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Shorter can be better: Balancing length and predictive power when measuring noncognitive skills to predict academic outcomes

Author

Listed:
  • Feng, Shuaizhang
  • Gan, Yu
  • Han, Yujie
  • Kautz, Tim

Abstract

We develop shorter versions of a Big Five survey designed to measure students’ noncognitive skills and predict students’ later academic outcomes. We find that measures with fewer items can better predict students’ outcomes, suggesting that using shorter versions of a Big Five Inventory may be cost-effective in large-scale social surveys.

Suggested Citation

  • Feng, Shuaizhang & Gan, Yu & Han, Yujie & Kautz, Tim, 2024. "Shorter can be better: Balancing length and predictive power when measuring noncognitive skills to predict academic outcomes," Economics Letters, Elsevier, vol. 236(C).
    • Handle: RePEc:eee:ecolet:v:236:y:2024:i:c:s0165176524000818
    DOI: 10.1016/j.econlet.2024.111598
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    References listed on IDEAS

    as
    1. García, Jorge Luis & Bennhoff, Frederik H. & Leaf, Duncan Ermini & Heckman, James J., 2021. "The Dynastic Benefits of Early Childhood Education," IZA Discussion Papers 14525, IZA Network @ LISER.
    2. Flavio Cunha & James J. Heckman & Susanne M. Schennach, 2010. "Estimating the Technology of Cognitive and Noncognitive Skill Formation," Econometrica, Econometric Society, vol. 78(3), pages 883-931, May.
    3. Kautz, Tim & Heckman, James J. & Diris, Ron & ter Weel, Bas & Borghans, Lex, 2014. "Fostering and Measuring Skills: Improving Cognitive and Non-Cognitive Skills to Promote Lifetime Success," IZA Discussion Papers 8696, IZA Network @ LISER.
    4. James Heckman & Rodrigo Pinto & Peter Savelyev, 2013. "Understanding the Mechanisms through Which an Influential Early Childhood Program Boosted Adult Outcomes," American Economic Review, American Economic Association, vol. 103(6), pages 2052-2086, October.
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    Cited by:

    1. Wu, Binglan & Feng, Shuaizhang & Zhang, Yijing & Han, Yujie, 2025. "Do children's time preferences predict future school outcomes?," China Economic Review, Elsevier, vol. 93(C).

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    Keywords

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    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • I20 - Health, Education, and Welfare - - Education - - - General

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