The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Manifest variable path analysis: Potentially serious and misleading consequences due to uncorrected measurement error.
Abstract
Despite clear evidence that manifest variable path analysis requires highly reliable measures, path analyses with fallible measures are commonplace even in premier journals. Using fallible measures in path analysis can cause several serious problems: (a) As measurement error pervades a given data set, many path coefficients may be either over- or underestimated. (b) Extensive measurement error diminishes power and can prevent invalid models from being rejected. (c) Even a little measurement error can cause valid models to appear invalid. (d) Differential measurement error in various parts of a model can change the substantive conclusions that derive from path analysis. (e) All of these problems become increasingly serious and intractable as models become more complex. Methods to prevent and correct these problems are reviewed. The conclusion is that researchers should use more reliable measures (or correct for measurement error in the measures they do use), obtain multiple measures for use in latent variable modeling, and test simpler models containing fewer variables.
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Self-citationHeene, M., Hilbert, S., Draxler, C., Ziegler, M., & Bühner, M. (2011) · Psychological Methods · doi:10.1037/a0024917 · Cited by 406
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“Since the method provides a more precise measurement of theoretical concepts [46], it is ideal for the correlation analysis targeted by the research.”
Metadata
- Authors
- Cole, D. A., & Preacher, K. J.
- Venue
- Psychological Methods
- Published
- 2014
- Volume
- 19
- Issue
- 2
- Pages
- 300-315
- ISSN
- 1082-989X (ISSN-L)
- 1939-1463
- OpenAlex
- W2108442198
- Cited by
- 462
- References
- 4
- FWCI
- 25.84
- Type
- journal article
- Language
- en
- Open access
- closed
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