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Journal articles on the topic 'Ordinal regression analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 27 July 2025

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1

Liu, Xing, and Hari Koirala. "Ordinal Regression Analysis: Using Generalized Ordinal Logistic Regression Models to Estimate Educational Data." Journal of Modern Applied Statistical Methods 11, no. 1 (2012): 242–54. http://dx.doi.org/10.22237/jmasm/1335846000.

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2

Waegeman, Willem, Bernard De Baets, and Luc Boullart. "ROC analysis in ordinal regression learning." Pattern Recognition Letters 29, no. 1 (2008): 1–9. http://dx.doi.org/10.1016/j.patrec.2007.07.019.

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3

Nennuri, Rajashekar, M. Geetha Yadav, Y. Sai Vahini, Goda Sairam Prabhas, and V. Rajashree. "Twitter Sentimental Analysis based on Ordinal Regression." Journal of Physics: Conference Series 1979, no. 1 (2021): 012069. http://dx.doi.org/10.1088/1742-6596/1979/1/012069.

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4

Saad, Shihab Elbagir, and Jing Yang. "Twitter Sentiment Analysis Based on Ordinal Regression." IEEE Access 7 (2019): 163677–85. http://dx.doi.org/10.1109/access.2019.2952127.

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Kadziński, MiŁosz, Salvatore Greco, and Roman SŁowiński. "Extreme ranking analysis in robust ordinal regression." Omega 40, no. 4 (2012): 488–501. http://dx.doi.org/10.1016/j.omega.2011.09.003.

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Krasko, O. V., M. Yu Reutovich, and A. L. Patseika. "Preoperative prediction of gastric cancer T-staging based on ordinal regression models." Informatics 21, no. 2 (2024): 36–53. http://dx.doi.org/10.37661/1816-0301-2024-21-2-36-53.

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Objectives. Study of ordinal regressions presented via the set of binary logistic regressions and their application in clinical practice for T-staging of gastric cancer.Methods. Methods of ordinal regression statistical models, model performance assessment, and survival analysis were used.Results. Basic ordinal regression models have been studied and applied to the clinical data of gastric cancer. Some clinical predictors have been added to the well-known prognostic criteria according to the TNM classification in the multifactor regression model, results seem appropriate for a personalized app

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Morris, Daryl E., Margaret Sullivan Pepe, and William E. Barlow. "Contrasting Two Frameworks for ROC Analysis of Ordinal Ratings." Medical Decision Making 30, no. 4 (2010): 484–98. http://dx.doi.org/10.1177/0272989x09357477.

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Background. Statistical evaluation of medical imaging tests used for diagnostic and prognostic purposes often employs receiver operating characteristic (ROC) curves. Two methods for ROC analysis are popular. The ordinal regression method is the standard approach used when evaluating tests with ordinal values. The direct ROC modeling method is a more recently developed approach, motivated by applications to tests with continuous values. Objective. The authors compare the methods in terms of model formulations, interpretations of estimated parameters, the ranges of scientific questions that can

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Simpson, Douglas G., Raymond J. Carroll, Haibo Zhou, and Daniel J. Guth. "Interval Censoring and Marginal Analysis in Ordinal Regression." Journal of Agricultural, Biological, and Environmental Statistics 1, no. 3 (1996): 354. http://dx.doi.org/10.2307/1400524.

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9

Bradley, R., and W. M. Maclaren. "Ordinal logistic regression analysis of flight task ratings." Aeronautical Journal 110, no. 1109 (2006): 447–56. http://dx.doi.org/10.1017/s0001924000001342.

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Abstract The relationship between a pilot workload rating for a simulated flight task in the proximity of an offshore platform helideck and three experimental factors – wind speed, wind direction and pilot is investigated. The statistical method employed is ordinal logistic regression, which allows the specifying and fitting of regression relationships between ordered categorical response variables and explanatory variables. The response variable in this context is a pilot’s rating of the workload induced by certain flight tasks, measured on an ordered categorical scale 1 to 10. Estimates of t

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10

Wang, Hongwei. "Ordinal Logistic Regression Analysis in Effective Teaching Practices." Journal of Mathematics and Statistics 20, no. 1 (2024): 13–17. http://dx.doi.org/10.3844/jmssp.2024.13.17.

