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# The Multitrait-Multimethod Matrix

## What is the Multitrait-Multimethod Matrix?

The Multitrait-Multimethod Matrix (hereafter labeled MTMM) is an approach to assessing
the construct validity of a set of measures in a study. It was developed in 1959 by
Campbell and Fiske (Campbell, D. and Fiske, D. (1959). Convergent and discriminant
validation by the multitrait-multimethod matrix. 56, 2, 81-105.) in part as an attempt to
provide a practical methodology that researchers could actually use (as opposed to the nomological network idea which was theoretically useful but did not
include a methodology). Along with the MTMM, Campbell and Fiske introduced two new types
of validity -- convergent and discriminant -- as subcategories
of construct validity. **Convergent validity** is the degree
to which concepts that should be related theoretically are interrelated in reality. **Discriminant
validity** is the degree to which concepts that should *not* be related
theoretically are, in fact, *not* interrelated in reality. You can assess both
convergent and discriminant validity using the MTMM. In order to be able to claim that
your measures have construct validity, you have to demonstrate both convergence and
discrimination.

The MTMM is simply a matrix or table of correlations arranged to facilitate the
interpretation of the assessment of construct validity. The MTMM assumes that you measure
each of several concepts (called *traits *by Campbell and Fiske) by each of several
methods (e.g., a paper-and-pencil test, a direct observation, a performance measure). The
MTMM is a very restrictive methodology -- ideally you should measure *each *concept
by *each *method.

To construct an MTMM, you need to arrange the correlation matrix by
concepts within
methods. The figure shows an MTMM for three concepts (traits
A, B and
C) each of which is
measured with three different methods (1,
2 and 3) Note that you lay the matrix out in
blocks by *method*. Essentially, the MTMM is just a correlation matrix between your
measures, with one exception -- instead of 1's along the diagonal (as in the typical
correlation matrix) we substitute an estimate of the reliability of each measure as the
diagonal.

Before you can interpret an MTMM, you have to understand how to identify the different parts of the matrix. First, you should note that the matrix is consists of nothing but correlations. It is a square, symmetric matrix, so we only need to look at half of it (the figure shows the lower triangle). Second, these correlations can be grouped into three kinds of shapes: diagonals, triangles, and blocks. The specific shapes are:

**The Reliability Diagonal**

(monotrait-monomethod)

Estimates of the reliability of each measure in the matrix. You can estimate reliabilities a number of different ways (e.g., test-retest, internal consistency). There are as many correlations in the reliability diagonal as there are measures -- in this example there are nine measures and nine reliabilities. The first reliability in the example is the correlation of Trait A, Method 1 with Trait A, Method 1 (hereafter, I'll abbreviate this relationship A1-A1). Notice that this is essentially the correlation of the measure with itself. In fact such a correlation would always be perfect (i.e., r=1.0). Instead, we substitute an estimate of reliability. You could also consider these values to be monotrait-monomethod correlations.

**The Validity Diagonals**

(monotrait-heteromethod)

Correlations between measures of the same trait measured using different methods. Since the MTMM is organized into method blocks, there is one validity diagonal in each method block. For example, look at the A1-A2 correlation of .57. This is the correlation between two measures of the same trait (A) measured with two different measures (1 and 2). Because the two measures are of the same trait or concept, we would expect them to be strongly correlated. You could also consider these values to be monotrait-heteromethod correlations.

**The Heterotrait-Monomethod Triangles**

These are the correlations among measures that share the same method of measurement. For instance, A1-B1 = .51 in the upper left heterotrait-monomethod triangle. Note that what these correlations share is method, not trait or concept. If these correlations are high, it is because measuring different things with the same method results in correlated measures. Or, in more straightforward terms, you've got a strong "methods" factor.

**Heterotrait-Heteromethod Triangles**

These are correlations that differ in both trait and method. For instance, A1-B2 is .22 in the example. Generally, because these correlations share neither trait nor method we expect them to be the lowest in the matrix.

**The Monomethod Blocks**

These consist of all of the correlations that share the same method of measurement. There are as many blocks as there are methods of measurement.

**The Heteromethod Blocks**

These consist of all correlations that do

notshare the same methods. There are (K(K-1))/2 such blocks, where K = the number of methods. In the example, there are 3 methods and so there are (3(3-1))/2 = (3(2))/2 = 6/2 = 3 such blocks.

## Principles of Interpretation

Now that you can identify the different parts of the MTMM, you can begin to understand
the rules for interpreting it. You should realize that MTMM interpretation requires the
researcher to use judgment. Even though some of the principles may be violated in an MTMM,
you may still wind up concluding that you have fairly strong construct validity. In other
words, you won't necessarily get *perfect* adherence to these principles in applied
research settings, even when you do have evidence to support construct validity. To me,
interpreting an MTMM is a lot like a physician's reading of an x-ray. A practiced eye can
often spot things that the neophyte misses! A researcher who is experienced with MTMM can
use it identify weaknesses in measurement as well as for assessing construct validity.

To help make the principles more concrete, let's make the example a bit more realistic. We'll imagine that we are going to conduct a study of sixth grade students and that we want to measure three traits or concepts: Self Esteem (SE), Self Disclosure (SD) and Locus of Control (LC). Furthermore, let's measure each of these three different ways: a Paper-and-Pencil (P&P) measure, a Teacher rating, and a Parent rating. The results are arrayed in the MTMM. As the principles are presented, try to identify the appropriate coefficients in the MTMM and make a judgement yourself about the strength of construct validity claims.

The basic principles or rules for the MTMM are:

- Coefficients in the reliability diagonal should consistently be the highest in the matrix.

