The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Evaluating Effect, Composite, and Causal Indicators in Structural Equation Models1
Abstract
Although the literature on alternatives to effect indicators is growing, there has been little attention given to evaluating causal and composite (formative) indicators. This paper provides an overview of this topic by contrasting ways of assessing the validity of effect and causal indicators in structural equation models (SEMs). It also draws a distinction between composite (formative) indicators and causal indicators and argues that validity is most relevant to the latter. Sound validity assessment of indicators is dependent on having an adequate overall model fit and on the relative stability of the parameter estimates for the latent variable and indicators as they appear in different models. If the overall fit and stability of estimates are adequate, then a researcher can assess validity using the unstandardized and standardized validity coefficients and the unique validity variance estimate. With multiple causal indicators or with effect indicators influenced by multiple latent variables, collinearity diagnostics are useful. These results are illustrated with a number of correctly and incorrectly specified hypothetical models.
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Cites
Regression Diagnostics: Identifying Influential Data and Sources of Collinearity
Belsley, D. A., Kuh, E., & Welsch, R. E. (1980) · Wiley Series in Probability and Statistics · doi:10.1002/0471725153 · Cited by 7,297
Conventional wisdom on measurement: A structural equation perspective.
Bollen, K., & Lennox, R. (1991) · Psychological Bulletin · doi:10.1037/0033-2909.110.2.305 · Cited by 3,618
Self-citationSpecifying Formative Constructs in Information Systems Research1
Petter, S., Straub, D., & Rai, A. (2007) · MIS Quarterly · doi:10.2307/25148814 · Cited by 32
Cited by
An assessment of the use of partial least squares structural equation modeling in marketing research
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-011-0261-6 · Cited by 8,088
Partial least squares structural equation modeling
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017) · Handbook of Market Research · doi:10.1007/978-3-319-05542-8_15-1 · Cited by 3,285
MacKenzie, S. B., Podsakoff, P. M., & Podsakoff, N. P. (2011) · MIS Quarterly · doi:10.2307/23044045 · Cited by 3,165
Testing measurement invariance of composites using partial least squares
Henseler, J., Ringle, C. M., & Sarstedt, M. (2016) · International Marketing Review · doi:10.1108/imr-09-2014-0304 · Cited by 2,964
Sarstedt, M., Ringle, C. M., Smith, D., Reams, R., & Hair, J. F. (2014) · Journal of Family Business Strategy · doi:10.1016/j.jfbs.2014.01.002 · Cited by 2,212
Metadata
- Authors
- Bollen, K. A.
- Venue
- MIS Quarterly
- Publisher
- MIS Quarterly
- Published
- 2011-06-01
- Volume
- 35
- Issue
- 2
- Pages
- 359-372
- ISSN
- 0276-7783 (ISSN-L)
- 2162-9730
- OpenAlex
- W3142030046
- Cited by
- 239
- References
- 3
- FWCI
- 16.91
- Type
- journal article
- Language
- en
- Open access
- closed
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