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11

Li, Yonghai, and Daniel W. Schafer. "Likelihood analysis of the multivariate ordinal probit regression model for repeated ordinal responses." Computational Statistics & Data Analysis 52, no. 7 (2008): 3474–92. http://dx.doi.org/10.1016/j.csda.2007.10.025.

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12

Nachmani, Inbar, Bar Genossar, Coral Scharf, Roee Shraga, and Avigdor Gal. "SLACE: A Monotone and Balance-Sensitive Loss Function for Ordinal Regression." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 18 (2025): 19598–606. https://doi.org/10.1609/aaai.v39i18.34158.

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Ordinal regression classifies an object to a class out of a given set of possible classes, where labels possess a natural order. It is relevant to a wide array of domains including risk assessment, sentiment analysis, image ranking, and recommender systems. Like common classification, the primary goal of ordinal regression is accuracy. Yet, in this context, the severity of prediction errors varies, e.g., in risk assessment, Critical Risk is more urgent than High risk and significantly more urgent than No risk. This leads to a modified objective of ensuring that the model's output is as close a

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13

Hannah, Murray, and Paul Quigley. "Presentation of Ordinal Regression Analysis on the Original Scale." Biometrics 52, no. 2 (1996): 771. http://dx.doi.org/10.2307/2532917.

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14

Hedeker, Donald, and Robert D. Gibbons. "A Random-Effects Ordinal Regression Model for Multilevel Analysis." Biometrics 50, no. 4 (1994): 933. http://dx.doi.org/10.2307/2533433.

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15

Perin, J., J. S. Preisser, C. Phillips, and B. Qaqish. "Regression analysis of correlated ordinal data using orthogonalized residuals." Biometrics 70, no. 4 (2014): 902–9. http://dx.doi.org/10.1111/biom.12210.

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16

Crane, Paul K., Laura E. Gibbons, Lance Jolley, and Gerald van Belle. "Differential Item Functioning Analysis With Ordinal Logistic Regression Techniques." Medical Care 44, Suppl 3 (2006): S115—S123. http://dx.doi.org/10.1097/01.mlr.0000245183.28384.ed.

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KUMARI, VANDITA, RANJANA AGRAWAL, and AMRENDER KUMAR. "Use of ordinal logistic regression in crop yield forecasting." MAUSAM 67, no. 4 (2021): 913–18. http://dx.doi.org/10.54302/mausam.v67i4.1419.

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The performance of ordinal logistic regression and discriminant function analysis has been compared in crop yield forecasting of wheat crop for Kanpur district of Uttar Pradesh. Crop years were divided into two or three groups based on the detrended yield. Crop yield forecast models have been developed using probabilities obtained through ordinal logistic regression along with year as regressors and validated using subsequent years data. In discriminant function approach two types of models were developed, one using scores and another using posterior probabilities. Performance of the models ob

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Lelisho, Mesfin Esayas, Abebe Argaw Wogi, and Seid Ali Tareke. "Ordinal Logistic Regression Analysis in Determining Factors Associated with Socioeconomic Status of Household in Tepi Town, Southwest Ethiopia." Scientific World Journal 2022 (February 3, 2022): 1–9. http://dx.doi.org/10.1155/2022/2415692.

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Background. Socioeconomic status (SES) refers to an individual’s or group’s social position or class, which is often determined by a combination of education, income, and occupation. Knowing factors that affect the SES of the society might help to take action and improve their economy. In addition, using an ordinal logistic regression model for ordered SES outcomes will yield suitable results and conclusions. This study aimed to utilize an ordinal logistic regression model to find the factors associated with SES for households in Tepi town, Southwest Ethiopia. Methods. The community-based cros

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19

Setyowati, Silfiana Lis, Indahwati, Anwar Fitrianto, Erfiani, and Muftih Alwi Aliu. "Comparison of Ordinal Logistic Regression and Geographically Weighted Ordinal Logistic Regression (GWOLR) in Predicting Stunting Prevalence among Indonesian Toddlers." Sainmatika: Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam 21, no. 2 (2024): 105–16. https://doi.org/10.31851/sainmatika.v21i2.15416.

