Using New Proximity Measures With Hierarchical Cluster Analysis to Detect Multidimensionality

Louis A. Roussos · William Stout · John I. Marden

100 citations0/0 files storedJournal of Educational Measurement

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Psychometric Methodologies and Testing Psychological Testing and Assessment Educational Assessment and Pedagogy

Abstract

A new approach for partitioning test items into dimensionally distinct item clusters is introduced. The core of the approach is a new item‐pair conditional‐covariance‐based proximity measure that can be used with hierarchical cluster analysis. An extensive simulation study designed to test the limits of the approach indicates that when approximate simple structure holds, the procedure can correctly partition the test into dimensionally homogeneous item clusters even for very high correlations between the latent dimensions. In particular, the procedure can correctly classify (on average) over 90% of the items for correlations as high as .9. The cooperative role that the procedure can play when used in conjunction with other dimensionality assessment procedures is discussed.

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References

1969Statistical Theories of Mental Test Scores 1904"General Intelligence," Objectively Determined and Measured 2012Applications of Item Response Theory To Practical Testing Problems 1968Some latent trait models and their use in inferring an examinee's ability 1953Assessing similarity between profiles.1997Local Dependence Indexes for Item Pairs Using Item Response Theory 1984Effects of Local Item Dependence on the Fit and Equating Performance of the Three-Parameter Logistic Model 1970Fitting a Response Model for n Dichotomously Scored Items

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