A network centrality method for the rating problem

We propose a new method for aggregating the information of multiple reviewers rating multiple products. Our approach is based on the network relations induced between products by the rating activity of the reviewers. We show that our method is algorithmically implementable even for large numbers of both products and consumers, as is the case for many online sites. Moreover, comparing it with the simple average, which is mostly used in practice, and with other methods previously proposed in the literature, it performs very well under various dimension, proving itself to be an optimal trade--off between computational efficiency, accordance with the reviewers original orderings, and robustness with respect to the inclusion of systematically biased reports.Comment: 25 pages, 8 figure

Class Attendance and Students’ Evaluations of Teaching: Do No-Shows Bias Course Ratings and Rankings?

Background: Many university departments use students’ evaluations of teaching (SET) to compare and rank courses. However, absenteeism from class is often nonrandom and, therefore, SET for different courses might not be comparable. Objective: The present study aims to answer two questions. Are SET positively biased due to absenteeism? Do procedures, which adjust for absenteeism, change course rankings? Research Design: The author discusses the problem from a missing data perspective and present empirical results from regression models to determine which factors are simultaneously associated with students’ class attendance and course ratings. In order to determine the extent of these biases, the author then corrects average ratings for students’ absenteeism and inspect changes in course rankings resulting from this adjustment. Subjects: The author analyzes SET data on the individual level. One or more course ratings are available for each student. Measures: Individual course ratings and absenteeism served as the key outcomes. Results: Absenteeism decreases with rising teaching quality. Furthermore, both factors are systematically related to student and course attributes. Weighting students’ ratings by actual absenteeism leads to mostly small changes in ranks, which follow a power law. Only a few, average courses are disproportionally influenced by the adjustment. Weighting by predicted absenteeism leads to very small changes in ranks. Again, average courses are more strongly affected than courses of very high or low in quality. Conclusions: No-shows bias course ratings and rankings. SET are more appropriate to identify high- and low-quality courses than to determine the exact ranks of average courses

Can Productive Capacity Differentials Really Explain Earnings Differentials Associated with Demographic Characteristics? Case of Experience

This study uses computerized personnel microdata on the white male managerial and professional employees at a major U.S. corporation to address the following question: Can the additional earnings which are associated with more labor market experience at a point in time really be explained by higher productivity at the same point in time? Our answer to this question, based on both cross-sectional and longitudinal information, is that performance plays a substantially smaller role in explaining cross-sectional experience-earnings differentials and earnings growth than is claimed by those who have adopted the human capital explanation of the experience-earnings profile. This response depends critically on our assumption that the performance ratings which supervisors give to their white male managerial and professional subordinates adequately reflect the subordinates' relative productivity in the year of assessment; we present a great deal of evidence which strongly supports this assumption.