Miguel Ángel Montero Alonso

Gender differences in e-learning satisfaction

Student learning skills differ depending on gender. The importance of studying this situation in the classroom is that recommendations can be made taking gender into consideration. In e-learning, the roles of students and teachers change. In line with recent research, the question this paper raises is whether or not gender differences also exist in e-learning. The main contribution this research makes to the literature is to provide evidence on the issues male and female students consider important in their assessment of e-subjects. This study is based on a sample of 1185 students who are doing on-line courses at the Universidad de Granada in Spain. The main conclusion is that female students are more satisfied than male students with the e-learning subjects that make up the sample. Furthermore, we find that female students assign more importance to the planning of learning, as well as to being able to contact the teacher in various ways.

Confidence Intervals of Weighted Kappa Coefficient of a Binary Diagnostic Test

Sensitivity and specificity are classic parameters to assess the performance of a binary diagnostic test. Another useful parameter to measure the performance of a binary test is the weighted kappa coefficient, which is a measure of the classificatory agreement between the binary test and the gold standard. Various confidence intervals are proposed for the weighted kappa coefficient when the binary test and the gold standard are applied to all of the patients in a random sample. The results have been applied to the diagnosis of coronary artery disease.

Determining sample size to evaluate and compare the accuracy of binary diagnostic tests in the presence of partial disease verification

Calculating sample size to evaluate the accuracy of a binary diagnostic test and to compare the accuracy of two binary diagnostic tests is an important question in the study of diagnostic statistical methods. In the presence of partial disease verification, the disease status of some patients in the sample is unknown, so that the calculation of sample size can be complicated. A method to calculate sample size when evaluating the sensitivity and the specificity of a binary diagnostic test and when comparing the sensitivity and specificity of two binary tests in the presence of partial disease verification is proposed. The results obtained were applied to the diagnosis of coronary stenosis.

Global hypothesis test to simultaneously compare the predictive values of two binary diagnostic tests

The positive and negative predictive values of a binary diagnostic test are measures of the clinical accuracy of the diagnostic test, which depend on the sensitivity and specificity of the diagnostic test and the disease prevalence, and therefore they are two interdependent parameters. The comparisons of predictive values in paired designs do not consider the dependence between predictive values. A global hypothesis test has been studied in order to simultaneously compare the predictive values of two or more binary diagnostic tests when the binary tests and the gold standard are applied to all of the individuals in a random sample. This global hypothesis test is an asymptotic hypothesis test based on the chi-square distribution. Simulation experiments have been carried out in order to study the type I error and the power of the global hypothesis test when comparing the predictive values of two and three binary diagnostic tests, respectively. From the results of the simulation experiments, a method has been proposed to simultaneously compare the predictive values of two or more binary diagnostic tests. The results have been applied to the diagnosis of coronary disease.

Global Hypothesis Test to Simultaneously Compare the Predictive Values of Two Binary Diagnostic Tests in Paired Designs: a Simulation Study

The positive and negative predictive values of a binary diagnostic test are measures of the clinical accuracy of the diagnostic that depend on the sensitivity and the specificity of the binary test and on the disease prevalence. Moreover, the positive predictive value and the negative predictive value are not parameters which are independent of each other. In this article, a global hypothesis test is studied to simultaneously compare the positive and negative predictive values of two binary diagnostic tests in paired designs.