Date of Award

1-1-2019

Document Type

Dissertation

Degree Name

Ph.D.

Department

Quantitative Research Methods

First Advisor

Duan Zhang, Ph.D.

Keywords

Executive function, Latent change score, Latent growth curve, Math achievement

Abstract

The current study compared latent growth curve (LGC) models and latent change score (LCS) models capabilities in modeling complex data in a development framework. Using the nationally representative ECLS-K:2011 dataset, LGC and LCS models explored the dynamic relationship between executive function and math achievement. The relationship between the two constructs has been extensively examined but little is understood about their dynamic relationship. The findings of this study indicated LCS to be more robust than LGC in modeling complex data and in examining dynamic relationship. The findings also suggested that one of the two executive functioning tasks, Dimensional Change Card Sort (DCCS), which measures cognitive flexibility, was the leading indicator and math was lagging while math achievement was the leading indicator and number reverse (which was the other executive functioning task and measures working memory) was lagging. This finding was only possible using LCS models. The study also demonstrated that the two EF measures performed differently with number reverse performing worse than its counterpart.

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.

Provenance

Received from ProQuest

Rights holder

Kerry-Ann Lewis Lewis Pearcy

File size

159 p.

File format

application/pdf

Language

en

Discipline

Statistics, Mathematics education, Developmental psychology

Available for download on Monday, October 04, 2021

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