Research Symposium

BIO

Natalia Corral is a sophomore Cell and Molecular Neuroscience Major on the pre-med track. She has been working as a research assistant in the meta-analysis project on predictors of dyslexia since the Fall of 2025. She hopes to continue working in this project for the rest of her undergraduate career, and continue learning more effective ways of predicting, diagnosing, and treating such a common learning disability. Natalia has been honored to work as a research assistant under the faculty members of the Department of Psychology who are conducting this research; Dr. Dorota Kossowska-Kuhn, Dr. Richard Wagner, and Dr. Fotena Zirps, who have been incredible teachers and mentors. Additionally, she is an active member of Phi Delta Epsilon, a pre-medical fraternity and works as a medical scribe for Baptist Hospital Urgent Care. She plans to graduate with a Bachelor of Science in Neuroscience in the Spring of 2028 during her senior year, and begin Medical School in the Fall of 2028.

Dyslexia Meta-Analysis

Abstract

Dyslexia is defined by the International Dyslexia Association as a neurobiological developmental learning disorder that is characterised by having trouble decoding, recognizing, and spelling words. As much as this form of neurodivergence is common, the mode of diagnosis for developmental dyslexia seems to often fall short of its interventive power. Because previous research suggests that no single predictor works well for complex phenomena such as dyslexia, this project aims to analyze common predictors of dyslexia using a model based meta-analysis (MASM) of the current literature in the field. This meta-analysis seeks to consolidate thousands of articles from scholarly sources and scientific journals to further strengthen the idea that three predictors of dyslexia— phonological processing impairment, poor response to intervention, and family history of dyslexia— are reliable modes of early prediction and diagnosis. Facilitated by a team of several reviewers, each article is title and abstract screened, full text reviewed, and extracted to create a proportional model to illustrate the statistical relevance each factor has in relation to the prediction and diagnosis of dyslexia. Though still in progress, the meta-analysis has now advanced to the extraction phase and is working towards a definitive result.

Keywords: Meta Analysis Dyslexia Predictors