The Impact of Parental Education on the Early Detection of Autism Spectrum Disorder in Children.
(2023) NEKP01 20231Department of Economics
- Abstract
- Utilizing the Double Machine Learning (DML) framework with both Partially Linear Regression (PLR) and Interactive Regression Models (IRM), this study examined the crucial role of parental education in the early detection of Autism Spectrum Disorder (ASD). Analysis was conducted on comprehensive data from the National Survey of Children's Health spanning the years 2017 to 2021. A significant negative association was found between parental education and the age at ASD diagnosis. This relationship was most notable in cases of moderate ASD severity, indicating that higher parental education levels tend to expedite ASD detection in this group. For mild and severe ASD cases, however, the relationship was less clear, suggesting that parental... (More)
- Utilizing the Double Machine Learning (DML) framework with both Partially Linear Regression (PLR) and Interactive Regression Models (IRM), this study examined the crucial role of parental education in the early detection of Autism Spectrum Disorder (ASD). Analysis was conducted on comprehensive data from the National Survey of Children's Health spanning the years 2017 to 2021. A significant negative association was found between parental education and the age at ASD diagnosis. This relationship was most notable in cases of moderate ASD severity, indicating that higher parental education levels tend to expedite ASD detection in this group. For mild and severe ASD cases, however, the relationship was less clear, suggesting that parental education's influence varies across the spectrum of ASD severity. The study's findings underscore the potential of parental education as a key socio-economic factor in ASD detection, highlighting a critical area for future research to further elucidate the role of socio-demographic factors across different ASD severity levels. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9121226
- author
- Suleymanli, Farahim LU
- supervisor
-
- Simon Reese LU
- organization
- course
- NEKP01 20231
- year
- 2023
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- ASD, DML, PLR, IRM, early detection, parental education, causal inference
- language
- English
- id
- 9121226
- date added to LUP
- 2023-06-19 10:08:41
- date last changed
- 2023-06-19 10:08:41
@misc{9121226, abstract = {{Utilizing the Double Machine Learning (DML) framework with both Partially Linear Regression (PLR) and Interactive Regression Models (IRM), this study examined the crucial role of parental education in the early detection of Autism Spectrum Disorder (ASD). Analysis was conducted on comprehensive data from the National Survey of Children's Health spanning the years 2017 to 2021. A significant negative association was found between parental education and the age at ASD diagnosis. This relationship was most notable in cases of moderate ASD severity, indicating that higher parental education levels tend to expedite ASD detection in this group. For mild and severe ASD cases, however, the relationship was less clear, suggesting that parental education's influence varies across the spectrum of ASD severity. The study's findings underscore the potential of parental education as a key socio-economic factor in ASD detection, highlighting a critical area for future research to further elucidate the role of socio-demographic factors across different ASD severity levels.}}, author = {{Suleymanli, Farahim}}, language = {{eng}}, note = {{Student Paper}}, title = {{The Impact of Parental Education on the Early Detection of Autism Spectrum Disorder in Children.}}, year = {{2023}}, }