Element salience as a predictor of item difficulty for Raven's Progressive Matrices

Meo, Maria Grazia; Roberts, Maxwell J.; Marucci, Francesco S.

doi:10.1016/j.intell.2006.10.001

Cited by 39 publications

(35 citation statements)

References 14 publications

(29 reference statements)

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“…This claim is in line with research on the cognitive processes respondents use to solve such subtests (e.g. Arendasy & Sommer, 2005;Embretson & Gorin, 2001;Meo, Roberts & Marucci, 2007). However, the use of mixed measures turns the substantive interpretation of the extracted factors into a difficult enterprise and might even lead to biased results (cf.…”

Section: Reasons For the Mixed Evidence On The G-factor Saturationsupporting

confidence: 78%

Investigating the ‘g’-saturation of various stratum-two factors using automatic item generation

2008

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Section: Reasons For the Mixed Evidence On The G-factor Saturationsupporting

confidence: 78%

Investigating the ‘g’-saturation of various stratum-two factors using automatic item generation

2008

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“…This article attempts to account for rising scores on culture-free intelligence tests as a knowledge-based phenomenon by reconciling Flynn's (2007) proposal that rising scores were caused by improved abstract reasoning with insights and discoveries that have emerged from studies of matrix reasoning (e.g.. Carpenter et al, 1990;Embretson, 1998;Primi, 2002;Meo et al, 2007).…”

Section: Discussionmentioning

confidence: 99%

A knowledge-based theory of rising scores on “culture-free” tests.

Fox¹,

Mitchum

2013

Journal of Experimental Psychology: General

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Secular gains in intelligence test scores have perplexed researchers since they were documented by Flynn (1984, 1987). Gains are most pronounced on abstract, so-called culture-free tests, prompting Flynn (2007) to attribute them to problem-solving skills availed by scientifically advanced cultures. We propose that recent-born individuals have adopted an approach to analogy that enables them to infer higher level relations requiring roles that are not intrinsic to the objects that constitute initial representations of items. This proposal is translated into item-specific predictions about differences between cohorts in pass rates and item-response patterns on the Raven's Matrices (Flynn, 1987), a seemingly culture-free test that registers the largest Flynn effect. Consistent with predictions, archival data reveal that individuals born around 1940 are less able to map objects at higher levels of relational abstraction than individuals born around 1990. Polytomous Rasch models verify predicted violations of measurement invariance, as raw scores are found to underestimate the number of analogical rules inferred by members of the earlier cohort relative to members of the later cohort who achieve the same overall score. The work provides a plausible cognitive account of the Flynn effect, furthers understanding of the cognition of matrix reasoning, and underscores the need to consider how test-takers select item responses.

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“…In addition, the logic problems are constructed by overlapping shapes on top of one another. This overlapping may reduce the salience of the shapes and makes the combination of shapes difficult to name, and both of these effects can make items more difficult (Meo, Roberts, & Marucci, 2007;Roberts, Welfare, Livermore, & Theadom, 2000).…”

Section: Comparison Of Raven's Matrices and The Sandia Matricesmentioning

confidence: 99%

Recreating Raven’s: Software for systematically generating large numbers of Raven-like matrix problems with normed properties

Matzen

Benz

Dixon

et al. 2010

Behavior Research Methods

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Raven's Progressive Matrices and similar matrix problems have been used in research and intelligence testing for decades. The matrix problems serve as a nonverbal test of analogical reasoning and are thought to measure analytical intelligence (Carpenter, Just, & Shell, 1990), also known as fluid intelligence (cf. Cattell, 1963). Although these matrix problems have a wide variety of research applications, the relatively small number of matrices in Raven's original sets (108 total;Raven, Court, & Raven, 1998) limits their utility in several domains, including neuroimaging experiments and computational modeling of cognitive processes.Our goal in the present study was to create and characterize a very large set of matrix problems that have properties similar to those of Raven's original matrices. We sought to create the matrix set in a systematic way that would allow researchers to have a great deal of control over the underlying structure, surface features, and difficulty of the matrix problems. This in turn would allow researchers to systematically expand the range of difficulty in their stimulus sets beyond the range provided by the original Raven's matrices. To accomplish these goals, we analyzed the underlying structures in Raven's original Standard Progressive Matrices (SPMs) to determine what types and combinations of relations were used. On the basis of that analysis, we developed software that can use the same underlying patterns to generate large numbers of unique matrix problems using parameters chosen by the researcher. Specifically, the software is designed so that researchers can choose the type, direction, and number of relations in a problem and create any number of unique matrices that share the same underlying structure (e.g., changes in numerosity in a diagonal pattern) but have different surface features (e.g., shapes, colors).Finally, we used the matrix generation software to produce a representative set of matrix problems that cover the range of underlying structures that can be produced by the software. This set of matrices was compared with Raven's SPMs in a norming study. The first goal of the norming study was to compare the difficulty of the generated matrices with the difficulty of the SPMs with the same underlying structure. The second goal was to assess the difficulty of specific structural features within the matrices and the range of problem difficulties that can be produced by the matrix generation software when those features are combined. Analysis of Raven's Progressive Matrix StructuresPrevious studies have analyzed the factors that contribute to the difficulty of Raven and Raven-like matrix problems. Raven's Progressive Matrices is a widely used test for assessing intelligence and reasoning ability (Raven, Court, & Raven, 1998). Since the test is nonverbal, it can be applied to many different populations and has been used all over the world (Court & Raven, 1995). However, relatively few matrices are in the sets developed by Raven, which limits their use in experiments requiring l...

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Element salience as a predictor of item difficulty for Raven's Progressive Matrices

Cited by 39 publications

References 14 publications

Investigating the ‘g’-saturation of various stratum-two factors using automatic item generation

Investigating the ‘g’-saturation of various stratum-two factors using automatic item generation

A knowledge-based theory of rising scores on “culture-free” tests.

Recreating Raven’s: Software for systematically generating large numbers of Raven-like matrix problems with normed properties

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