A taxonomy of covariance structure models for representing
multitrait-multimethod data is presented. Using
this taxonomy, it is possible to formulate alternate
series of hierarchically ordered, or nested, models for
such data. By specifying hierarchically nested models,
significance tests of differences between competing
models are available. Within the proposed framework,
specific model comparisons may be formulated to test
the significance of the convergent and the discriminant
validity shown by a set of measures as well as the extent
of method variance. Application of the proposed
framework to three multitrait-multimethod matrices allowed
resolution of contradictory conclusions drawn in
previously published work, demonstrating the utility of
the present approach.
Widaman, Keith F. (1985). Hierarchically nested covariance structure models for multitrait-multimethod data. Applied Psychological Measurement, 9, 1-26. doi:10.1177/014662168500900101
Widaman, Keith F..
Hierarchically nested covariance structure models for multitrait-multimethod data.
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