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Ordinal logistic regression is a type of logistic regression used for response variables with an ordinal scale, containing two or more categories with levels between them. This method is an extension of logistic regression where the observed response variable is ordinal with a clear order. It addresses spatial effects that can cause variance heterogeneity and improve parameter estimation accuracy compared to logistic regression. Geographically Weighted Regression (GWR) is a statistical analysis technique designed to account for spatial heterogeneity. GWOLR is an extension of OLS and GWR models

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Jajang, Jajang, Nunung Nurhayati, and Suci Jena Mufida. "ORDINAL LOGISTIC REGRESSION MODEL AND CLASSIFICATION TREE ON ORDINAL RESPONSE DATA." BAREKENG: Jurnal Ilmu Matematika dan Terapan 16, no. 1 (2022): 075–82. http://dx.doi.org/10.30598/barekengvol16iss1pp075-082.

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Logistic regression (LR) is a model that associates the relationship between category-type response variables with quantitative or quantitative and qualitative predictor variables. The prediction of the LR model is in the form of probability. This research studied logistic regression (LR) models and Classification Trees in the case of ordinal response variable types. The data used in this research from The Central Statistics Agency (BPS). The research variables used are Human Development Index (HDI), gross enrollment rate for high school, percentage of poor people, open unemployment, and perce

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21

Kobayashi, Yuichiro. "Identifying L2 Developmental Indices while Controlling for L1 Effects: A Multilevel Ordinal Logistic Regression Analysis." Journal of Pan-Pacific Association of Applied Linguistics 25, no. 2 (2021): 87–104. http://dx.doi.org/10.25256/paal.25.2.5.

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22

Feng, Xiang-Nan, Hao-Tian Wu, and Xin-Yuan Song. "Bayesian Adaptive Lasso for Ordinal Regression With Latent Variables." Sociological Methods & Research 46, no. 4 (2015): 926–53. http://dx.doi.org/10.1177/0049124115610349.

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We consider an ordinal regression model with latent variables to investigate the effects of observable and latent explanatory variables on the ordinal responses of interest. Each latent variable is characterized by correlated observed variables through a confirmatory factor analysis model. We develop a Bayesian adaptive lasso procedure to conduct simultaneous estimation and variable selection. Nice features including empirical performance of the proposed methodology are demonstrated by simulation studies. The model is applied to a study on happiness and its potential determinants from the Inte

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23

Kadir, Dler H., and Ameera W. Omer. "Implementing Analysis of Ordinal Regression Model on Student’s Feedback Response." Cihan University-Erbil Journal of Humanities and Social Sciences 5, no. 1 (2021): 45–49. http://dx.doi.org/10.24086/cuejhss.v5n1y2021.pp45-49.

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Instruction is a multidimensional procedure including a quantity of features, e.g., tutor qualities, that occasionally are hard to assess. In certain points, education efficiency, that is a part of instructing, is affected by a combination of teacher features for example, capacity and clarity to encourage the students to make them study of his subjects, capacity to establish the lesson also with trainings and lectures. These aspects are not only attributable to motivate students, but also age, gender, prior experiences, As more and more the effectiveness of teaching is becoming even more signi

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XU, Peng, Lu QI, Jian XIONG, and Haosheng YE. "A Regression Analysis Model of Ordinal Variable to Psychological Data." Acta Psychologica Sinica 47, no. 12 (2015): 1520. http://dx.doi.org/10.3724/sp.j.1041.2015.01520.

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25

Wan, Jim Y., Whedy Wang, and Judith Bromberg. "A SAS macro for residual deviance of ordinal regression analysis." Computer Methods and Programs in Biomedicine 45, no. 4 (1994): 307–10. http://dx.doi.org/10.1016/0169-2607(94)01591-3.

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26

Hedeker, Donald, and Robert D. Gibbons. "MIXOR: a computer program for mixed-effects ordinal regression analysis." Computer Methods and Programs in Biomedicine 49, no. 2 (1996): 157–76. http://dx.doi.org/10.1016/0169-2607(96)01720-8.

