Executive Function and Academic Achievement: Bidirectional or Unidirectional? A Conceptual Replication of Schmitt et al. (2017)

• In Event: Unpacking the Complexities of Bi-Directional Associations between Executive Functions and Math

Abstract

Research spanning multiple disciplines has demonstrated associations between children’s executive function (EF) skills and early academic achievement (e.g., Best, Miller, & Naglieri, 2011; Bull et al., 2008). However, the nature of these relations has become a topic of debate in developmental and educational literature recently (see Peng & Kievit, 2020 for review). In particular, research in this area has treated EF as foundational for academic skill development given its support of learning and adaptation to early school settings (e.g., Blair, 2002; Zelazo, Blair, & Willoughby, 2016). Recent work, however, examining bidirectional links between EF skills and academic achievement have challenged the predominant unidirectional perspective of EF supporting academic skill development, introducing the theory of mutualism (e.g., Fuhs et al., 2014; Schmitt et al., 2017; Welsh et al., 2010). Findings from these bidirectional studies have shown mixed results, creating unclear takeaways for directionality theories. For example, Welsh and colleagues (2010) found bidirectional relations between EF and numeracy skills (but not literacy skills) during preschool, whereas other work has shown that EF prospectively predicts math and literacy achievement from preschool to kindergarten (Fuhs et al., 2014). Additionally, studies examining reciprocal relations using more than two time-points have yielded different directional patterns across early development. For example, Schmitt et al. (2017) found bidirectional relations between EF and math across the preschool school year and unidirectional associations from EF to math across kindergarten.

Therefore, the goal of the current study was to conduct a conceptual replication of the autoregressive cross-lagged models reported in Schmitt et al. (2017) in two independent longitudinal samples (N (STAR) = 279, and N (Pathways) = 277). We chose to replicate Schmitt et al. (2017) because of the significant overlap in the EF measures used in the original study (see Table 1), the developmental timing of measurement (preschool through the end of kindergarten), and the specific focus on subtests from the Woodcock-Johnson III Tests of Academic Achievement (Woodcock, McGrew, & Mather, 2001). After establishing longitudinal measurement invariance for the EF factors, we specified a longitudinal structural equation model with math and literacy as manifest variables.

Consistent with Schmitt et al. (2017), EF predicted subsequent math achievement in both samples (see Table 2). In the Pathways sample, EF at kindergarten predicted both math (β(SE) = .66(.17), p < .001) and literacy achievement (β(SE) = .33(.13), p < .05) in first grade. In the STAR sample, EF predicted math achievement from preschool to kindergarten (β(SE) = .84(.39)p < .05), and kindergarten to first grade (β(SE) = .39 (.15), p < .01). However, contrary to the original findings, math achievement did not significantly predict later EF in either sample.

The lack of bidirectional relations between EF and math in both samples does not support the theory of mutualism between these two constructs. In sum, the results of the present conceptual replication were mixed. The current conceptual replication has revealed that slight differences in EF measurement type, model specification, and developmental timing can contribute to the divergence of findings across this area of research.

Authors

• Alexa Ellis, University of Alabama, Tuscaloosa
  Presenting Author

• Sammy Ahmed, University of Rhode Island
  Non-Presenting Author

• Selin Zeytinoglu, University of Maryland - College Park
  Non-Presenting Author

• Elif Isbell, University of California Merced
  Non-Presenting Author

• Esther M. Leerkes, UNC Greensboro
  Non-Presenting Author

• Jennie K Grammer, University of Virginia - Charlottesville
  Non-Presenting Author

• William Gehring, University of Michigan
  Non-Presenting Author

• Frederick J Morrison, University of Michigan - Ann Arbor
  Non-Presenting Author

• Pamela Davis-Kean, University of Michigan - Ann Arbor
  Non-Presenting Author