Abstract
Across the world countries are currently striving to eliminate poverty, improve the quality of education, optimize well-being, among other areas of improvement. In an effort to help such improvements, a group called Young Lives ran a longitudinal study on youth in Ethiopia, India, Peru, and Vietnam that studied the many facets of poverty. The purpose of this study is to utilize the Young Lives dataset to determine how countries can more readily improve social-emotional skills by looking at important experiences in adolescents' lives. Specifically, this study examines what factors increase a child's ability to socialize with peers, which is shown to be linked to higher academic success as well as a fuller development into adulthood. In order to measure the ability to socialize with peers, Young Lives used the relationships with Peers Scale (RPS). I examined, through implementing structural equation modeling techniques, what factors significantly predict RPS scores, as well as which time point the factors are most predictive. I also inspected the psychometric properties of the RPS on the Young Lives' population and observed measurement invariance across time and country in order to ensure this scale is a valid measure. Steps to improve relationships with peers can be taken by encouraging higher intrinsic locus of control, providing equal educational opportunities, improving safety conditions, providing nutritional education, and eliminating competition for resources.
Degree
MS
College and Department
David O. McKay School of Education; Instructional Psychology and Technology
Rights
https://lib.byu.edu/about/copyright/
BYU ScholarsArchive Citation
Fullmer, Susanna, "Determining Predictors of Peer Relations: A Study on Youth inEthiopia, India, Peru, and Vietnam" (2021). Theses and Dissertations. 9084.
https://scholarsarchive.byu.edu/etd/9084
Date Submitted
2021-06-17
Document Type
Thesis
Handle
http://hdl.lib.byu.edu/1877/etd11722
Keywords
adolescents, developing nations, educational improvement, child development, multiple regression analysis
Language
english