Convergent and Discriminant Validation by the Multitrait-Multimethod Matrix.

Campbell, D. T., & Fiske, D. W. (1959). Convergent and Discriminant Validation by the Multitrait-Multimethod Matrix. Psychological Bulletin, 56(2), 81-105.

Type

Journal article

Intellectual base

Primary research

Year

1959

DOI

10.1037/h0046016

Link

https://doi.org/10.1037/h0046016

Cited from

ethnography_qualitative_methods, statistics_experimental_design

Cited by

5 citations across 5 artifacts.

Each citation links to the sentence it supports in the citing article.

Primes

• Construct Validity
  • Convergent and discriminant validity together approximate a small factor-analytic argument: if everything correlates with everything, the proxy is not isolating the construct; if nothing correlates with anything, the proxy is not measuring a real construct at all.

    This sourceIntroduces the multitrait-multimethod matrix and the convergent/discriminant distinction for locating method-variance leaks.

• Correlated-Source Attribution Failure
  • The pattern compresses the per-substrate vocabularies of multicollinearity, common-method bias, common-cause inflation, ensemble redundancy, and group-think corroboration into one shape with a small, portable intervention catalogue. Decorrelate by design: instrumentation, randomisation, blinding, source-separation protocols, independent sampling, multi-trait/multi-method designs.

    This sourceIntroduces the multitrait-multimethod design to separate trait variance from shared-method variance — the decorrelate-by-design and shared-variance-quantification remedy. (

• Cross-Dimensional Leakage
  • Measurement and instrumentation: a single instrument's drift, calibration error, or operator effect contaminates multiple measured quantities; multi-trait/multi-method matrix designs were built to separate trait from method variance.

    This sourceIntroduces the MTMM matrix to separate trait variance from shared method variance — the canonical 'cross the channel, then partition' design, distinguishing same-method/different-trait from same-trait/different-method correlations.

• Triangulation
  • The use of multiple independent observers, coders, or analysts to verify the same observations, reducing observer bias

Mechanisms

• Multitrait-Multimethod Matrix
  • Its strength is that it needs no statistical model and exposes method variance a single-method study cannot see: it is the cleanest way to answer "would these constructs still look related if a different instrument had measured them?"

Verification

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