Depression and Academic Performance in FUNAAB

EasyChair Preprint no. 11939

Abstract

Depression has become a prevalent mental illness that affects one out of every twenty persons at one point in their lifetime, a major health issue among university student, it varies among undergraduate students of different countries and cultures and is influenced by sociodemographic, intrapersonal, interpersonal and environmental factors. The aim of this research is to evaluate the effect of depression on student academic performance in FUNAAB using R shiny and Quarto to bridge the existing gap in dedicated websites for sharing scientific research insights with others across the world. Previous studies have examined the influence of sociodemographic, intrapersonal, interpersonal and environmental factors on depression, but this study improved on these by predicting Academic performance from factors influencing depression. A well-structured questionnaire was develop to source for the data, exploratory factor analysis was used to describe the data and identify underlying factors, multivariate assumptions were screened, outliers were removed, a three-factor model was tested based on theory and reliability was calculated for each factor, and the ordinal logistic regression model built had prediction accuracy of 50% with a misclassification rate of 50% and a predictive dashboard website app using Shiny and Quarto packages in R and Rstudio was developed with the findings. A good academic performance will reduce the effect and chances that a student will be depressed. Hence, effort should be made to help cushion the effect of the independent factors on academic performance.

Keyphrases: academic performance, Depression, environmental stress, Interpersonal stress, Intrapersonal stress, R shiny and Quarto packages.

@Booklet{EasyChair:11939,
  author = {Oluwaseun Wale-Orojo and Victor Arowolo and Omolola Atanda and Ajibola Soyinka and Olaniyi Olayiwola},
  title = {Depression and Academic Performance in FUNAAB},
  howpublished = {EasyChair Preprint no. 11939},

  year = {EasyChair, 2024}}