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Jewsbury, Paul A.; Bowden, Stephen C. – Journal of Psychoeducational Assessment, 2017
Fluency is an important construct in clinical assessment and in cognitive taxonomies. In the Cattell-Horn-Carroll (CHC) model, Fluency is represented by several narrow factors that form a subset of the long-term memory encoding and retrieval (Glr) broad factor. The CHC broad classification of Fluency was evaluated in five data sets, and the CHC…
Descriptors: Memory, Construct Validity, Cognitive Processes, Factor Analysis
Oluwalana, Olasumbo O. – ProQuest LLC, 2019
A primary purpose of cognitive diagnosis models (CDMs) is to classify examinees based on their attribute patterns. The Q-matrix (Tatsuoka, 1985), a common component of all CDMs, specifies the relationship between the set of required dichotomous attributes and the test items. Since a Q-matrix is often developed by content-knowledge experts and can…
Descriptors: Classification, Validity, Test Items, International Assessment
Peer reviewedTakane, Yoshio; de Leeuw, Jan – Psychometrika, 1987
Equivalence of marginal likelihood of the two-parameter normal ogive model in item response theory and factor analysis of dichotomized variables was formally proved. Ordered and unordered categorical data and paired comparisons data were discussed, and a taxonomy of data for the models was suggested. (Author/GDC)
Descriptors: Classification, Factor Analysis, Latent Trait Theory, Mathematical Models
Beauducel, Andre; Herzberg, Philipp Yorck – Structural Equation Modeling: A Multidisciplinary Journal, 2006
This simulation study compared maximum likelihood (ML) estimation with weighted least squares means and variance adjusted (WLSMV) estimation. The study was based on confirmatory factor analyses with 1, 2, 4, and 8 factors, based on 250, 500, 750, and 1,000 cases, and on 5, 10, 20, and 40 variables with 2, 3, 4, 5, and 6 categories. There was no…
Descriptors: Factor Analysis, Maximum Likelihood Statistics, Classification, Sample Size
Peer reviewedOlsson, Ulf – Multivariate Behavioral Research, 1979
The paper discusses the consequences for maximum likelihood factor analysis which may follow if the observed variables are ordinal with only a few scale steps. Results indicate that classification may lead to a substantial lack of fit of the model--an erroneous indication that more factors are needed. (Author/CTM)
Descriptors: Classification, Factor Analysis, Goodness of Fit, Maximum Likelihood Statistics

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