[work] Perustieteiden korkeakoulu / SCI

Permanent URI for this collectionhttps://aaltodoc.aalto.fi/handle/123456789/58

Browse

Browsing [work] Perustieteiden korkeakoulu / SCI by Author "Aguirre-Urreta, Miguel I."

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Estimating Formative Measurement Models in IS Research – Analysis of the Past and Recommendations for the Future
    (2016) Rönkkö, Mikko; Evermann, Joerg; Aguirre-Urreta, Miguel I.
     School of Science | J Muu elektroninen julkaisu
    While debates on the appropriateness of formative measurement within structural equation models continue, such models are frequently found in IS research. IS researchers faced with such a model must identify the best method to estimate the model parameters, and have at their disposal covariance-based structural equation modeling (CBSEM), Partial Least Squares path modeling (PLS), and regression with summed scales, among other techniques.While all these methods can estimate models with formatively-specified latent variables, IS researchers frequently cite the presence of formative measurement as the reason for choosing PLS for model estimation over alternatives. Intuitively, a composite-based method such as PLS would appear to have an advantage in this particular scenario. In fact, some PLS researchers argue that PLS should only be used for such models. However, there is a dearth of empirical studies showing whether such an advantage does indeed exist.In this research, we discuss the statistical problems posed by models that include formatively-specified latent variables, and present a large-scale simulation study to investigate the relative performance of different estimation methods when faced with formative measurement, using models from studies published in MIS Quarterly. Based on our simulation results, we present recommendations for IS researchers interested in the estimation of models that include formatively-specified latent variables.
  • Improvements to PLSc: Remaining problems and simple solutions
    (2016) Rönkkö, Mikko; McIntosh, Cameron N.; Aguirre-Urreta, Miguel I.
     School of Science | J Muu elektroninen julkaisu
    The recent article by Dijkstra and Henseler (2015b) presents a consistent partial least squares (PLSc) estimator that corrects for measurement error attenuation and provides evidence showing that, generally, PLSc performs comparably to a wide variety of more conventional estimators for structural equation models (SEM) with latent variables. However, PLSc does not adjust for other limitations of conventional PLS, namely: (1) bias in estimates of regression coefficients due to capitalization on chance; and (2) overestimation of composite reliability due to the proportionality relation between factor loadings and indicator weights. In this article, we illustrate these problems and then propose a simple solution: the use of unit-weighted composites, rather than those constructed from PLS results, combined with errors-in-variables regression (EIV) by using reliabilities obtained from factor analysis. Our simulations show that these two improvements perform as well as or better than PLSc. We also provide examples of how our proposed estimator can be easily implemented in various proprietary and open source software packages.