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27

Hirk, Rainer, Kurt Hornik, and Laura Vana. "Multivariate ordinal regression models: an analysis of corporate credit ratings." Statistical Methods & Applications 28, no. 3 (2018): 507–39. http://dx.doi.org/10.1007/s10260-018-00437-7.

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28

DINH, Doan Van. "Optimal Inflation Threshold and Economic Growth: Ordinal Regression Model Analysis." Journal of Asian Finance, Economics and Business 7, no. 5 (2020): 91–102. http://dx.doi.org/10.13106/jafeb.2020.vol7.no5.091.

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29

DAĞDEVİREN ERTAŞ, Behiye. "Investigation of Teaching Profession Dedication by Ordinal Logistic Regression Analysis." MANAS Sosyal Araştırmalar Dergisi 12, no. 4 (2023): 1236–48. http://dx.doi.org/10.33206/mjss.1302439.

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Bu araştırmanın temel amacı öğretmenlerin mesleki adanmışlık düzeyinin saptanması ve bağımsız değişkenlerin bağımlı değişkeni yordama düzeylerinin incelenmesidir. Bu araştırma nicel yöntemle incelenmiş bir çalışma olup ilişkisel tarama desenindedir. Araştırmanın çalışma grubunu 455 öğretmen oluşturmaktadır. Araştırma Yozgat ilinde gerçekleştirilmiştir. Araştırmada kolay ulaşılabililir örnekleme ile veriler toplanmıştır. Araştırmada Öğretmenlik Mesleğine Adanmışlık Ölçeği (Kozikoğlu ve Senemoğlu, 2018) kullanılmıştır. Veriler sıralı lojistik regresyon analizi ile analiz edilmiştir. Araştırma so

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Zufa, Fajri, Sigit Nugroho, and Mudin Simanihuruk. "Perbandingan Analisis Diskriminan dan Analisis Regresi Logistik Ordinal dalam Prediksi Klasifikasi Kondisi Kesehatan Bank." Jurnal Matematika 7, no. 2 (2017): 92. http://dx.doi.org/10.24843/jmat.2017.v07.i02.p86.

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The purpose of this research is to compare the accuracy of bank classification prediction based on Capital Adequacy Ratio (CAR), Earning Asset Quality (EAQ), Non Performing Loan (NPL), Return on Assets (ROA), Net Interest Margin (NIM), Short Term Mismatch (STM) and Loan to Deposit Ratio (LDR). Discriminant analysis and ordinal logistic regression analysis are compared in classifying the prediction. The data used are secondary data, namely data classification of bank conditions in Indonesia in 2014 obtained from research institute PT Infovesta Utama. Based on Apparent Error Rate (APER) score ob

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Maity, Arnab Kumar, and Jyotirmoy Dey. "Power Analysis of Collapsed Ordered Categories with Application to Cancer Data." Calcutta Statistical Association Bulletin 70, no. 2 (2018): 87–95. http://dx.doi.org/10.1177/0008068318803140.

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Ordinal data are often found in clinical studies. Sometimes the analysis of this data is done using binary logistic regression after collapsing the categories of ordinal responses. However, these analyses may not be appropriate in practice because either the assumptions are violated or because the information is lost. Cumulative logistic regression is shown to be a better alternative approach. The efficiency of cumulative logistic regression is demonstrated using simulation studies. A novel sequential testing approach is suggested in the context of cancer data. In addition, in the absence of t

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Fu, L. Y., Y. G. Wang, and C. J. Liu. "Rank Regression for Analyzing Ordinal Qualitative Data for Treatment Comparison." Phytopathology® 102, no. 11 (2012): 1064–70. http://dx.doi.org/10.1094/phyto-05-11-0128.

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Ordinal qualitative data are often collected for phenotypical measurements in plant pathology and other biological sciences. Statistical methods, such as t tests or analysis of variance, are usually used to analyze ordinal data when comparing two groups or multiple groups. However, the underlying assumptions such as normality and homogeneous variances are often violated for qualitative data. To this end, we investigated an alternative methodology, rank regression, for analyzing the ordinal data. The rank-based methods are essentially based on pairwise comparisons and, therefore, can deal with

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Tosteson, A. N., M. C. Weinstein, J. Wittenberg, and C. B. Begg. "ROC curve regression analysis: the use of ordinal regression models for diagnostic test assessment." Environmental Health Perspectives 102, suppl 8 (1994): 73–78. http://dx.doi.org/10.1289/ehp.94102s873.

