Statistical Analysis of Questionnaire Data via Cumulative Logistic Regression Model

cumulative logistic regression vs ordinary 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...

Bayesian Logistic Regression Model Choice via Laplace-Metropolis Algorithm

Following a Bayesian statistical inference paradigm, we provide an alternative methodology for analyzing a multivariate logistic regression. We use a multivariate normal prior in the Bayesian analysis. We present a unique Bayes estimator associated with a prior which is admissible. The Bayes estimators of the coefficients of the model are obtained via MCMC methods. The proposed procedure...

Common pitfalls in statistical analysis: Logistic regression

Logistic regression analysis is a statistical technique to evaluate the relationship between various predictor variables (either categorical or continuous) and an outcome which is binary (dichotomous). In this article, we discuss logistic regression analysis and the limitations of this technique.

Macroscopic Congestion Intensity Measurement Model Based on Cumulative Logistic Regression

An efficient and accurate measurement of congestion intensity helps investigate the traffic conditions at different road classes and provides useful information for transportation planning and traffic operation improvement. However, the existing traffic congestion intensity measurement models often suffer from two major problems: one is that there is no generally accepted method that can be use...

Logistic Regression Analysis of Non-randomized Response Data Collected via the Parallel Model in Sensitive Surveys