
  • Koenker, R., and G. J. Bassett. 1978. Regression quantiles. Econometrica 46 (1):33–50. doi:10.2307/1913643.
  • Lipsitz, S. R., G. M. Fitzmaurice, G. Molenberghs, and L. P. Zhao. 1997. Quantile regression methods for longitudinal data with drop-outs: Application to CD4 cell counts of patients infected with the human immunodeficiency virus. Journal of the Royal Statistical Society: Series C (Applied Statistics) 46 (4):463–76. doi:10.1111/1467-9876.00084.
  • Little, R. J. A., and D. B. Rubin. 1987. Statistical analysis with missing data. New York: Wiley.
  • Liu, X. 2008. The synthesis evaluation of listed companys financial indicators based on partial least square path modeling. Application of Statistics an Management 27 (4):695–700.
  • LohmØLler. J. B. 1989. Latent variable path modeling with partial least squares. Heidelberg: Physica-Verlag.
  • Mackenzie, S. B., P. M. Podsakoff, and C. B. Jarvis. 2005. The problem of measurement model misspecification in behavioral and organizational research and some recommended solutions. The Journal of Applied Psychology 90 (4):710–30. doi:10.1037/0021-9010.90.4.710.
  • MacKenzie, S. B., P. M. Podsakoff, and N. P. Podsakoff. 2011. Construct measurement and validation procedures in mis and behavioral research: Integrating new and existing techniques. MIS Quarterly 35 (2):293–334. doi:10.2307/23044045.
  • Polites, G. L., N. Roberts, and J. Thatcher. 2012. Conceptualizing models using multidimensional constructs: A review and guidelines for their use. European Journal of Information Systems 21 (1):22–48. doi:10.1057/ejis.2011.10.
  • Ringle, C. M., M. Sarstedt, and D. W. Straub. 2012. Editors comments: A critical look at the use of PLS-SEM in MIS quarterly. MIS Quarterly 36 (1):iii–xiv. doi:10.2307/41410402.
  • Ringle, C. M., S. Wende, and J. M. Becker. 2015. SmartPLS 3. Boenningstedt: SmartPLS GmbH.
  • Robert, W. G., R. K. Bruce, and O. A. W. Herman. 1979. Partial least squares path modeling with latent variables. Analytica Chimica Acta 112 (4):417–21.
  • Ruan, J., and H. Ji. 2006. Economic development evaluation based on the PLS structural equation model for west of China. Statistical Education 8:4–7.
  • Sanchez, G. 2013. PLS path modeling with R. Berkeley: Trowchez Editions.
  • Sherwood, B., L. Wang, and X. Zhou. 2013. Weighted quantile regression for analyzing health care cost data with missing covariates. Statistics in Medicine 32 (28):4967–79. doi:10.1002/sim.5883.
  • Tenenhaus, M., V. V. Esposito, Y. M. Chatelin, and C. Lauro. 2005. PLS path modeling. Computational Statistics & Data Analysis 48 (1):159–205. doi:10.1016/j.csda.2004.03.005.
  • Wang, C. Y., S. J. Wang, L. Zhao, and S. T. Ou. 1997. Weighted semiparametric estimation in regression analysis with missing covariate data. Journal of the American Statistical Association 92 (438):512–25. doi:10.1080/01621459.1997.10474004.
  • Wang, H., and L. Fu. 2004. The application research of PLS path modeling on establishing synthesis evaluation index. System Engineering: Theory and Practice 10:80–5.
  • Wei, Y., Y. Ma, and R. J. Carroll. 2012. Multiple imputation in quantile regression. Biometrika 99 (2):423–38. doi:10.1093/biomet/ass007.
  • Wetzels, M., G. Odekerken-Schroder, and C. van Oppen. 2009. Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS Quarterly 33 (1):177–95. doi:10.2307/20650284.

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