An extension and application of the multitrait-multimethod matrix technique.

Mellon, Phyllis M.; Crano, William D.

doi:10.1037/0022-0663.69.6.716

Cited by 12 publications

(11 citation statements)

References 6 publications

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“…Remembering that in this analysis methods are actually occasions, this finding reflects the fact that the teacher ratings tended to order the children differently from year to year, whereas the standardized tests were stable over time. In two key respects, the present results resemble those obtained by Mellon and Crano (1977). Recall that they, too, found higher convergent validity, and lower method bias, for the standardized tests than for the teacher ratings.…”

Section: Resultssupporting

confidence: 85%

“…The results given in Table 2 shed no light on this possibility. Mellon and Crano (1977) have proposed an extension of the ANOVA approach that capitalizes on the availability of longitudinal data to address issues such as these, and their lead was followed in the second analysis.…”

Section: Resultsmentioning

confidence: 99%

“…Using formulas given by Kavanagh et al (1971) and by Mellon and Crano (1977), the information in each matrix was then expressed in terms of the mean squares of a three-way ANOVA, as shown in Table 2. Considering the subjects as random and traits and methods as fixed, and assuming no threeway interaction, the main effect of subjects and the interactions of subjects with The results for the three matrices are not entirely consistent, although the significant main effect of subjects for all 3 years indicates that the children were well differentiated by the six trait-method combinations (convergent validity as judged by the ANOVA criterion).…”

Section: Resultsmentioning

confidence: 99%

“…Specifically, the ANOVA criterion represents the overall amount of agreement on subjects over both methods and traits; i.e., the extent to which a general factor accounts for the entire MTMM matrix. Further, the ANOVA approach fails to specify which traits or methods meet or fail to meet the various criteria, although Mellon and Crano (1977), following Kavanagh et al (1971), have outlined an extension of the approach that overcomes this disadvantage. Finally, there is a problem in comparing the results of the ANOVA approach for different MTMM matrices, but, again, Kavanagh et al have suggested a refinement to the approach that makes such comparisons possible.…”

mentioning

confidence: 96%

“…A recent piece of research by Mellon and Crano (1977) applied the refined and extended ANOVA approach to investigate the pattern of relationships among three academic traits -arithmetic ability, reading ability, and general ability -as assessed by two methods -teacher ratings and standardized tests. Their data, which came from a 3-year study of a large sample of British elementary school children, were presented primarily to illustrate the application of the ANOVA approach, but the data are of interest in their own right.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Convergent and Discriminant Validation of Two Methods of Assessing Three Academic Traits

Silverstein¹,

Brownlee²,

Legutki³

et al. 1983

J Spec Educ

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A refined and extended analysis of variance (ANOVA) approach was used to investigate the pattern of relationships among arithmetic ability, reading ability, and general ability as assessed by teacher ratings and standardized tests. The subjects were 54 educable mentally retarded children who were rated and tested annually for 3 years.The tests proved to have much higher convergent and discriminant validity than the ratings, and to be much less affected by method bias. These differences are seen as reflecting the natural superiority of standardized tests over teacher ratings for most purposes.More than 20 years have passed since Campbell and Fiske (1959) introduced the notion of convergent and discriminant validation by the multitrait-multimethod (MTMM) matrix: a matrix containing the intercorrelations that result when each of several traits is measured by each of several methods. Essentially, their validity criteria were that measures of the same trait involving different methods should not only be substantially correlated, but that they should also correlate more highly than (a) measures of different traits involving different methods or (b) measures of different traits involving the same method. The first criterion is evidence of convergent validity, whereas the second and third provide evidence for discriminant validity. While noting that various statistical treatments for evaluating MTMM matrices might be developed, Campbell and Fiske chose to leave that challenging task to others. Nor have others been slow to take up the challenge; in a recent review of the literature, Schmitt, Coyle, and Saari (1977) illustrated six different approaches to the analysis of MTMM matrices and discussed their relative advantages and disadvantages.The approach employed in the present study is basically one proposed by Kavanagh, MacKinney, and Wolins (1971), following earlier work by Stanley (1961), Wolins (Note 1), and Zyzanski (1962). In this approach, the essential information in an MTMM matrix is expressed in terms of the usual mean squares of a three-way (subjects x traits x methods) factorial analysis of variance (ANOVA). Specifically, the main effect of subjects is taken to represent convergent validity;

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Section: Resultssupporting

confidence: 85%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 96%

mentioning

confidence: 99%

See 3 more Smart Citations

Convergent and Discriminant Validation of Two Methods of Assessing Three Academic Traits

Silverstein¹,

Brownlee²,

Legutki³

et al. 1983

J Spec Educ

View full text Add to dashboard Cite

show abstract

Student-teacher concordance on dimensions of student social competence: A multitrait-multimethod analysis

Byrne

Schneider

1986

J Psychopathol Behav Assess

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CFA Models with a General Factor and Multiple Sets of Secondary Factors

2018

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We propose a class of confirmatory factor analysis models that include multiple sets of secondary or specific factors and a general factor. The general factor accounts for the common variance among manifest variables, whereas multiple sets of secondary factors account for the remaining source-specific dependency among subsets of manifest variables. A special case of the model is further proposed which constrains the specific factor loadings to be proportional to the general factor loadings. This proportional model substantially reduces the number of model parameters while preserving the essential structure of the general model. Furthermore, the proportional model allows for the interpretation of latent variables as the expected values of the observed manifest variables, decomposition of the variances, and the inclusion of interactions, similar to generalizability theory. We provide two applications to illustrate the utility of the proposed class of models.

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An extension and application of the multitrait-multimethod matrix technique.

Cited by 12 publications

References 6 publications

Convergent and Discriminant Validation of Two Methods of Assessing Three Academic Traits

Convergent and Discriminant Validation of Two Methods of Assessing Three Academic Traits

Student-teacher concordance on dimensions of student social competence: A multitrait-multimethod analysis

CFA Models with a General Factor and Multiple Sets of Secondary Factors

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