Analysis of Factors Contributing to Extended Study Duration Among Students of the Faculty of Mathematics and Natural Sciences, University of Bengkulu Using Binary Logistic Regression

Logistic regression is a statistical method used to analyze the relationship between a dichotomous dependent variable and one or more independent variables, which may be numerical or categorical. In this study, binary logistic regression is applied to identify the factors influencing the study duration of students in the Faculty of Mathematics and Natural Sciences at the University of Bengkulu. These factors include both internal and external elements, such as cumulative GPA (Grade Point Average), gender, parents’ occupation, scholarship status, and university admission pathway. The results show that GPA significantly affects the length of study, with an odds ratio of 1102.13, indicating that each one-unit increase in GPA greatly increases the likelihood of graduating on time. This study suggests the use of additional statistical techniques, such as bootstrapping, to enhance parameter estimation accuracy and recommends reporting effect sizes, such as odds ratios, for a more comprehensive interpretation of the relationship between independent and dependent variables.

Authors

Institutions

Publication Details

Journal
Journal of Statistics and Data Science
Published
2025-04-25
DOI
https://doi.org/10.33369/jsds.v2i2.41287
Primary Topic
Educational Technology and Assessment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analysis of Factors Contributing to Extended Study Duration Among Students of the Faculty of Mathematics and Natural Sciences, University of Bengkulu Using Binary Logistic Regression

Indah Wahyuliani, Pepi Novianti
Journal of Statistics and Data Science
Educational Technology and Assessment
article

Analysis of Factors Contributing to Extended Study Duration Among Students of the Faculty of Mathematics and Natural Sciences, University of Bengkulu Using Binary Logistic Regression

Indah Wahyuliani, Pepi Novianti
article en

Abstract

Logistic regression is a statistical method used to analyze the relationship between a dichotomous dependent variable and one or more independent variables, which may be numerical or categorical. In this study, binary logistic regression is applied to identify the factors influencing the study duration of students in the Faculty of Mathematics and Natural Sciences at the University of Bengkulu. These factors include both internal and external elements, such as cumulative GPA (Grade Point Average), gender, parents’ occupation, scholarship status, and university admission pathway. The results show that GPA significantly affects the length of study, with an odds ratio of 1102.13, indicating that each one-unit increase in GPA greatly increases the likelihood of graduating on time. This study suggests the use of additional statistical techniques, such as bootstrapping, to enhance parameter estimation accuracy and recommends reporting effect sizes, such as odds ratios, for a more comprehensive interpretation of the relationship between independent and dependent variables.

Journal of Statistics and Data ScienceVol. 2(2)
University of Bengkulu (ID)
Openalex Percentile: Top 89%
Educational Technology and Assessment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.