Publication Date
6-1-2024
School
School of Business
Major
Business Administration
Keywords
Linguistics, Language Learning, Language Acquisition, Data Analysis, Language Factors
Disciplines
Applied Linguistics | Business Analytics | Computational Linguistics | First and Second Language Acquisition | Modern Languages
Recommended Citation
Barrieau, Madisen, "Predicting Language Proficiency Using a Multiple Regression Model" (2024). Senior Honors Theses. 1437.
https://digitalcommons.liberty.edu/honors/1437
Abstract
Businesses that design language learning products have a common goal to impart language proficiency to users. Many variables play a role in reaching language proficiency, including time spent in study, method of learning, use of technology, motivation, and more. This study involved creating several multiple regression models in R on a dataset featuring many of these variables. This research sought to identify the relative weight and measurability of these variables unto the goal of predicting language proficiency. Several regression models were created, and the best model showed that language similarity and length of residence in target culture, among other factors, contributed to language proficiency. Suggestions for businesses selling language-learning products and recommendations for future research are included.
Included in
Applied Linguistics Commons, Business Analytics Commons, Computational Linguistics Commons, First and Second Language Acquisition Commons, Modern Languages Commons
