Omission of Causal Indicators: Consequences and Implications for Measurement

Miguel I. Aguirre-Urreta*, Mikko Rönkkö, George M. Marakas

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

21 Citations (Scopus)

Abstract

One of the central assumptions of the causal-indicator literature is that all causal indicators must be included in the research model and that the exclusion of one or more relevant causal indicators would have severe negative consequences by altering the meaning of the latent variable. In this research we show that the omission of a relevant causal indicator does not affect downstream estimates relating the focal latent variable to other variables in the model, which challenges the current stance in the literature. Further, we argue that this occurrence presents a fundamental challenge to the causal-indicator literature, in that the lack of negative consequences is not consistent with the tenet that latent variables derive their meaning from the set of causal indicators included in a research model. Rather, though causal indicators help identify the focal latent variable, its meaning is derived from its position as a common factor of other downstream variables—latent or observed—to which it is related.

Original languageEnglish
Pages (from-to)75-97
Number of pages23
JournalMEASUREMENT: INTERDISCIPLINARY RESEARCH AND PERSPECTIVES
Volume14
Issue number3
DOIs
Publication statusPublished - 2 Jul 2016
MoE publication typeA1 Journal article-refereed

Keywords

  • causal indicators
  • formative measurement
  • omitted variables
  • structural equation modeling

Fingerprint

Dive into the research topics of 'Omission of Causal Indicators: Consequences and Implications for Measurement'. Together they form a unique fingerprint.

Cite this