ISBN
979-13-87631-36-9
Fecha de publicación
28-12-2024
Licencia
D. R. © copyright 2024; Aurora Irma Máynez Guaderrama y Virginia Guadalupe López Torres.
Sandra Nelly Leyva Hernández
Universidad Autónoma de Baja California
0000-0002-5687-9945
Arcelia Toledo López
Instituto Politécnico Nacional
0000-0002-2328-5438
Leonardo Ramos López
Universidad Autónoma de Baja California
0000-0003-3721-4216
Acerca de
El análisis multivariado ha evolucionado hacia técnicas de segunda generación como el modelado de ecuaciones estructurales que integra técnicas como el análisis factorial y el análisis de regresión lineal (Hair et al., 2017; Williams et al., 2009). Este tipo de modelado se divide en el basado en covarianza y basado en varianza (Rigdon et al., 2017).
La aplicación del modelado de ecuaciones estructurales basado en covarianzas considera supuestos estrictos como datos normales y tamaños de muestra grandes. Mientras que el modelado de ecuaciones estructurales por mínimos cuadrados parciales (Partial Least Squares Structual Equation Modeling, o PLS-SEM por sus siglas en inglés) es menos restrictivo, analiza modelos complejos y tamaños de muestra más reducidos (Hair et al., 2011). Se recomienda PLS-SEM para modelos complejos con más de 5 constructos y más de 6 indicadores por constructo (Sarstedt et al., 2014). Además, ofrece resultados de análisis que otros métodos no proporcionan, como la predicción en modelos de investigación (Nitzl y Chin, 2017). PLS-SEM no requiere normalidad de datos o una muestra grande y además puede estimar modelos de medición formativos y reflectivos (Ruiz et al., 2010).
Diferentes estudios muestran la aplicación de PLS-SEM en varias disciplinas (Roemer, 2016). Existe un aumento notorio de su uso en la investigación en negocios, en sistemas de información de gestión, en marketing y en gestión estratégica (Sinkovics et al., 2016). Recientemente, esta técnica se ha aplicado en la investigación de mercado, para el análisis de capacidades de marketing (Takata, 2016), satisfacción del cliente y lealtad (Al-Msallam y Alhaddad, 2016). También se han hecho comparaciones del uso de tratamientos de datos por PLS consistente y PLS tradicional en un modelo de intención de comportamiento (Cheah et al., 2018). Se recomienda el uso de PLS-SEM para el análisis de mediación y moderación de estudios de marketing con muestras pequeñas y distribución de datos no normal (Wong, 2016). Sin embargo, son pocos los estudios que analizan la mediación por PLS-SEM, comparado con el uso de modelado de ecuaciones estructurales basado en covarianzas para este análisis (Nitzl et al., 2016). No obstante, no existe un acuerdo estadístico sobre los criterios adecuados de dichos análisis.
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