Research Details

Abstract

A crucial undertaking in research on organizations is to obtain valid estimates of the organization-level constructs and their relationships to one another. One organizational research methodology has multiple individuals within each firm act as informants and report their perceptions of these constructs. Drawing upon past work, a comprehensive, confirmatory measurement approach is presented that enables delineation of the organizational constructs of interest from potential sources of measurement error in multiple informant reports. A confirmatory composition model first specifies the relationship between measurements taken from multiple/informants and organization-level indicators of organizational constructs. This model provides an explicit representation of the informants' perceptual agreement with respect to each measure, the bias in their responses due to their particular perspectives on the firm, and random measurement error. Measures which demonstrate significant perceptual agreement across informants are retained as organization-level indicators. A confirmatory measurement model is then presented that relates the defined indicators, to their posited underlying organizational constructs, with the constructs allowed to freely intercorrelate. Finally, a structural model relates the constructs to one another as specified by some theory. The structure model in conjunction with the measurement model permits a comprehensive assessment of the construct validity of the organizational properties. An extension of the approach is also discussed where the assessment of commonalities and differences between different kinds of organizations with respect to some theoretical model is of interest. An illustration of the confirmatory approach is given, drawing upon data from a study of distributor firm and manufacturer firm working relationships.

Type

Article

Author(s)

James Anderson

Date Published

1987

Citations

Anderson, James. 1987. An Approach for Confirmatory Measurement and Structural Equation Modeling of Organized Properties. Management Science.(4): 525-541.