Jason W. Miller

Extensions of the Johnson-Neyman Technique to Linear Models With Curvilinear Effects: Derivations and Analytical Tools

The past decade has witnessed renewed interest in the use of the Johnson-Neyman (J-N) technique for calculating the regions of significance for the simple slope of a focal predictor on an outcome variable across the range of a second, continuous independent variable. Although tools have been developed to apply this technique to probe 2- and 3-way interactions in several types of linear models, this method has not been extended to include quadratic terms or more complicated models involving quadratic terms and interactions. Curvilinear relations of this type are incorporated in several theories in the social sciences. This article extends the J-N method to such linear models along with presenting freely available online tools that implement this technique as well as the traditional pick-a-point approach. Algebraic and graphical representations of the proposed J-N extension are provided. An example is presented to illustrate the use of these tools and the interpretation of findings. Issues of reliability as well as “spurious moderator” effects are discussed along with recommendations for future research.

The Prowess and Pitfalls of Bayesian Structural Equation Modeling Important Considerations for Management Research

Muthén and Asparouhov introduced an approach for conducting Bayesian inference in the context of structural equation models that they termed Bayesian structural equation modeling (BSEM). In this article, we provide an overview of the BSEM technique, illustrate how this technique relates to confirmatory and exploratory factor analysis, and highlight several key problems with using the BSEM approach as it is currently advocated. Utilizing data from a large-scale study of entrepreneurial self-efficacy, we develop a modified approach for applying the BSEM technique in a manner that is more consistent with accepted principles of reflective measurement, factor analysis, and model selection. We devise a series of recommendations to guide future use of the BSEM technique to help ensure that mainstream use of this approach heralds the coming of a new day in measurement development rather than a false dawn.