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Amini, Payam, Abbas Moghimbeigi, Farid Zayeri, Leili Tapak, Saman Maroufizadeh, and Geert Verbeke. "Longitudinal Joint Modelling of Ordinal and Overdispersed Count Outcomes: A Bridge Distribution for the Ordinal Random Intercept." Computational and Mathematical Methods in Medicine 2021 (March 3, 2021): 1–13. http://dx.doi.org/10.1155/2021/5521881.

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Associated longitudinal response variables are faced with variations caused by repeated measurements over time along with the association between the responses. To model a longitudinal ordinal outcome using generalized linear mixed models, integrating over a normally distributed random intercept in the proportional odds ordinal logistic regression does not yield a closed form. In this paper, we combined a longitudinal count and an ordinal response variable with Bridge distribution for the random intercept in the ordinal logistic regression submodel. We compared the results to that of a normal

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Erfina Agustin Hidayat and Nusar Hajarisman. "Aplikasi Regresi Logistik Ordinal Multilevel untuk Pemodelan Huruf Mutu Mata Kuliah Statistika Dasar Mahasiswa Universitas Islam Bandung Tahun 2019/2020." Bandung Conference Series: Statistics 3, no. 2 (2023): 302–12. http://dx.doi.org/10.29313/bcss.v3i2.8096.

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Abstract. Multilevel ordinal logistic regression analysis is a regression analysis for discrete-scale responses, especially with a hierarchical ordinal scale. The hierarchical structure indicates that the data analyzed comes from several levels, where lower levels are nested in higher levels. This article will discuss the application of multilevel ordinal logistic regression using the Maximum Likelihood Estimation (MLE) method in the field of education, namely regarding the quality letter of the Basic Statistics course of 2019 Bandung Islamic University students at the Faculty of Engineering,

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Mayawi, Mayawi, Nurhayati Nurhayati, Taufan Talib, Ariestha W. Bustan, and Novita S. Laamena. "Ordinal Logistic Regression Analysis of Factors that Affecting the Blood Sugar Levels Diabetes Mellitus Patients." Pattimura International Journal of Mathematics (PIJMath) 2, no. 1 (2023): 33–42. http://dx.doi.org/10.30598/pijmathvol2iss1pp33-42.

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Penelitian ini bertujuan untuk menganalisis pengaruh faktor-faktor risiko terhadap kadar gula darah pada penderita diabetes mellitus menggunakan analisis regresi logistik ordinal. Faktor-faktor risiko yang dijadikan variabel bebas adalah usia, jenis kelamin, Indeks Massa Tubuh (IMT), tekanan darah, Tingkat Kolesterol (TC), Low Density Lipoprotein (LDL), High Density Lipoprotein (HDL), Thyrocalcitonin Hormone (TCH) dan Loss Trigliserida(LTG). Data yang digunakan dalam penelitian ini diperoleh dari https://hastie.su.domains/Papers/LARS/diabetes.data. Jumlah sampel yang diambil sebanyak 100 respo

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P, Priyadarshini, and Veeramanju K.T. "STUDENT SATISFACTION ANALYSIS WITH GENETIC ALGORITHM-BASED DATA AUGMENTATION AND REGRESSION MODELS." ICTACT Journal on Soft Computing 16, no. 1 (2025): 3769–77. https://doi.org/10.21917/ijsc.2025.0522.

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Student satisfaction plays an important role in determining the quality, retention, and reputation of an institution. However, limited survey data can reduce the accuracy of predictive models. This study explores how Genetic Algorithm based data augmentation can improve dataset reliability and enhance analysis using LASSO and Ordinal Regression. By generating synthetic responses, GA expands the dataset while maintaining statistical accuracy, leading to better feature selection and ranking. LASSO Regression identified key factors influencing student satisfaction, such as career services, curric

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Wang, Hongwei, Fernando G. Quintana, Yunlong Lu, Muhammad Mohebujjaman, and Kanon Kamronnaher. "How Are BMI, Nutrition, and Physical Exercise Related? An Application of Ordinal Logistic Regression." Life 12, no. 12 (2022): 2098. http://dx.doi.org/10.3390/life12122098.

