Abstract

Socioeconomic Status (SES) is widely used to predict child outcomes, yet its measurement varies widely across studies. This study examines Coleman’s (1998) theoretical model of SES, comprising of Human, Financial and Social Capital, using Confirmatory Factory Analysis (CFA) and Exploratory Structural Equation Modeling (ESEM). Additionally, it evaluates the model’s ability to predict vocabulary scores and aims to access its incremental validity over the widely used Hollingshead Socioeconomic Index (HSEI). Data was collected from 306 caregivers of children aged 15-30 months (m = 23.37; 71.6% Caucasian) via Prolific. CFA results indicated that Coleman’s model was not a good fit for the data. However, ESEM analysis demonstrated a strong fit with two correlated error terms, revealing a three-factor structure that was different from Coleman’s original model. The new model identified three factors: human capital (caregivers’ education level and years of education), wealth/prestige capital (caregivers’ income, income-to-needs ratio, net worth, status and prestige), and neighborhood capital (neighborhood education, income, and poverty). However, neither the new SES model nor HSEI significantly predicted vocabulary scores in this sample. These findings highlight the complexity of SES measurement and underscore the need for a robust theoretical framework in assessing its impact on child development.

Degree Date

Summer 8-5-2025

Document Type

Thesis

Degree Name

M.A.

Department

Psychology

Advisor

Sarah Kucker

Acknowledgements

I would like to express my sincere gratitude to my advisor, Dr. Sarah Kucker, for her invaluable mentorship, guidance, and support throughout this project. Her insight and encouragement have been instrumental to both my academic development and the completion of this thesis. I am also deeply grateful to my committee members – Dr. Michael Chmielewski, Dr. Chrystyna Kouros, and Dr. Joshua Oltmanns – for their thoughtful feedback and ongoing support. I would like to extend special thanks to Dr. Akihito Kamata for his generous assistance with data analysis. I am thankful to my family and friends for their constant encouragement throughout this process. I am especially indebted to my partner, Dr. Jashkaran Gadhvi, whose support, patience, and care allowed me to focus fully on this work. Finally, I would like to thank my daughter, Rayva, and our cat, Joey, for their comforting presence and emotional support during this journey.

Number of Pages

57

Format

.pdf

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

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