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Multitrait-multimethod matrix

Statistical technique used to examine construct validity

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Definitions and key components

Multiple traits are used in this approach to examine (a) similar or (b) dissimilar traits (constructs), in order to establish convergent and discriminant validity between traits. Similarly, multiple methods are used in this approach to examine the differential effects (or lack thereof) caused by method specific variance. Scores could be correlated because they measure similar traits, or because they are based on similar methods, or both. When variables that are supposed to measure different constructs show a high correlation because they based on similar methods, this is sometimes described as a "nuisance variance" or "method bias" problem.

There are six major considerations when examining a construct's validity through the MTMM matrix, which are as follows:

  1. Evaluation of convergent validity – Tests designed to measure the same construct should correlate highly amongst themselves.
  2. Evaluation of discriminant (divergent) validity – The construct being measured by a test should not correlate highly with different constructs.
  3. Trait-method unit- Each task or test used in measuring a construct is considered a trait-method unit; in that the variance contained in the measure is part trait, and part method. Generally, researchers desire low method-specific variance and high trait variance.
  4. Multitrait-multimethod – More than one trait and more than one method must be used to establish (a) discriminant validity and (b) the relative contributions of the trait or method-specific variance. This tenet is consistent with the ideas proposed in Platt's concept of Strong inference (1964).
  5. Truly different methodology – When using multiple methods, one must consider how different the actual measures are. For instance, delivering two self-report measures are not truly different measures; whereas using an interview scale or a psychosomatic reading would be.
  6. Trait characteristics – Traits should be different enough to be distinct, but similar enough to be worth examining in the MTMM.

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