A Matlab based software for logistic regression is developed to enhance the process of teaching quantitative topics and assist researchers with analyzing wide area of applications where categorical data is involved. The software offers an option of performing stepwise logistic regression to select the most significant predictors. The software includes a feature to detect influential observation...

Data often have hierarchical or clustered structures, such as patients clustered within hospitals or students nested within schools. Hierarchical models are statistical models that are used to analyze hierarchical or multilevel data. SAS GLIMMIX procedure is a new and highly useful tool for hierarchical modeling with discrete responses. This paper is focused on hierarchical logistic regression ...

The common practice of collapsing inherently continuous or ordinal variables into two categories causes information loss that may potentially weaken power to detect effects of explanatory variables and result in Type II errors in statistical inference. The purpose of this investigation was to illustrate, using a substantive example, the potential increase in power gained from an ordinal instead...

This paper presents an empirical investigation of the critical determinants for the adoption of e-market in Australian small-and-medium sized enterprises (SMEs) within the technology-organization-environment (TOE) framework. A conceptual model is proposed for better understanding the adoption of e-market in Australian SMEs. Structural equation modelling is used for testing and validating the co...

The aim of this research is to study and develop models for driver’s lane-changing behaviour in an urban area using the logistic regression method. A pilot study was conducted using a videotape recording technique to film an approach road leading to a signalised intersection in an urban road during the morning off-peak period. Inter related coding methods were designed to described and verify t...