The Differential Impact of the COVID-19 Crisis on Small-Scale Development Initiatives,a Cross-Country Comparison

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - The COVID-19 pandemic presents Northern-based development organisations with unprecedented difficulties. They are...     

A comparison of linear regression techniques in method comparison studies

In this study, the performances of linear regression techniques, which are especially used in clinical chemistry in method comparison studies, are compared via the Monte-Carlo simulation. The regression techniques that take the measurement errors of both dependent and independent variables into account are called Type II regression techniques. In this study, we also compare the performances of Type II and Type I (classical regression techniques that do not take the measurement errors of the independent variable into account) regression techniques for different sample sizes and different shape parameters of the Weibull distribution. The mean square error is used as a performance criterion of each technique. MATLAB 7.02 software is used in the simulation study. As a result, in all conditions, the ordinary least-square (OLS)-bisector regression technique, which bisects the OLS(Y | X) and the OLS(X | Y), shows the best performance.     

A randomized response procedure for multiple-sensitive questions

The Randomized Response (RR) technique is a well-established interview procedure which guarantees privacy protection in social surveys dealing with sensitive items. The RR method assumes a stochastic mechanism to create uncertainty about the true status of the respondents in order to ensure privacy protection and to avoid tendencies to dissimulate or respond in a socially desirable direction. A very general model for the RR method was introduced by Franklin (Commun Stat Theory Methods 18:489?C505, 1989) when a single-sensitive question is under study. However, since social surveys are often based on questionnaires containing more than a single-sensitive question, the analysis of multivariate RR data is of considerable interest. This paper focuses on the generalization of the Franklin model in a multiple-sensitive question setting and on related inferential issues.