scispace - formally typeset
Open AccessJournal ArticleDOI

In defense of causal-formative indicators: A minority report.

Reads0
Chats0
TLDR
It is concluded that measurement theory needs (a) to incorporate these types of indicators, and (b) to better understand their similarities to and differences from traditional indicators.
Abstract
Causal-formative indicators directly affect their corresponding latent variable. They run counter to the predominant view that indicators depend on latent variables and are thus often controversial. If present, such indicators have serious implications for factor analysis, reliability theory, item response theory, structural equation models, and most measurement approaches that are based on reflective or effect indicators. Psychological Methods has published a number of influential articles on causal and formative indicators as well as launching the first major backlash against them. This article examines 7 common criticisms of these indicators distilled from the literature: (a) A construct measured with "formative" indicators does not exist independently of its indicators; (b) Such indicators are causes rather than measures; (c) They imply multiple dimensions to a construct and this is a liability; (d) They are assumed to be error-free, which is unrealistic; (e) They are inherently subject to interpretational confounding; (f) They fail proportionality constraints; and (g) Their coefficients should be set in advance and not estimated. We summarize each of these criticisms and point out the flaws in the logic and evidence marshaled in their support. The most common problems are not distinguishing between what we call causal-formative and composite-formative indicators, tautological fallacies, and highlighting issues that are common to all indicators, but presenting them as special problems of causal-formative indicators. We conclude that measurement theory needs (a) to incorporate these types of indicators, and (b) to better understand their similarities to and differences from traditional indicators. (PsycINFO Database Record

read more

Citations
More filters
Book ChapterDOI

Partial Least Squares Structural Equation Modeling

TL;DR: Partial least squares structural equation modeling (PLS-SEM) has become a popular method for estimating path models with latent variables and their relationships as discussed by the authors, and a common goal of PLSSEM analyses is to identify key success factors and sources of competitive advantage for important target constructs such as customer satisfaction, customer loyalty, behavioral intentions, and user behavior.
Journal ArticleDOI

Estimation issues with PLS and CBSEM: Where the bias lies! ☆

TL;DR: In this article, the authors disentangle conceptual variables and their measurement model operationalization from the estimation perspective, and develop a unifying framework for different structural equation modeling methods, highlighting the biases that occur when using composite-based partial least squares path modeling to estimate common factor models and common factor-based covariance-based structural equation modelling to estimate composite models.
Journal ArticleDOI

An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research

TL;DR: This work systematically examines how PLS-SEM has been applied in major hospitality research journals with the aim of providing important guidance and, if necessary, opportunities for realignment in future applications.
Journal ArticleDOI

Partial least squares structural equation modeling in HRM research

TL;DR: Partial least squares structural equation modeling (PLS-SEM) has become a key multivariate analysis technique that human resource management (HRM) researchers frequently use as discussed by the authors, and it has been shown to be effective in many HRM problems.
Journal ArticleDOI

Gain more insight from your PLS-SEM results: The importance-performance map analysis

TL;DR: This paper is the first to offer researchers a tutorial and annotated example of an IPMA, and is particularly useful for generating additional findings and conclusions by combining the analysis of the importance and performance dimensions in practical PLS-SEM applications.
References
More filters
Book

Structural Equations with Latent Variables

TL;DR: The General Model, Part I: Latent Variable and Measurement Models Combined, Part II: Extensions, Part III: Extensions and Part IV: Confirmatory Factor Analysis as discussed by the authors.
Book

Scale development : theory and applications

TL;DR: In this paper, the authors discuss the role of measurement in the social sciences and propose guidelines for scale development in the context of scale-based measurement. But, the authors do not discuss the relationship between scale scores and scale length.
Book

Scale Development : Theory and Applications

TL;DR: Measurement in the Broader Research Context Before the Scale Development After the Scale Administration Final Thoughts References Index about the Author.
Journal ArticleDOI

Power analysis and determination of sample size for covariance structure modeling.

TL;DR: In this article, a framework for hypothesis testing and power analysis in the assessment of fit of covariance structure models is presented, where the value of confidence intervals for fit indices is emphasized.
Book

Statistical Theories of Mental Test Scores

TL;DR: In this paper, the authors present a survey of test theory models and their application in the field of mental test analysis. But the focus of the survey is on test-score theories and models, and not the practical applications and limitations of each model studied.
Related Papers (5)