On the Use of Formative Measurement Specifications in Structural Equation Modeling: A Monte Carlo Simulation Study to Compare Covariance-Based and Partial Least Squares Model Estimation Methodologies

The broader goal of this paper is to provide social researchers with some analytical guidelines when investigating structural equation models (SEM) with predominantly a formative specification. This research is the first to investigate the robustness and precision of parameter estimates of a formative SEM specification. Two distinctive scenarios (normal and non-normal data scenarios) are compared with the aid of a Monte Carlo simulation study for various covariance-based structural equation modeling (CBSEM) estimators and various partial least squares path modeling (PLS-PM) weighting schemes. Thus, this research is also one of the first to compare CBSEM and PLS-PM within the same simulation study. We establish that the maximum likelihood (ML) covariance-based discrepancy function provides accurate and robust parameter estimates for the formative SEM model under investigation when the methodological assumptions are met (e.g., adequate sample size, distributional assumptions, etc.). Under these conditions, ML-CBSEM outperforms PLS-PM. We also demonstrate that the accuracy and robustness of CBSEM decreases considerably when methodological requirements are violated, whereas PLS-PM results remain comparatively robust, e.g. irrespective of the data distribution. These findings are important for researchers and practitioners when having to choose between CBSEM and PLS-PM methodologies to estimate formative SEM in their particular research situation.PLS, path modeling, covariance structure analysis, structural equation modeling, formative measurement, simulation study

On the Use of Formative Measurement Specifications in Structural Equation Modeling: A Monte Carlo Simulation Study to Compare Covariance-Based and Partial Least Squares Model Estimation Methodologies

The broader goal of this paper is to provide social researchers with some analytical guidelines when investigating structural equation models (SEM) with predominantly a formative specification. This research is the first to investigate the robustness and precision of parameter estimates of a formative SEM specification. Two distinctive scenarios (normal and non-normal data scenarios) are compared with the aid of a Monte Carlo simulation study for various covariance-based structural equation modeling (CBSEM) estimators and various partial least squares path modeling (PLS-PM) weighting schemes. Thus, this research is also one of the first to compare CBSEM and PLS-PM within the same simulation study. We establish that the maximum likelihood (ML) covariance-based discrepancy function provides accurate and robust parameter estimates for the formative SEM model under investigation when the methodological assumptions are met (e.g., adequate sample size, distributional assumptions, etc.). Under these conditions, ML-CBSEM outperforms PLS-PM. We also demonstrate that the accuracy and robustness of CBSEM decreases considerably when methodological requirements are violated, whereas PLS-PM results remain comparatively robust, e.g. irrespective of the data distribution. These findings are important for researchers and practitioners when having to choose between CBSEM and PLS-PM methodologies to estimate formative SEM in their particular research situation.marketing ;

A Dangerous Blind Spot in IS Research: False Positives Due to Multicollinearity Combined With Measurement Error

Econometrics textbooks generally conclude that in regression, because the calculation of path estimate variances includes avariance inflation factor (VIF) that reflects correlations between “independent” constructs, multicollinearity should not causefalse positives except in extreme cases. However, textbook treatments of multicollinearity assume perfect measurement –rare in behavioral research. VIF is based on apparent correlations between constructs -- always less than actual correlationswhen measurement error exists. A brief review of recent articles in the MIS Quarterly suggests that the conditions forexcessive false positives are present in published research. In this paper we show (analytically and with a series of MonteCarlo simulations) that multicollinearity combined with measurement error presents greater than expected dangers from falsepositives in IS research when regression or PLS is used. Suggestions for how to address this situation are offered

USE OF PARTIAL LEAST SQUARES AS A THEORY TESTING TOOL – AN ANALYSIS OF INFORMATION SYSTEMS PAPERS

Motivated by recent critique toward partial least squares path modeling (PLS), we present a research question if the PLS method, as used currently, is at all an appropriate tool for theory testing. We briefly summarize some of the recent critique of the use of PLS in IS as a theory testing tool. Then we analyze the results of 12 PLS analyzes published in leading IS journals testing if these models would have been rejected in the case that the data used for model testing had very little correspondence with the theorized models. Our Monte Carlo simulation shows that PLS will often provide results that support the tested hypotheses even if the model was not appropriate for the data. We conclude that the current practices of PLS studies have likely resulted in publishing research where the results are likely false and suggest that more attention should be paid on the assumptions of the PLS model or that alternative approached like summed scales and regression or structural equation modeling with estimators that have known statistical properties should be used instead

