Predicted percentage effects on adult earnings of early childhood programmes, based on test scores versus adult outcomes

The current article reexamines the correlation between achievement test scores and earnings by providing new evidence on the association between academic skills and measures of adult earnings assessed when participants were in their 30s, 40s, and 50s. Results suggest that math and reading scores are strong predictors of economic attainment throughout participants’ careers, but these associations may also be sensitive to controls for other characteristics—including measures of the early family environment, general cognitive functioning, and socioemotional skills. Although these associations demonstrate the likely importance of achievement skills in determining labor market productivity, the variability in the achievement-to-earnings correlation suggests that researchers should apply caution when using the correlation to project the long-run effects of educational interventions.

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Adult outcomes as a function of an early childhood educational program: An Abecedarian Project follow-up.

Developmental Psychology ◽

10.1037/a0026644 ◽

2012 ◽

Vol 48 (4) ◽

pp. 1033-1043 ◽

Cited By ~ 144

Author(s):

Frances A. Campbell ◽

Elizabeth P. Pungello ◽

Margaret Burchinal ◽

Kirsten Kainz ◽

Yi Pan ◽

...

Keyword(s):

Early Childhood ◽

Educational Program ◽

Adult Outcomes

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Early Childhood Programs Change Test Scores but Do They Change Brain Activity?

AEA Randomized Controlled Trials ◽

10.1257/rct.7662-1.0 ◽

2021 ◽

Author(s):

Karen Ye

Keyword(s):

Early Childhood ◽

Test Scores ◽

Brain Activity ◽

Early Childhood Programs

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Insights Into Young Children's Coding With Data Analytics

Teaching Computational Thinking and Coding to Young Children - Advances in Early Childhood and K-12 Education ◽

10.4018/978-1-7998-7308-2.ch015 ◽

2021 ◽

pp. 295-317

Author(s):

Apittha Unahalekhaka ◽

Jessica Blake-West ◽

XuanKhanh Nguyen

Keyword(s):

Early Childhood ◽

Science Education ◽

Computer Science ◽

Test Scores ◽

Learning Analytics ◽

Computer Science Education ◽

Computational Thinking ◽

Future Directions ◽

Research In Education ◽

Educational Field

Over the past decade, there has been a growing interest in learning analytics for research in education and psychology. It has been shown to support education by predicting learning performances such as school completion and test scores of students in late elementary and above. In this chapter, the authors discuss the potential of learning analytics as a computational thinking assessment in early childhood education. They first introduce learning analytics by discussing its various applications and the benefits and limitations that it offers to the educational field. They then provide examples of how learning analytics can deepen our understanding of computational thinking through observing young children's engagement with ScratchJr: a tablet coding app designed for K-2 students. Finally, they close this chapter with future directions for using learning analytics to support computer science education.

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Measuring Unobserved Variables in Educational Inequality Research: Mathematics and Language 'Abilities' in Early Childhood

10.31235/osf.io/tyhrg ◽

2019 ◽

Author(s):

Alejandra Rodríguez Sánchez

Keyword(s):

Early Childhood ◽

Measurement Error ◽

Cognitive Abilities ◽

Test Scores ◽

Social Inequalities ◽

Educational Inequality ◽

Theory Of Measurement ◽

Measurement Model ◽

Measurement Models ◽

Validation Framework

Evidence of social inequalities in cognitive abilities in early childhood has been documented in many societies; however, three characteristics of the data used to measure cognitive constructs make it difficult to quantify inequalities across groups. First, a causal understanding of validity is not compatible with the standard validation framework, which forces researchers to think critically what it means to measure unobserved constructs. Second, test scores only provide ordinal information about individuals, they are not interval scales and require the use of suitable corresponding methods for their study. Third, measurement invariance, which causes measurement error, may make comparison of test scores across groups invalid. The paper explores these three data problems applied to standardized tests---one mathematics and two language assessments---taken by a cohort of German children. The paper proposes a comparative validation framework for researchers based on nonparametric psychometric models and the representational theory of measurement. This framework can help researchers to determine if data fit the assumptions of a measurement model, to check for various forms of measurement error, and to overcome potential issues. A comparison of competing statistical modeling alternatives reveals substantial differences: By conceptualizing ability as ordinal instead of interval and excluding items that do not fit the assumptions of measurement models, I find a reduction in effect sizes for typical covariates studied in social stratification research.

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