Ordinal regression

About: Ordinal regression is a research topic. Over the lifetime, 1879 publications have been published within this topic receiving 65431 citations.

Using Ordinal Regression Model to Analyze Student Satisfaction Questionnaires

Abstract: The ordinal regression method was used to model the relationship between the ordinal outcome variable, e.g., different levels of student satisfaction regarding the overall college experience, and the explanatory variables concerning demographics and student learning environment in a predominantly minority health sciences center. The outcome variable for student satisfaction was measured on an ordered, categorical, and four-point Likert scale— ‘very dissatisfied’, ‘dissatisfied’, ‘satisfied’, and ‘very satisfied’. Explanatory variables included two demographics, e.g., gender and ethnic groups, and 42 questionnaire items related to the satisfaction of faculty involvement, curriculum contents, support services, facilities, and leisure activities at the college. The major decisions involved in the model building for ordinal regression were deciding which explanatory variables should be included in the model and choosing the link function (e.g., logit link or complementary log-log link) that demonstrated the model appropriateness. In addition, the model fitting statistics, the accuracy of the classification results, and the validity of the model assumption, e.g., parallel lines, were essentially assessed for selecting the best model. The research findings indicated that explanatory variables such as faculty competence and student-faculty relations were significantly associated with the satisfaction of the overall college experience. This discovery suggests that faculty members have played a major role in creating a pleasant environment to facilitate student satisfaction. In addition, the curriculum content regarding health promotion and disease prevention was significantly associated with the satisfaction of the overall college experience. It may also provide strong evidence that a specific component of the medical curriculum addressed student needs and contributed to the fulfillment of the medical college goal, e.g., delivery of primary care through health promotion and disease prevention.

Ageing, Learning, and Computer Technology in Australia

Abstract: Learning is an important aspect of active ageing, yet older people are not often included in discussions of the issue. Older people vary in their need, desire, and ability to learn, and this is evident in the context of technology. The focus of the data analysis for this paper was on determining the place of learning and technology in active ageing. The paper describes results from 2,645 respondents aged from 50 to 74 + years, in Australia, to a 178-item variable postal survey. The survey measured aspects of learning;, work; social, spiritual and emotional status; health; vision; home; life events; and demographics. There was also an open-ended question about being actively engaged in life. Ordinal regression analysis showed that interest in learning, keeping up to date, valuing communication, being younger, and being male are predictors of learning about technology. The results are at variance with an earlier analysis of our data which showed that women are generally more interested in learning. The open...

Modelling Uncertainty and Overdispersion in Ordinal Data

Abstract: In this article we introduce a probability distribution generated by a mixture of discrete random variables to capture uncertainty, feeling, and overdispersion, possibly present in ordinal data surveys. The choice of the components of the new model is motivated by a study on the data generating process. Inferential issues concerning the maximum likelihood estimates and the validation steps are presented; then, some empirical analyses are given to support the usefulness of the approach. Discussion on further extensions of the model ends the article.

Employed women's well-being: The global And daily impact of work: Working paper series--04-05

Abstract: Using the 1993 wave of the National Longitudinal Survey (NLS) Young Women's Cohort, this paper develops and tests a two-equation model of women's well-being. The model conceptualizes and measures well-being at both a global level and a daily level. A priori, distinct antecedent individual, family, and work variables are postulated to affect global and daily well-being. Ordinal regression and OLS regression are used to estimate the global and daily equations, respectively. In addition to various individual and family variables, it is found that having supervisory capacity, receiving a recent promotion, and government employee status significantly increase women's global well-being, while union membership decreases it. It is also found that working an irregular shift, paid leave, and telecommuting in current job significantly decrease women's daily well-being.