ERIC Number: EJ1115615
Record Type: Journal
Publication Date: 2016
Abstractor: As Provided
Omission of Causal Indicators: Consequences and Implications for Measurement
Aguirre-Urreta, Miguel I.; Rönkkö, Mikko; Marakas, George M.
Measurement: Interdisciplinary Research and Perspectives, v14 n3 p75-97 2016
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.
Descriptors: Causal Models, Measurement, Research Problems, Structural Equation Models, Formative Evaluation, Statistical Analysis, Path Analysis, Regression (Statistics), Monte Carlo Methods
Psychology Press. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A