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Is the Factor Observed in Investigations on the Item-Position Effect Actually the Difficulty Factor?
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Schweizer, Karl; Troche, Stefan – Educational and Psychological Measurement, 2018
In confirmatory factor analysis quite similar models of measurement serve the detection of the difficulty factor and the factor due to the item-position effect. The item-position effect refers to the increasing dependency among the responses to successively presented items of a test whereas the difficulty factor is ascribed to the wide range of…
Descriptors: Investigations, Difficulty Level, Factor Analysis, Models
Exploring the Latent Structure of the Luria Model for the KABC-II at School Age: Further Insights from Confirmatory Factor Analysis
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McGill, Ryan J. – Psychology in the Schools, 2017
The present study examined the factor structure of the Luria interpretive model for the Kaufman Assessment Battery for Children-Second Edition (KABC-II) with normative sample participants aged 7-18 (N = 2,025) using confirmatory factor analysis with maximum-likelihood estimation. For the eight subtest Luria configuration, an alternative…
Descriptors: Children, Intelligence Tests, Models, Factor Structure
Do Adaptive Representations of the Item-Position Effect in APM Improve Model Fit? A Simulation Study
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Zeller, Florian; Krampen, Dorothea; Reiß, Siegbert; Schweizer, Karl – Educational and Psychological Measurement, 2017
The item-position effect describes how an item's position within a test, that is, the number of previous completed items, affects the response to this item. Previously, this effect was represented by constraints reflecting simple courses, for example, a linear increase. Due to the inflexibility of these representations our aim was to examine…
Descriptors: Goodness of Fit, Simulation, Factor Analysis, Intelligence Tests
Identifying Useful Auxiliary Variables for Incomplete Data Analyses: A Note on a Group Difference Examination Approach
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Raykov, Tenko; Marcoulides, George A. – Educational and Psychological Measurement, 2014
This research note contributes to the discussion of methods that can be used to identify useful auxiliary variables for analyses of incomplete data sets. A latent variable approach is discussed, which is helpful in finding auxiliary variables with the property that if included in subsequent maximum likelihood analyses they may enhance considerably…
Descriptors: Data Analysis, Identification, Maximum Likelihood Statistics, Statistical Analysis
Joint Confirmatory Factor Analysis of the Woodcock-Johnson Tests of Cognitive Abilities, Third Edition, and the Stanford-Binet Intelligence Scales, Fifth Edition, with a Preschool Population
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Chang, Mei; Paulson, Sharon E.; Finch, W. Holmes; Mcintosh, David E.; Rothlisberg, Barbara A. – Psychology in the Schools, 2014
This study examined the underlying constructs measured by the Woodcock-Johnson Tests of Cognitive Abilities, Third Edition (WJ-III COG) and the Stanford-Binet Intelligence Scales, Fifth Edition (SB5), based on the Cattell-Horn-Carrol (CHC) theory of cognitive abilities. This study reports the results of the first joint confirmatory factor analysis…
Descriptors: Factor Analysis, Intelligence Tests, Preschool Children, Maximum Likelihood Statistics
Modeling Ability Differentiation in the Second-Order Factor Model
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Molenaar, Dylan; Dolan, Conor V.; van der Maas, Han L. J. – Structural Equation Modeling: A Multidisciplinary Journal, 2011
In this article we present factor models to test for ability differentiation. Ability differentiation predicts that the size of IQ subtest correlations decreases as a function of the general intelligence factor. In the Schmid-Leiman decomposition of the second-order factor model, we model differentiation by introducing heteroscedastic residuals,…
Descriptors: Factor Structure, Models, Intelligence Quotient, Correlation
Confirmatory Maximum Likelihood Factor Analysis as Applied to Measures of Social Intelligence within the Structure-of-Intellect Model.
Dunmire, Phyllisann M.; And Others – 1988
Confirmatory maximum likelihood factor analysis was used to determine how accurately each of several hypothesized combinations of first-order and/or higher-order factors could describe the covariation within selected sub-matrices taken from the total correlation matrix originally analyzed by M. O'Sullivan et al. (1965). Focus was on evaluating the…
Descriptors: Behavior Patterns, Factor Analysis, Intelligence Tests, Maximum Likelihood Statistics