Multitrait-multimethod matrix
From Wikipedia, the free encyclopedia
The multitrait-multimethod (MTMM) matrix is an approach to examining Construct Validity developed by Campbell and Fiske(1959)[1]. There are six major considerations when examining a constructs 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 validity — The construct being measured by a test should not correlate highly with a 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. Multi-method/trait — 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).[2]
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.
Contents |
[edit] Multitrait
Multiple traits are used in this approach to examine (a) similar or (b) dissimilar traits, as to establish convergent and discriminant validity amongst traits.
[edit] Multimethod
Similarly, multiple methods are used in this approach to examine the differential effects (or lack thereof) caused by method specific variance.
[edit] Example
The example below provides a prototypical matrix and what the correlations between measures mean. The diagonal line is typically filled in with a reliability coefficient of the measure (e.g. alpha coefficient).
| Test | Beck Depression Inv | Beck Anxiety Inv | Hepner Depression Interview | Hepner Anxiety Interview |
|---|---|---|---|---|
| BDI | (Reliability Coefficient) | |||
| BAI | Mono Method, Hetero Trait | (Reliability Coefficient) | ||
| HDIv | Hetero Method, Mono Trait | Hetero Method, Hetero Trait | (Reliability Coefficient) | |
| HAIv | Hetero Method, Hetero Trait | Hetero Method, Mono Trait | Mono Method, Hetero Trait | (Reliability Coefficient) |
In this example the first row and the first column display the trait being assessed (i.e. anxiety or depression) as well as the method of assessing this trait (i.e. interview or survey as measured by fictitious measures). The term Hetero Method indicates that in this cell the correlation between two separate methods is being reported. Mono Method indicates the opposite, in that the same method is being used (e.g. interview, interview). Hetero Trait indicates that the cell is reporting two supposedly different traits. Mono Method indicates the opposite- that the same trait is being used.
In evaluating an actual matrix one wishes to examine the proportion of variance shared amongst traits and methods as to establish a sense of how much method specific variance is induced by the measurement method, as well as provide a look at how unique the trait is, as compared to another trait.
[edit] CFA
With the advent of higher powered computors and more advance statistical techniques, confirmatory factor analysis has taken much of the role that the MTMM matrix once attempted to accomplish [3].
[edit] Notes references
- ^ Campbell, D.T., & FiskeD.W. (1959) Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56, 81-105 "
- ^ John R. Platt (1964). "Strong inference". Science 146 (3642).
- ^ Figueredo, A., Ferketich, S., Knapp, T.,(1991) Focus on psychometrics: More on MTMM:The Role of Confirmatory Factor Analysis. Nursing & Health, 14, 387-391
|
||||||||||||||||||||
