Response biases

Response biases comprise a variety of systematic tendencies of responding to questionnaire items. Response biases exert an influence on item responses in addition to any constructs that the questionnaire is designed to measure and can therefore potentially bias the corresponding trait level estimates. This chapter addresses general response biases that are independent of item content, including response styles (e.g., extreme response style, acquiescence) and rater biases (halo effect, leniency/severity bias), as well as response biases that are related to item content and depend strongly on the context (socially desirable responding). The chapter summarizes research on correlates of response biases and research on inter-individual and cross-cultural differences in engaging in response styles and rater biases. It describes different methods that can be applied at the test construction stage to prevent or minimize the occurrence of response biases. Finally, it depicts methods developed for correcting for the effects of response biases.</p

A geometric construction of panel-regular lattices in buildings of types ~A_2 and ~C_2

Using Singer polygons, we construct locally finite affine buildings of types ~A_2 and ~C_2 which admit uniform lattices acting regularly on panels. This construction produces very explicit descriptions of these buildings as well as very short presentations of the lattices. All but one of the ~C_2-buildings are necessarily exotic. To the knowledge of the author, these are the first presentations of lattices in buildings of type ~C_2. Integral and rational group homology for the lattices is also calculated.Comment: 42 pages, small corrections and cleanup. Results are unchanged

A Model Handbook of Hiring and Employment Practices and Procedures for Selected Private K-12 Schools

The purpose of the project was to develop a model handbook of hiring and employment practices and procedures for private K-12 schools. To accomplish this purpose a review of literature and current policies or procedures from public and private schools, agencies and organizations was conducted. Additional related information from selected sources was obtained and analyzed

In The Candle - Light : Intermezzo

https://digitalcommons.library.umaine.edu/mmb-ps/2878/thumbnail.jp

Deep reinforcement learning from human preferences

For sophisticated reinforcement learning (RL) systems to interact usefully with real-world environments, we need to communicate complex goals to these systems. In this work, we explore goals defined in terms of (non-expert) human preferences between pairs of trajectory segments. We show that this approach can effectively solve complex RL tasks without access to the reward function, including Atari games and simulated robot locomotion, while providing feedback on less than one percent of our agent's interactions with the environment. This reduces the cost of human oversight far enough that it can be practically applied to state-of-the-art RL systems. To demonstrate the flexibility of our approach, we show that we can successfully train complex novel behaviors with about an hour of human time. These behaviors and environments are considerably more complex than any that have been previously learned from human feedback

THE END OF SUPPLY CONTROLS: THE ECONOMIC EFFECTS OF RECENT CHANGE IN FEDERAL PEANUT POLICY

The paper analyzes recent changes in U.S. peanut policy as enacted in the 2002 Farm Security Act. A model representing the impact of the 2002 farm bill on the domestic and foreign prices of edible peanuts is constructed and the gains and losses to peanut producing states are measured.Agricultural and Food Policy,