The Use of PLS When Analyzing Formative Constructs: Theoretical Analysis and Results From Simulations

Partial Least Squares (PLS) has become an increasingly popular approach to testing research models with multiple proposed causality links. Moreover, recent interest in the specification of constructs in a formative manner has accentuated this tendency, given the purported ability of PLS to handle this methodological development. While a review of the literature reveals an extensive use of PLS in this capacity, there is neither theoretical nor empirical evidence supporting this property of the technique. An examination of the inner workings of PLS shows several limitations of PLS when used in \u27formative\u27 (Mode B) estimation, and compares it to linear regression and covariance-based approaches. Results from Monte Carlo simulations comparing the performance of PLS and covariance-based techniques in estimating models with formatively specified constructs in either exogenous or endogenous positions reveals important biases for PLS, but not for covariance-based SEM. The results are discussed and recommendations for researchers are proposed

Accuracy on parameter recovery, with ordinals data, of structure covariance analysis and partial least squares path modeling

ReconocimientoSe compara la precisión en la recuperación de parámetros del Análisis de Estructura de Covarianza (ACOV) y el Modelo de Rutas mediante Mínimos Cuadrados Parciales (PLS-PM), en un modelo simple con variables manifiestas simuladas con escala ordinal de cinco puntos. Se utiliza un diseño experimental, manipulando el método de estimación, tamaño muestral, nivel de asimetría y tipo de especificación del modelo. Se valora la media de las diferencias absolutas para el modelo estructural. ACOV presenta estimaciones más precisas que PLS-PM, en distintas condiciones experimentales. Cuando se utiliza un tamaño muestral pequeño, ambas técnicas son igualmente precisas. Se sugiere utilizar ACOV frente a PLS-PM. Se desaconseja fundamentar la elección de PLS-PM frente a ACOV en la utilización de una muestra pequeñaThe accuracy on parameter recovery is compared between Structure Covariance Analysis (ACOV) and Partial Least Squares Path Modeling (PLS-PM), with simulated ordinals data with 5 points, in a simple model. An experimental design is used, controlling the estimation method, sample size, skewness level and model specification. Mean absolute differences are used to assess accuracy for the structural model. ACOV provided more accurate estimates of the structural parameters than PLS-PM in different experimental conditions. With a small sample size, both techniques are equally accurate. Using ACOV against PLS-PM is suggested. PLS choosing ACOV instead based on the use of a small sample size is not recommendedArtículo de investigación. FONDECYT de Iniciación 2013, N° 11130722. Beca Presidente de la República de Chile (2008), de la Comisión Nacional de Investigación Científica y Tecnológica de Chile (CONICYT

Consistent and asymptotically normal PLS estimators for linear structural equations

A vital extension to partial least squares (PLS) path modeling is introduced: consistency. While maintaining all the strengths of PLS, the consistent version provides two key improvements. Path coefficients, parameters of simultaneous equations, construct correlations, and indicator loadings are estimated consistently. The global goodness-of-fit of the structural model can also now be assessed, which makes PLS suitable for confirmatory research. A Monte Carlo simulation illustrates the new approach and compares it with covariance-based structural equation modelin

PLS Pluses and Minuses_x000D_ In Path Estimation Accuracy

PLS Pluses and Minuses Abstract: In this paper we ask three questions. Do PLS path estimates compensate for measurement error? Do they capitalize on chance? And is PLS able to more accurately weight measurement indicators so as to make path estimations more accurate? The evidence is quite convincing that PLS path estimates do have all three of these characteristics. Our analysis suggests, however, that measurement error has by far the largest impact, followed by capitalization on chance, with better weighting of indicators having the smallest influence. MIS researchers need to consider how to respond to these findings._x000D_ _x000D

CONSTRUCT VALIDITY IN PARTIAL LEAST SQUARES PATH MODELING

Partial least squares path modeling (PLS) has seen increased use in the information systems research community. One of the stated key advantages of PLS is that it weights the indicator variables based on the strength of the relationship between the indicators and the underlying constructs, which presumably decreases the effect of measurement error in the analysis results. In this paper we argue that this assumption is not valid. While PLS indeed does weight the indicators to maximize the explained variance, it does this by including error variance in the model thus reducing construct validity. We use a simulation study of a simple PLS model to show that when compared to traditional sum scale approach, PLS estimates are actually often less valid. Although our study has its limitations, it hints that the use of PLS as a theory testing tool should be reevaluated and that more research testing the effectiveness of the PLS approach is in order