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Background: This paper performs a detailed ordinal logistic regression study in an evaluation of a survey at a university in South Texas, USA. We show that, for categorical data in our case, ordinal logistic regression works well. Methods: The survey was designed according to the guidelines in diet and lifestyle from the American Heart Association and the United States Department of Agriculture and was sent out to all registered students at Texas A&M International University in Laredo, Texas. Data analysis included 601 students’ results from the survey. Data analysis was conducted in Rstud

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Shahzad, Hazik Bin, Wan Muhammad Amir W Ahmad, Mohamad Nasarudin Adnan, and Anas Imran Arshad. "PREDICTION CLASSIFICATION AND MODELLING USING DECISION TREE WITH ORDERED REGRESSION AND ITS APPLICATION TO SOCIO-BEHAVIORAL FACTORS ASSOCIATED WITH TOOTHBRUSHING FREQUENCY IN CHILDREN." Malaysian Journal of Public Health Medicine 23, no. 2 (2023): 187–97. http://dx.doi.org/10.37268/mjphm/vol.23/no.2/art.2053.

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Toothbrushing is considered the best self-care behavior for the prevention of oral diseases. Brushing teeth twice a day is considered the social norm, but the development of such habits is dependent on psychosocial, economic, and environmental factors. Recognizing the significance of statistical modeling in medical sciences, this study will use decision trees and ordinal regression to predict frequency of toothbrushing in children. The methodology will be harmonized in the R syntax. The study illustrated the development of the method using 527 observations from WHO oral health questionnaire fo

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Hamid, Assyifa Lala Pratiwi, Anwar Fitrianto, Indahwati Indahwati, Erfiani Erfiani, and Khusnia Nurul Khikmah. "The Comparison between Ordinal Logistic Regression and Random Forest Ordinal in Identifying the Factors Causing Diabetes Mellitus." Jambura Journal of Mathematics 5, no. 2 (2023): 289–304. http://dx.doi.org/10.34312/jjom.v5i2.20289.

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Diabetes is one of the high-risk diseases. The most prominent symptom of this disease is high blood sugar levels. People with diabetes in Indonesia can reach 30 million people. Therefore, this problem needs further research regarding the factors that cause it. Further analysis can be done using ordinal logistic regression and random forest. Both methods were chosen to compare the modelling results in determining the factors causing diabetes conducted in the CDC dataset. The best model obtained in this study is ordinal logistic regression because it generates an accuracy value of 84.52%, which

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Liu, Xing, Ann A. O'Connell, and Hari Koirala. "Ordinal Regression Analysis: Predicting Mathematics Proficiency Using the Continuation Ratio Model." Journal of Modern Applied Statistical Methods 10, no. 2 (2011): 513–27. http://dx.doi.org/10.22237/jmasm/1320120600.

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Polyzos, Serafeim, and Dionysios Minetos. "An ordinal regression analysis of tourism enterprises' location decisions in Greece." Anatolia 22, no. 1 (2011): 102–19. http://dx.doi.org/10.1080/13032917.2011.556225.

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Maulia, Lyfia Indah. "Ordinal Logistic Regression Analysis on Consumer Satisfaction Levels in Internet Usage." Journal of Applied Statistics and Data Science 1, no. 2 (2024): 100–108. http://dx.doi.org/10.21776/ub.jasds.2024.001.02.4.

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Khikmah, Laelatul, and Elis Meida Ratnasari. "Ordinal Logistic Regression Approach for Probability Analysis of Student Stress Levels." Journal of Intelligent Computing & Health Informatics 3, no. 1 (2023): 18. http://dx.doi.org/10.26714/jichi.v3i1.11793.

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Stress is a condition or condition where a person feels pressured because of the many demands, both from within and from outside the individual that must be met. Stress is an uncomfortable stressful event for someone that can cause negative effects such as dizziness, emotional instability, irritability, loss of appetite, difficulty concentrating, and difficulty sleeping. One of the factors that cause stress is doing the Final Project. Someone who is experiencing stress can be seen from the level of stress, namely the level of mild, moderate and severe stress. To see a person's stress level, se

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Grigoroudis, E., G. Nikolopoulou, and C. Zopounidis. "Customer satisfaction barometers and economic development: An explorative ordinal regression analysis." Total Quality Management & Business Excellence 19, no. 5 (2008): 441–60. http://dx.doi.org/10.1080/14783360802018095.

