Decomposing Achievement Test Scores into Measures of Cognitive and Noncognitive Skills
SSRN, ISSN: 1556-5068
2019
- 5Citations
- 1,794Usage
- 8Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Achievement test scores not only reflect students’ ability, knowledge and intelligence, but also their noncognitive skills such as personality traits and motivation. If multiple traits determine test scores, an important question is whether it is feasible to unravel the information from one test to get separate measures for different traits. The aim of this paper is to disentangle cognitive and noncognitive factors from the performance on a single test. We use the random allocation of test booklets to students, which generates exogenous variation in the position of questions in the test. We document that (1) performance declines during the test; (2) this decline differs from the performance at the start of the test; (3) these factors are very stable over time; (4) both factors are differently related to cognitive and noncognitive measures. Performance at the start of the test strongly relating to cognitive measures and openness, and the performance decline during the test relating to conscientiousness, agreeableness, and need for achievement; and (5) both factors predict life outcomes differently. Performance at the start of the test shows a stronger relation with being employed, being in good overall health and not smoking at age 34, whereas the performance decline during the test shows a stronger relation with being single, life satisfaction or drinking behavior at age 34. The paper shows that one achievement test can be used to measure different personal characteristics – both cognitive and noncognitive – enabling the investigation of the channels through which achievement tests predict future outcomes and providing an approach for objective measurement of personality.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85110555479&origin=inward; http://dx.doi.org/10.2139/ssrn.3414156; https://www.ssrn.com/abstract=3414156; https://dx.doi.org/10.2139/ssrn.3414156; https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3414156; https://ssrn.com/abstract=3414156
Elsevier BV
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