Needed Developments in the Understanding of Quasi-Factor Methods

In this response to Evermann and Rönkkö (2023), I acknowledge areas of agreement about applying partial least squares (PLS) path modeling but cite substantial disagreements. The authors encourage researchers to also consider generalized structured component analysis and regression component analysis as procedures built around weighted composites rather than common factors. We can best regard all of these methods as quasi-factor methods, which avoid the uncertainty of factor indeterminacy though they still rely on covariance structures similar to those in factor analysis. They remain useful tools for researchers

Predictive Validity and Formative Measurement in Structural Equation Modeling: Embracing Practical Relevance

Composite-based methods like partial least squares (PLS) path modeling have an advantage over factor-based methods (like CB-SEM) because they yield determinate predictions, while factor-based methods’ prediction is constrained in this regard by factor indeterminacy. To maximize practical relevance, research findings should extend beyond the study’s own data. We explain how PLS practices, deriving, at least in part, from attempts to mimic factor-based methods, have hamstrung the potential of PLS. In particular, PLS research has focused on parameter recovery and overlooked predictive validity. We demonstrate some implications of considering predictive abilities as a complement to parameter recovery of PLS by reconsidering the institutionalized practice of mapping formative measurement to Mode B estimation of outer relations. Extensive simulations confirm that Mode A estimation performs better when sample size is moderate and indicators are collinear while Mode B estimation performs better when sample size is very large or true predictability (R²) is high

Quantifying model selection uncertainty via bootstrapping and Akaike weights

Picking one ‘winner’ model for researching a certain phenomenon while discarding the rest implies a confidence that may misrepresent the evidence. Multimodel inference allows researchers to more accurately represent their uncertainty about which model is ‘best’. But multimodel inference, with Akaike weights—weights reflecting the relative probability of each candidate model—and bootstrapping, can also be used to quantify model selection uncertainty, in the form of empirical variation in parameter estimates across models, while minimizing bias from dubious assumptions. This paper describes this approach. Results from a simulation example and an empirical study on the impact of perceived brand environmental responsibility on customer loyalty illustrate and provide support for our proposed approach

Parceling Cannot Reduce Factor Indeterminacy in Factor Analysis: A Research Note

Parceling-using composites of observed variables as indicators for a common factor-strengthens loadings, but reduces the number of indicators. Factor indeterminacy is reduced when there are many observed variables per factor, and when loadings and factor correlations are strong. It is proven that parceling cannot reduce factor indeterminacy. In special cases where the ratio of loading to residual variance is the same for all items included in each parcel, factor indeterminacy is unaffected by parceling. Otherwise, parceling worsens factor indeterminacy. While factor indeterminacy does not affect the parameter estimates, standard errors, or fit indices associated with a factor model, it does create uncertainty, which endangers valid inference