Finding groups in structural equation modeling through the partial least squares algorithm
暂无分享,去创建一个
[1] P. Rousseeuw. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .
[2] Edward E. Rigdon,et al. Choosing PLS path modeling as analytical method in European management research: A realist perspective , 2016 .
[3] V. E. Vinzi,et al. REBUS-PLS: A response-based procedure for detecting unit segments in PLS path modelling , 2008 .
[4] Christian M. Ringle,et al. Finite Mixture Partial Least Squares Analysis: Methodology and Numerical Examples , 2010 .
[5] D. Straub,et al. Editor's comments: a critical look at the use of PLS-SEM in MIS quarterly , 2012 .
[6] Arun Rai,et al. Discovering Unobserved Heterogeneity in Structural Equation Models to Avert Validity Threats , 2013, MIS Q..
[7] M. Sarstedt. A review of recent approaches for capturing heterogeneity in partial least squares path modelling , 2008 .
[8] Marko Sarstedt,et al. Segmentation of PLS path models by iterative reweighted regressions , 2015 .
[9] Edward E. Rigdon,et al. On Comparing Results from CB-SEM and PLS-SEM: Five Perspectives and Five Recommendations , 2017 .
[10] Marko Sarstedt,et al. Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research , 2014 .
[11] Marko Sarstedt,et al. Treating Unobserved Heterogeneity in PLS-SEM: A Multi-method Approach , 2017 .
[12] William M. Rand,et al. Objective Criteria for the Evaluation of Clustering Methods , 1971 .
[13] Marko Sarstedt,et al. Genetic algorithm segmentation in partial least squares structural equation modeling , 2013, OR Spectrum.
[14] M. Sarstedt,et al. Uncovering and Treating Unobserved Heterogeneity with FIMIX-PLS: Which Model Selection Criterion Provides an Appropriate Number of Segments? , 2011 .
[15] W. DeSarbo,et al. Finite-Mixture Structural Equation Models for Response-Based Segmentation and Unobserved Heterogeneity , 1997 .
[16] Silvia Squillacciotti,et al. Prediction Oriented Classification in PLS Path Modeling , 2010 .
[17] Marko Sarstedt,et al. Identifying and treating unobserved heterogeneity with FIMIX-PLS: Part II – A case study , 2016 .
[18] Joseph F. Hair,et al. Estimation issues with PLS and CBSEM: Where the bias lies! ☆ , 2016 .
[19] José L. Roldán,et al. European management research using partial least squares structural equation modeling (PLS-SEM) , 2015 .
[20] Wayne S. DeSarbo,et al. Market Segmentation for Customer Satisfaction Studies via a New Latent Structure Multidimensional Scaling Model , 2005 .
[21] J. Lohmöller. Predictive vs. Structural Modeling: PLS vs. ML , 1989 .
[22] M. Wedel,et al. Market Segmentation: Conceptual and Methodological Foundations , 1997 .
[23] M. Sarstedt,et al. Treating unobserved heterogeneity in PLS path modeling: a comparison of FIMIX-PLS with different data analysis strategies , 2010 .
[24] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[25] Eva Ceulemans,et al. CHull as an alternative to AIC and BIC in the context of mixtures of factor analyzers , 2013, Behavior research methods.
[26] H. Kiers,et al. Factorial k-means analysis for two-way data , 2001 .
[27] Trupti M. Kodinariya,et al. Review on determining number of Cluster in K-Means Clustering , 2013 .
[28] G. W. Milligan,et al. An examination of procedures for determining the number of clusters in a data set , 1985 .
[29] Hans Baumgartner,et al. On the use of structural equation models for marketing modeling , 2000 .
[30] K. Jöreskog. Structural analysis of covariance and correlation matrices , 1978 .
[31] Michel Wedel,et al. International Market Segmentation Based on Consumer–Product Relations , 1999 .
[32] Frank Huber,et al. Capturing Customer Heterogeneity using a Finite Mixture PLS Approach , 2002 .
[33] Robert Tibshirani,et al. Estimating the number of clusters in a data set via the gap statistic , 2000 .
[34] Markus Eberl,et al. An Application of PLS in Multi-Group Analysis: The Need for Differentiated Corporate-Level Marketing in the Mobile Communications Industry , 2010 .
[35] T. Caliński,et al. A dendrite method for cluster analysis , 1974 .
[36] J. Carroll,et al. K-means clustering in a low-dimensional Euclidean space , 1994 .