Structural equation modeling, often referred to as SEM, is a well‐established, covariance‐based multivariate method used in Human Resource Development (HRD) quantitative research. In some research contexts, however, the rigorous assumptions associated with covariance‐based SEM (CB‐SEM) limit applications of the method. An emergent complementary SEM approach, partial least squares structural equation modeling (PLS‐SEM), is a variance‐based SEM method that provides valid solutions and overcomes several limitations associated with CB‐SEM. Despite PLS‐SEM's increasing popularity in many social sciences disciplines, the method has yet to gain traction in the field of HRD. An accessible overview of the method, including potential advantages for HRD research and extant methodological advancements, is provided in this article with the goal of encouraging productive dialogue in the field of HRD surrounding the PLS‐SEM approach. We present an emergent analytical tool for quantitative HRD research, offer practical guidelines for researchers to consider when selecting a SEM method, and clarify assessment stages and up‐to‐date evaluation criteria through an illustrative example.
To the detriment of human resource development (HRD) theory building and research, many scholars may think that research data with a low coefficient alpha is destined for the file drawer; this does not have to be the case. Contemporary literature suggests that many scholars do not know how to move forward with data that yields α < .70. In addition, an investigation revealed that many scholars practice the method of item deletion to increase alpha. Besides supporting the case that discarding research simply because of low coefficient alphas may be unnecessary, a guide is presented to demonstrate how scholars and scholar–practitioners may be able to analyze data when an initial estimate of internal reliability is low. We caution that deleting items may increase reliability at the cost of validity. As an alternative, this study demonstrates that eliminating subjects can increase alpha and maintain the integrity of the scale. This guide presents generalizability theory as a means to identify the source of error variance in data as well as a step-by-step process to correct for low coefficient alpha. The guide is illustrated with data and R syntax.
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