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The Partial Least Squares Approach to Structural Equation Modeling

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Abstract

Provides a nontechnical introduction to the partial least squares (PLS) approach. As a logical base for comparison, the PLS approach for structural path estimation is contrasted to the covariance-based approach. In so doing, a set of considerations are then provided with the goal of helping the reader understand the conditions under which it might be reasonable or even more appropriate to employ this technique. This chapter builds up from various simple 2 latent variable models to a more complex one. The formal PLS model is provided along with a discussion of the properties of its estimates. An empirical example is provided as a basis for highlighting the various analytic considerations when using PLS and the set of tests that one can employ is assessing the validity of a PLS-based model. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
... Para calcular los p-valores asociados a cada uno de los parámetros estimados (cargas, coeficientes de regresión) se usará Bootstrapping que permite examinar la estabilidad de las estimaciones ofrecidas por el análisis PLS a través de un procedimiento de re-muestreo (Chin, 1998). Para tratar los valores perdidos, se emplea el método de Pairwise deletion donde, con el fin de mantener la máxima información posible, para cada análisis, la eliminación por pares solo elimina aquellos casos que muestran valores faltantes en cada par de variables. ...
... Estos valores permiten soportar H1, H2, H3, H5, H8, H9 y H10 dado que sus coeficientes son significativos y superiores a 0,3. Siguiendo a Chin (1998), se puede afirmar que existe una relación causal entre dos constructos del modelo si el valor β entre ellas es mayor o igual a 0,2 y además es significativo estadísticamente. Las hipótesis H4, H6 y H7 no son significativos al 5 %, si bien la H4 sí resulta significativa al 10 %. ...
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... The results obtained from Cronbach's α reliability coefficient are satisfactory, ranging between 0.8 and 0.9, recommended values for advanced stages of research (Nunnally & Bernstein, 1994). The analysis of composite reliability yields satisfactory results, which far exceed the minimum required, 0.7 (Chin, 1998). Furthermore, all the constructs of the structural model obtain values for the average variance extracted, greater than 0.5 (Fornell & Larcker, 1981), so we can confirm the reliability and convergent validity of the measurement instrument of the structural model. ...
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... Cronbach's alpha value exceeding 0.7 is generally deemed acceptable for ensuring the reliability of research instruments (Nunnally & Bernstein, 1994 (Fornell & Larcker, 1981) have established the discriminant validity of the constructs. In addition, AVE values for each construct being greater than the maximum shared variance (MSV) and the square root of AVE values exceeding inter-construct correlations, as per the criteria set by Chin (1998) have further confirmed the discriminant validity. These results have provided robust evidence supporting both convergent and discriminant validity would enable researchers to proceed with SEM. ...
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... Para evaluar el modelo, hemos utilizado el coeficiente de determinación R², que representa la cantidad de varianza explicada en una variable dependiente por las variables independientes. El valor de R 2 de fatiga de redes sociales fue de 0,181, superando el umbral recomendado de 0,100 (Chin, 1998), lo que sugiere que el modelo entero explicó suficiente varianza en los constructos endógenos. ...
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... Moreover, we employed Average Variance Extracted (AVE) scores to assess convergent validity, with recommendations from Chin (1998). Chin proposed that AVE values should be equal to or greater than 0.50 for convergent validity. ...
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