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46

Miftha Delinda and Devni Prima Sari. "University Election Analysis Logistic Regression Approach with Dummy and Ordinal Variables." Mathematical Journal of Modelling and Forecasting 1, no. 2 (2023): 1–9. http://dx.doi.org/10.24036/mjmf.v1i2.13.

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Education has a very important role to advance the development of a country. One of them is the university. Thus, if you continue your studies at university, it is hoped that you will have the knowledge and skills in accordance with the study program you are taking, which will later become the basic capital to be more competent in the world of work. Logistic regression is a statistical method that can be used to determine the factors that influence the choice of university for class XII Phase F students. The dependent variable consists of two categories. This research aims to determine the fac

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Ibrahim, Agusnawan Linu, Agussabti Agussabti, and Fajri Fajri. "An Analysis of the Extension Workers Empowerment in Pidie Jaya Regency." International Journal of Multicultural and Multireligious Understanding 8, no. 2 (2021): 464. http://dx.doi.org/10.18415/ijmmu.v8i2.2379.

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This study aims to determine the factors that affect the level of empowerment of extension workers in Pidie Jaya. The data used are primary data obtained from the results of filling out a questionnaire of 112 extension workers in Pidie Jaya. The analytical method used is using Ordinal Regression. Ordinal regression analysis is a statistical method that describes the relationship between a response variable and more than one predictor variable where the response variable is more than two categories and the measurement scale is level. The results of the study concluded that the level of empowerm

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Melchor, Peter John M., Emma O. Suana, and John Mark N. Saldivar. "On the Multidimensionality of Teachers’ Qualities, Personal Achievement, and their Role in Students’ Achievement in General Mathematics." International Journal of Scientific Research and Management (IJSRM) 13, no. 02 (2025): 3984–96. https://doi.org/10.18535/ijsrm/v13i02.el08.

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This study was conducted to develop a valid and reliable tool that can be used to measure teachers' qualities and create a robust ordinal logistic regression model that predicts students' achievement in General Mathematics based on teachers' qualities and achievements as independent variables. The statistical tools used were the Reliability Test, Exploratory Factor Analysis, and Ordinal Regression Analysis. The researcher interviewed ten experienced mathematics teachers and ten senior high school students who recently finished General Mathematics. Their responses were then converted to measura

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Hamzah Abdul Hamid, Yap Bee Wah, Khatijahhusna Abdul Rani, and Xian Jin Xie. "The Effect Of Divisive Analysis Clustering Technique On Goodness-Of-Fit Test For Multinomial Logistic Regression." Journal of Advanced Research in Applied Sciences and Engineering Technology 48, no. 2 (2024): 39–48. http://dx.doi.org/10.37934/araset.48.2.3948.

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The relationship between a categorical dependent variable and independent variable(s) are usually modelled using the logistic regression method. There are three types of logistic regression: binary, multinomial, and ordinal. When there is two cayegories of dependent variable, binary logistic regression is used while when there is more than two nominal categories of dependent variable, multinomial logistic regression is employed. Ordinal logistic regression is used when the dependent variable contains more than two ordinal categories. All regression models should be checked after being fitted t

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Zhang, Dong Ling, Xiu Zhi Zhu, and Jie Qun Hua. "Internal Control Quality Evaluation Model for Information System Based on QFD and Risk Analysis." Applied Mechanics and Materials 643 (September 2014): 380–84. http://dx.doi.org/10.4028/www.scientific.net/amm.643.380.

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Internal control Quality evaluation of the information system based on the risk management is a more significant issue in the corporate governance. It presents a proposed method for the development of quality assessment and risk evaluation and early-warning for internal control of the information system, and shows the application of the ordinal logistic regression model and its advantages. It involved several steps: processing a quality design through QFD based on the customer need, building a quality evaluation index system of internal control based on risk analysis, building the ordinal logi

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