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Verhelst, Norman D. – Scandinavian Journal of Educational Research, 2012
When using IRT models in Educational Achievement Testing, the model is as a rule too simple to catch all the relevant dimensions in the test. It is argued that a simple model may nevertheless be useful but that it can be complemented with additional analyses. Such an analysis, called profile analysis, is proposed and applied to the reading data of…
Descriptors: Multidimensional Scaling, Profiles, Item Response Theory, Achievement Tests
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Vieillard, Sandrine; Guidetti, Michele – Journal of Experimental Child Psychology, 2009
The current study examined the abilities of children (6 and 8 years of age) and adults to freely categorize and label dynamic bodily/facial expressions designed to portray happiness, pleasure, anger, irritation, and neutrality and controlled for their level of valence, arousal, intensity, and authenticity. Multidimensional scaling and cluster…
Descriptors: Children, Classification, Labeling (of Persons), Multivariate Analysis
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Kiers, Henk A. L.; Vicari, Donatella; Vichi, Maurizio – Psychometrika, 2005
For the exploratory analysis of a matrix of proximities or (dis)similarities between objects, one often uses cluster analysis (CA) or multidimensional scaling (MDS). Solutions resulting from such analyses are sometimes interpreted using external information on the objects. Usually the procedures of CA, MDS and using external information are…
Descriptors: Classification, Multidimensional Scaling, Multivariate Analysis, Models
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Deng, Chi-Ping; Armstrong, Patrick Ian; Rounds, James – Journal of Vocational Behavior, 2007
Holland's [Holland, J. L. (1959). A theory of occupational choice. Journal of Counseling Psychology, 6, 35-45; Holland, J. L. (1997). Making vocational choices: A theory of vocational personalities and work environments (3rd ed.). Odessa, FL: Psychological Assessment Resources, Inc.] RIASEC types were initially developed using a restricted range…
Descriptors: Reputation, Career Choice, Multidimensional Scaling, Labor Market
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Peay, Edmund R. – Psychometrika, 1975
A class of closely related hierarchical grouping methods are discussed and a procedure which implements them in an integrated fashion is presented. These methods avoid some theoretical anomalies inherent in clustering and provide a framework for viewing partitioning and nonpartitioning grouping. Significant relationships between these methods and…
Descriptors: Classification, Cluster Grouping, Computer Programs, Data Analysis