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The behavioral sciences, including most of psychology, seek to explain and predict behavior with the help of theories and models that involve concepts (e.g., attitudes) that are subsequently translated into measures. Currently, some subdisciplines such as social psychology focus almost exclusively on measures that demand reflection or even introspection when administered to persons. We argue that such a focus hinders progress in explaining behavior. One major reason is that such an exclusive focus on reflections results in common method bias, which then produces spurious relations, or in other words, low discriminant validity. Without the valid measurement of theoretical concepts, theoretical assumptions cannot be tested, and hence, theory development will be hampered. We argue that the use of a greater variety of methods would reduce these problems and would in turn foster theory building. Using a representative sample of N = 472 participants (age: M = 51.0, SD = 17.7; 54% female), we compared the validity of a classical introspective attitude measure (i.e., the New Ecological Paradigm) with that of an alternative attitude measure (i.e., the General Ecological Behavior scale). The latter measure, which was based on self-reported behavior, showed substantially better validity that we argue could aid theory development.
LICE is one of the four major LHC experiments at CERN. When the accelerator enters the Run 3 data-taking period, starting in 2021, ALICE expects almost 100 times more Pb-Pb central collisions than now, resulting in a large increase of data throughput. In order to cope with this new challenge, the collaboration had to extensively rethink the whole data processing chain, with a tighter integration between Online and Offline computing worlds. Such a system, code-named ALICE O2, is being developed in collaboration with the FAIR experiments at GSI. It is based on the ALFA framework which provides a generalized implementation of the ALICE High Level Trigger approach, designed around distributed software entities coordinating and communicating via message passing.
We will highlight our efforts to integrate ALFA within the ALICE O2 environment. We analyze the challenges arising from the different running environments for production and development, and conclude on requirements for a flexible and modular software framework. In particular we will present the ALICE O2 Data Processing Layer which deals with ALICE specific requirements in terms of Data Model. The main goal is to reduce the complexity of development of algorithms and managing a distributed system, and by that leading to a significant simplification for the large majority of the ALICE users.