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We consider partially observed binary matched-pair data. We assume that the incomplete subjects are missing at random. Within this missing framework, we propose an EM-algorithm based approach to construct an interval estimator of the proportion difference incorporating all the subjects. In conjunction with our proposed method, we also present two improvements to the interval estimator through some correction factors. The performances of the three competing methods are then evaluated through extensive simulation. Recommendation for the method is given based on the ability to preserve type-I error for various sample sizes. Finally, the methods are illustrated in two real-world data sets. An R-function is developed to implement the three proposed methods. [ABSTRACT FROM AUTHOR]

Cohen's kappa coefficient was originally proposed for two raters only, and it later extended to an arbitrarily large number of raters to become what is known as Fleiss' generalized kappa. Fleiss' generalized kappa and its large-sample variance are still widely used by researchers and were implemented in several software packages, including, among others, SPSS and the R package "rel." The purpose of this article is to show that the large-sample variance of Fleiss' generalized kappa is systematically being misused, is invalid as a precision measure for kappa, and cannot be used for constructing confidence intervals. A general-purpose variance expression is proposed, which can be used in any statistical inference procedure. A Monte-Carlo experiment is presented, showing the validity of the new variance estimation procedure. [ABSTRACT FROM AUTHOR]

This study develops a theoretical model for the costs of an exam as a function of its duration. Two kind of costs are distinguished: (1) the costs of measurement errors and (2) the costs of the measurement. Both costs are expressed in time of the student. Based on a classical test theory model, enriched with assumptions on the context, the costs of the exam can be expressed as a function of various parameters, including the duration of the exam. It is shown that these costs can be minimized in time. Applied in a real example with reliability.80, the outcome is that the optimal exam time would be much shorter and would have reliability.675. The consequences of the model are investigated and discussed. One of the consequences is that optimal exam duration depends on the study load of the course, all other things being equal. It is argued that it is worthwhile to investigate empirically how much time students spend on preparing for resits. Six variants of the model are distinguished, which differ in their weights of the errors and in the way grades affect how much time students study for the resit. [ABSTRACT FROM AUTHOR]

Reliable measurements are key to social science research. Multiple measures of reliability of the total score have been developed, including coefficient alpha, coefficient omega, the greatest lower bound reliability, and others. Among these, the coefficient alpha has been most widely used, and it is reported in nearly every study involving the measure of a construct through multiple items in social and behavioral research. However, it is known that coefficient alpha underestimates the true reliability unless the items are tau-equivalent, and coefficient omega is deemed as a practical alternative to coefficient alpha in estimating measurement reliability of the total score. However, many researchers noticed that the difference between alpha and omega is minor in applications. Since the observed differences in alpha and omega can be due to sampling errors, the purpose of the present study, therefore, is to propose a method to evaluate the difference of coefficient alpha ( α ^ ) and omega ( ω ^ ) statistically. In particular, the current article develops a procedure to estimate the SE of ( ω ^ − α ^ ) and consequently the confidence interval (CI) for ( ω − α ). This procedure allows us to test whether the observed difference ( ω ^ − α ^ ) is due to sample error or ω ^ is significantly greater than α ^ . The developed procedure is then applied to multiple real data sets from well-known scales to empirically verify the values of ( ω ^ − α ^ ) in practice. Results showed that in most of the comparisons the differences are significantly above zero but cases also exist where the CIs contain zero. An R program for calculating ω ^ , α ^ , and the SE of ( ω ^ − α ^ ) is also included in the present study so that the developed procedure is easily accessible to applied researchers. [ABSTRACT FROM AUTHOR]