That is, a trait should be more highly correlated with itself than with anything else! This is uniformly true in our example.

- Coefficients in the validity diagonals should be significantly different from zero and high enough to warrant further investigation.

This is essentially evidence of convergent validity. All of the correlations in our example meet this criterion.

- A validity coefficient should be higher than values lying in its column and row in the same heteromethod block.

In other words, (SE P&P)-(SE Teacher) should be greater than (SE P&P)-(SD Teacher), (SE P&P)-(LC Teacher), (SE Teacher)-(SD P&P) and (SE Teacher)-(LC P&P). This is true in all cases in our example.

- A validity coefficient should be higher than all coefficients in the heterotrait-monomethod triangles.

This essentially emphasizes that trait factors should be stronger than methods factors. Note that this is

nottrue in all cases in our example. For instance, the (LC P&P)-(LC Teacher) correlation of .46 is less than (SE Teacher)-(SD Teacher), (SE Teacher)-(LC Teacher), and (SD Teacher)-(LC Teacher) -- evidence that there might me a methods factor, especially on the Teacher observation method.

- The same
*pattern*of trait interrelationship should be seen in all triangles.

The example clearly meets this criterion. Notice that in all triangles the SE-SD relationship is approximately twice as large as the relationships that involve LC.

## Advantages and Disadvantages of MTMM

The MTMM idea provided an operational methodology for assessing construct validity. In the one matrix it was possible to examine both convergent and discriminant validity simultaneously. By its inclusion of methods on an equal footing with traits, Campbell and Fiske stressed the importance of looking for the effects of how we measure in addition to what we measure. And, MTMM provided a rigorous framework for assessing construct validity.

Despite these advantages, MTMM has received little use since its introduction in 1959.
There are several reasons. First, in its purest form, MTMM requires that you have a
fully-crossed measurement design -- each of several traits is measured by each of several
methods. While Campbell and Fiske explicitly recognized that one could have an incomplete
design, they stressed the importance of multiple replication of the same trait across
method. In some applied research contexts, it just isn't possible to measure all traits
with all desired methods (would you use an "observation" of weight?). In most
applied social research, it just wasn't feasible to make methods an explicit part of the
research design. Second, the judgmental nature of the MTMM may have worked against its
wider adoption (although it should actually be perceived as a strength). many researchers
wanted a test for construct validity that would result in a single statistical coefficient
that could be tested -- the equivalent of a reliability coefficient. It was impossible
with MTMM to quantify the *degree* of construct validity in a study. Finally, the
judgmental nature of MTMM meant that different researchers could legitimately arrive at
different conclusions.

## A Modified MTMM -- Leaving out the Methods Factor

As mentioned above, one of the most difficult aspects of MTMM from an implementation point of view is that it required a design that included all combinations of both traits and methods. But the ideas of convergent and discriminant validity do not require the methods factor. To see this, we have to reconsider what Campbell and Fiske meant by convergent and discriminant validity.

## What is convergent validity?

It is the principle that *measures of theoretically similar constructs should be
highly intercorrelated*. We can extend this idea further by thinking of a measure that
has multiple items, for instance, a four-item scale designed to measure self-esteem. If
each of the items actually does reflect the construct of self-esteem, then we would expect
the items to be highly intercorrelated as shown in the figure. These strong
intercorrelations are evidence in support of convergent validity.

## And what is discriminant validity?

It is the principle that *measures of theoretically different constructs should not
correlate highly with each other*. We can see that in the example that shows two
constructs -- self-esteem and locus of control -- each measured in two instruments. We
would expect that, because these are measures of different constructs, the cross-construct
correlations would be low, as shown in the figure. These low correlations are evidence for
validity. Finally, we can put this all together to see how we can address both convergent
and discriminant validity simultaneously. Here, we have two constructs -- self-esteem and
locus of control -- each measured with three instruments. The red and green correlations
are within-construct ones. They are a reflection of convergent validity and should be
strong. The blue correlations are cross-construct and reflect discriminant validity. They
should be uniformly lower than the convergent coefficients.

The important thing to notice about this matrix is that *it does not
explicitly include a methods factor* as a true MTMM would. The matrix examines both
convergent and discriminant validity (like the MTMM) but it only explicitly looks at
construct intra- and interrelationships. We can see in this example that the MTMM idea
really had two major themes. The first was the idea of looking simultaneously at the
pattern of convergence and discrimination. This idea is similar in purpose to the notions
implicit in the nomological network -- we are looking at the
pattern of interrelationships based upon our theory of the nomological net. The second
idea in MTMM was the emphasis on methods as a potential **confounding factor**.

While methods may confound the results, they won't necessarily do so in any given study.
And, while we need to examine our results for the potential for methods factors, it may be
that combining this desire to assess the confound with the need to assess construct
validity is more than one methodology can feasibly handle. Perhaps if we split the two
agendas, we will find that the possibility that we can examine convergent and discriminant
validity is greater. But what do we do about methods factors? One way to deal with them is
through replication of research projects, rather than trying to incorporate a methods test
into a single research study. Thus, if we find a particular outcome in a study using
several measures, we might see if that same outcome is obtained when we replicate the
study using different measures and methods of measurement for the same constructs. The
methods issue is considered more as an issue of generalizability (across measurement
methods) rather than one of construct validity.

When viewed this way, we have moved from the idea of a MTMM to that of the multitrait matrix that enables us to examine convergent and discriminant validity, and hence construct validity. We will see that when we move away from the explicit consideration of methods and when we begin to see convergence and discrimination as differences of degree, we essentially have the foundation for the pattern matching approach to assessing construct validity.

Copyright �2006, William M.K. Trochim, All Rights Reserved

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Last Revised: 10/20/2006