Formative Constructs Implemented via Common Factors

2017

We compare formative and reflective measurement models in the context of structural equation models (SEM). The formative model is expressed as part of the structural regression equation. The identification status of the models is different, although they differ only in the direction of some arrows. It is shown, that the reflective model permits the identification of more parameters and requires less restrictions. Formative measurement models are recommended only under a strict theoretical necessity.

Journal of Business Research, 2008

Research Paper Series (Vol. 14). Maastricht University, Maastricht (Netherlands). , 2009

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.

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.

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European Journal of Information Systems, 2012

Together with the development of information systems research, there has also been increased interest in non-linear relationships between focal constructs. This article presents six Partial Least Squares-based approaches for estimating formative constructs' quadratic effects. In addition, these approaches' performance is tested by means of a complex Monte Carlo experiment. The experiment reveals significant and substantial differences between the approaches. In general, the performance of the hybrid approach as suggested by is most convincing in terms of point estimate accuracy, statistical power, and prediction accuracy. The two-stage approach suggested by showed almost the same performance; differences between it and the hybrid approach -although statistically significant -were unsubstantial. Based on these results, the article provides guidelines for the analysis of nonlinear effects by means of variance-based structural equation modelling.

In this article, we provide guidance for substantive researchers on the use of structural equation modeling in practice for theory testing and development. We present a comprehensive, two-step modeling approach that employs a series of nested models and sequential chi-square difference tests. We discuss the comparative advantages of this approach over a one-step approach. Considerations in specification, assessment of fit, and respecification of measurement models using confirmatory factor analysis are reviewed. As background to the two-step approach, the distinction between exploratory and confirmatory analysis, the distinction between complementary approaches for theory testing versus predictive application, and some developments in estimation methods also are discussed. Substantive use of structural equation modeling has been growing in psychology and the social sciences. One reason for this is that these confirmatory methods (e.g., Bentler, 1983; Browne, 1984; Joreskog, 1978)provide researchers withacom-prehensive means for assessing and modifying theoretical models. As such, they offer great potential for furthering theory development. Because of their relative sophistication, however, a number of problems and pitfalls in their application can hinder this potential from being realized. The purpose of this article is to provide some guidance for substantive researchers on the use of structural equation modeling in practice for theory testing and development. We present a comprehensive, two-step modeling approach that provides a basis for making meaningful inferences about theoretical constructs and their interrelations, as well as avoiding some specious inferences. The model-building task can be thought of as the analysis of two conceptually distinct models (Anderson & Gerbing, 1982; Joreskog & Sorbom, 1984). A confirmatory measurement, or factor analysis, model specifies the relations of the observed measures to their posited underlying constructs, with the constructs allowed to intercorrelate freely. A confirmatory structural model then specifies the causal relations of the constructs to one another, as posited by some theory. With full-information estimation methods, such as those provided in the EQS (Bentler, 1985) or LISREL (Joreskog & Sorbom, 1984) programs, the measurement and structural submodels can be estimated simultaneously. The ability to do this in a one-step analysis ap

Electronic Journal of Applied Statistical Analysis, 2012

Although the dispute between formative models and reflective models is not exactly recent, it is still alive in current literature, largely in the context of structural equation models. There are many aspects of SEM that should be considered in deciding on the right approach to the data. This work is intended to be a brief presentation of the state of the art for SEM based on covariance matrices. We outline the different positions on five particular issues: causality, selection of observed measures, internal consistency, identifiability, and measurement error.

Journal of Modern Applied Statistical Methods, 2015

The ability to validate formative measurement has increased in importance as it is used to develop and test theoretical models. A method is proposed to gather convergent and discriminant validity evidence of formative measurement. Survey data is used to test the proposed method.

uow.edu.au

This paper presents a framework that helps researchers to design and validate both formative and reflective measurement models. The 13 framework draws from the existing literature and includes both theoretical and empirical considerations. Two important examples, one from 14 international business and one from marketing, illustrate the use of the framework. Both examples concern constructs that are fundamental to 15 theory-building in these disciplines, and constructs that most scholars measure reflectively. In contrast, applying the framework suggests that a 16 formative measurement model may be more appropriate. These results reinforce the need for all researchers to justify, both theoretically and 17 empirically, their choice of measurement model. Use of an incorrect measurement model undermines the content validity of constructs, 18 misrepresents the structural relationships between them, and ultimately lowers the usefulness of management theories for business researchers and 19 practitioners. The main contribution of this paper is to question the unthinking assumption of reflective measurement seen in much of the business 20 literature.