ORDINAL REGRESSION FOR MODELLING THE FAMILY WELL-BEING AMONG THE MALAYSIANS

Background and Purpose: Understanding factors which affect the level of family well-being is important as it contributes to effective decision making among the policymakers to improve the family lives as well as to strengthen the family institution. Accordingly, this line of research is gaining attention. This study develops an ordinal regression model which identifies demographic, economic and social factors that are significant in explaining the status of family well-being.    Methodology: Data involving 2,808 respondents from a nationwide survey conducted by the National Population and Family Development Board of Malaysia in 2011 were used in this study. Ordinal regression model was implemented to describe the three levels of family well-being.   Findings: The national survey reported that high level of family well-being was experienced by 76.3 per cent of the respondents, followed by moderate (18.4%) and low (5.3%). The fitted ordinal regression model found that ethnic background, family relationship, community relationship, health and safety levels, economic situation of the family, religious practice, housing, and environment are significantly related to family well-being. Meanwhile, it was found that the level of income is not a significant factor in determining the level of family well-being.     Contributions: There are a limited number of studies on the application of ordinal regression for modelling the level of family well-being, particularly with covariates involving the demographic and social characteristics of the respondents. This study fills in the gap in the literature where the ordinal regression model provides useful information for policymakers to enhance the status of family well-being in Malaysia via various policy initiatives.   Keywords: Family well-being, Ordinal Regression Model, ordinal data, Proportional Odds Model.   Cite as: Muhammad Sapri, N. A., Ibrahim, K., Abu Bakar, M. A., & Mohd Ariff, N. (2021). Ordinal regression for modelling the family well-being among the Malaysians.  Journal of Nusantara Studies, 6(2), 424-447. http://dx.doi.org/10.24200/jonus.vol6iss2pp424-44

Decision Tree Model for Non-Fatal Road Accident Injury

Non-fatal road accident injury has become a great concern as it is associated with injury and sometimes leads to the disability of the victims. Hence, this study aims to develop a model that explains the factors that contribute to non-fatal road accident injury severity. A sample data of 350 non-fatal road accident cases of the year 2016 were obtained from Kota Bharu District Police Headquarters, Kelantan. The explanatory variables include road geometry, collision type, accident time, accident causes, vehicle type, age, airbag, and gender. The predictive data mining techniques of decision tree model and multinomial logistic regression were used to model non-fatal road accident injury severity. Based on accuracy rate, decision tree with CART algorithm was found to be more accurate as compared to the logistic regression model. The factors that significantly contribute to non-fatal traffic crashes injury severity are accident cause, road geometry, vehicle type, age and collision type

PREDICTION OF MALAYSIAN WOMEN DIVORCE USING MACHINE LEARNING TECHNIQUES

This paper discusses the performance of three machine learning techniques namely Decision Tree, Logistic Regression and Artificial Neural Network for predicting divorce among Malaysian women. Secondary data were obtained from the Fifth Malaysia Population and Family Survey (MPFS-5) conducted by the National Population and Family Development Board (LPPKN). The total number of instances in the dataset was 7,644 ever married Malaysian women aged 15 to 59 years old. Divorce is currently a serious problem among the Malaysian community due to various reasons. In 2019, the divorce rate in Malaysia rose by 12% from the previous year. During the first three months of the Movement Control Order (MCO), i.e. from March 18 to June 18, 2020, the Syariah Court of Malaysia recorded 6,569 divorce cases. Worse, a total of 90,766 divorce cases were recorded from January to October 2020. Six predictive models were used for comparison, namely Decision Tree (C5.0 and CHAID), Logistic Regression (Forward Stepwise and Backward Stepwise), and Artificial Neural Network (Multi-Layer Perceptron and Radial Basis Function). Among the six predictive methods, the Decision Tree model (C5.0) was found to be the best model in classifying divorce among Malaysian women. The accuracy of the C5.0 model was 77.96% followed by the Artificial Neural Network (Multi-Layer Perceptron) and Logistic Regression (Forward Stepwise) model (74.68% and 67.89%, respectively). The order of important predictors in predicting divorce among Malaysian women is the wives’ employment status (0.1531) followed by the husbands’ employment status (0.1396), type of marriage (0.1327), race/ethnicity (0.1327), distant relationship (0.1212), the wives’ qualification level (0.1115), age group (0.1053) and religion (0.0998)