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140122 ||| eng |
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|a 9781461396901
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| 100 |
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|a Bejar, Isaac I.
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| 245 |
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|a Cognitive and Psychometric Analysis of Analogical Problem Solving
|h Elektronische Ressource
|c by Isaac I. Bejar, Roger Chaffin, Susan Embretson
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| 250 |
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|a 1st ed. 1991
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| 260 |
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|a New York, NY
|b Springer New York
|c 1991, 1991
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| 300 |
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|a XIV, 237 p
|b online resource
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| 505 |
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|a 1: Introduction -- Objectives of the Project and Overview of the Report -- 2: Theories of Memory Representation and Analogical Reasoning -- A Normative Model -- Words and Concepts -- Reasoning with Analogies -- Summary -- 3: A Taxonomy of Semantic Relations -- The Taxonomy -- A Higher-Order Classification of Semantic Relations -- Empirical Evidence for the Dichotomy -- Factor Analytic Evidence -- Earlier Work on Cognitive Dictionaries -- Uses of the Taxonomy -- 4: Description of the Item Pool -- Distribution of Deltas and r-Biserial -- Distribution According to Test Development Taxonomies -- Distribution According to Relational Taxonomy -- Relationship of the Taxonomy of Semantic Relations to Other Test Development Taxonomies -- Summary -- 5: The Effect of Vocabulary Level and Rationale Complexity on Item Difficulty -- Data and Procedures -- Regression of Delta on Stem and Key Frequency for All Items -- The Role of Complexity in Difficulty -- Summary -- 6: The Relationship Between Delta and r-Biserial -- The Negative Relationship As an Artifact -- The Relationship of Delta and r-Biserial for Other GRE Items -- Other Data Sets -- Recomputing r-Biserial -- Polyserial Analysis -- Summary -- 7: Expert Analyses of Analogy Items -- Method -- Results -- Summary -- 8: Cognitive Processing and Item Difficulty -- Relationship Between Processing Demands and Psychometric Difficulty -- Subjects and Method for Experiments 1 and 2 -- Experiment 1 -- Experiment 2 -- Lexical Overlap -- Practical Value of Process Variables in Modeling Difficulty -- Summary -- 9: Cross Validation: Analysis of Pretested Items -- Establishing Criteria -- Regression Results -- A Practical Assessment of Utility -- Predicting Low r-Biserials -- Summary -- 10: Summary and Conclusions -- The Study in Perspective -- Systematic Item Writing -- Psychological ResponseModeling -- Processing Models -- Practical Considerations -- Further Research -- References -- Author Index
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| 653 |
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|a Psychoanalysis
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| 653 |
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|a Cognitive Psychology
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| 653 |
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|a Cognitive psychology
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| 700 |
1 |
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|a Chaffin, Roger
|e [author]
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| 700 |
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|a Embretson, Susan
|e [author]
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b SBA
|a Springer Book Archives -2004
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| 490 |
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|a Recent Research in Psychology
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| 028 |
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|a 10.1007/978-1-4613-9690-1
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| 856 |
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|u https://doi.org/10.1007/978-1-4613-9690-1?nosfx=y
|x Verlag
|3 Volltext
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|a 153
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|a If one were to conduct an analysis of any profession the "ability to think analogically" is more than likely to be one of the requirements for success, be it an architectural studio, a research laboratory, a legal office, or a nuclear plant. Cognitive scientists are aware of the prominence of analogical reasoning in all forms of reasoning and learning, and have devoted substantial effort to ascer taining its nature. Test builders, like cognitive scientists, are aware of the cen trality of analogical reasoning and figure, correctly, that a test that samples a student's ability to think analogically may well be a good predictor of success in a variety of fields. This book is the result of a project to investigate analogical reasoning from both an individual differences and a cognitive perspective. The book is directed to both researchers and practitioners concerned with the nature and measurement of analogical reasoning. Cognitive scientists, linguists, psycholinguists, and natural language researchers will find the seman tic taxonomy and accompanying empirical results food for thought. Test devel opers will fmd it reassuring that performance on verbal analogy items is not just a reflection of the size of a person's vocabulary, and that tests can be designed according to principles, rather than assembled to satisfy a set of statistical speci fications. Psychometricians will find that content and response modelling can go together and that there are distinct benefits in approaching psychometric re sponse modelling from that integrative perspective
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