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

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  • 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

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    1. 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.
    2. Jorge Luis Garcia & Frederik Bennhoff & Duncan Ermini Leaf & James J. Heckman, 2021. "The Dynastic Benefits of Early Childhood Education," Working Papers 2021-033, Human Capital and Economic Opportunity Working Group.
    3. 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.
    4. 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, Institute of Labor Economics (IZA).
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    More about this item

    Keywords

    Abbreviated surveys; Big Five; Predictive power; Noncognitive skills; Personality traits;
    All these keywords.

    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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