In this paper we focus on gaining insight into the residential satisfaction of households near highways, based on survey data collected among 1,225 respondents in the Netherlands living within 1,000 meters from a highway. Ordinal regression was used to study the impact of highway externalities on residential satisfaction. Moreover, we gained first insights into the reactions of people on highwa...

Quality of life has been increasingly emphasized in public health research in recent years. Typically, the results of quality of life are measured by means of ordinal scales. In these situations, specific statistical methods are necessary because procedures such as either dichotomization or misinformation on the distribution of the outcome variable may complicate the inferential process. Ordina...

OBJECTIVES To evaluate visual grading characteristics (VGC) and ordinal regression analysis during head CT optimisation as a potential alternative to visual grading assessment (VGA), traditionally employed to score anatomical visualisation. METHODS Patient images (n = 66) were obtained using current and optimised imaging protocols from two CT suites: a 16-slice scanner at the national Maltese...

Reflecting the usefulness of multilevel analysis and the importance of categorical outcomes in many areas of research, generalization of multilevel models for categorical outcomes has been an active area of statistical research. For dichotomous response data, several approaches adopting either a logistic or probit regression model and various methods for incorporating and estimating the influen...

INTRODUCTION We present an ordinal logistic regression model for identification of items with differential item functioning (DIF) and apply this model to a Mini-Mental State Examination (MMSE) dataset. We employ item response theory ability estimation in our models. Three nested ordinal logistic regression models are applied to each item. Model testing begins with examination of the statistical...

gression enable us to investigate the relationship between a categorical outcome and a set of explanatory variables. The outcome or response can be either dichotomous (yes, no) or ordinal (low, medium, high). During dichotomous response, we are performing standard logistic regression and for ordinal response, model that uses standard logistic regression formula with feature selection using forw...

Logistic regression is one of statistical methods that used to analyze the correlation between categorical response variables and predictor which are or continuous. Many studies on logistic have been carried out by assuming variable its logit link function a linear relationship. However, in several cases it was found relationship not always linear, but could be quadratic, cubic, form other curv...