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Strien, A. J., Swaay, C. A. M., & Termaat, T. (2013). Opportunistic citizen science data of animal species produce reliable estimates of distribution trends if analysed with occupancy models. J Appl Ecol, 50(6), 1450–1458.
Abstract: Summary Many publications documenting large-scale trends in the distribution of species make use of opportunistic citizen data, that is, observations of species collected without standardized field protocol and without explicit sampling design. It is a challenge to achieve reliable estimates of distribution trends from them, because opportunistic citizen science data may suffer from changes in field efforts over time (observation bias), from incomplete and selective recording by observers (reporting bias) and from geographical bias. These, in addition to detection bias, may lead to spurious trends. We investigated whether occupancy models can correct for the observation, reporting and detection biases in opportunistic data. Occupancy models use detection/nondetection data and yield estimates of the percentage of occupied sites (occupancy) per year. These models take the imperfect detection of species into account. By correcting for detection bias, they may simultaneously correct for observation and reporting bias as well. We compared trends in occupancy (or distribution) of butterfly and dragonfly species derived from opportunistic data with those derived from standardized monitoring data. All data came from the same grid squares and years, in order to avoid any geographical bias in this comparison. Distribution trends in opportunistic and monitoring data were well-matched. Strong trends observed in monitoring data were rarely missed in opportunistic data. Synthesis and applications. Opportunistic data can be used for monitoring purposes if occupancy models are used for analysis. Occupancy models are able to control for the common biases encountered with opportunistic data, enabling species trends to be monitored for species groups and regions where it is not feasible to collect standardized data on a large scale. Opportunistic data may thus become an important source of information to track distribution trends in many groups of species.
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Sabou, M., Bontcheva, K., & Scharl, A. (2012). Crowdsourcing Research Opportunities: Lessons from Natural Language Processing. In Proceedings of the 12th International Conference on Knowledge Management and Knowledge Technologies (pp. 17–1). i-KNOW â��12. New York, NY, USA: Acm.
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Podsakoff, P. M., MacKenzie, Scott B., Lee, Jeong-Yeon, Podsakoff, Nathan P. (3002). Common method biases in behavioral research: A critical review of the literature and recommended remedies. J. Appl. Psychol., 85(5), 879–903.
Abstract: Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
$11.95
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Bücheler, T., & Sieg, J. H. (2011). Understanding Science 2.0: Crowdsourcing and Open Innovation in the Scientific Method. Proceedings of the 2nd European Future Technologies Conference and Exhibition 2011 (FET 11), 7, 327–329.
Abstract: The innovation process is currently undergoing significant change in many industries. The World Wide Web has created a virtual world of collective intelligence and helped large groups of people connect and collaborate in the innovation process [1]. Von Hippel [2], for instance, states that a large number of users of a given technology will come up with innovative ideas. This process, originating in business, is now also being observed in science. Discussions around “Citizen Science” [3] and “Science 2.0” [4] suggest the same effects are relevant for fundamental research practices. “Crowdsourcing” [5] and “Open Innovation” [6] as well as other names for those paradigms, like Peer Production, Wikinomics, Swarm Intelligence etc., have become buzzwords in recent years. However, serious academic research efforts have also been started in many disciplines. In essence, these buzzwords all describe a form of collective intelligence that is enabled by new technologies, particularly internet connectivity. The focus of most current research on this topic is in the for-profit domain, i.e. organizations willing (and able) to pay large sums to source innovation externally, for instance through innovation contests. Our research is testing the applicability of Crowdsourcing and some techniques from Open Innovation to the scientific method and basic science in a non-profit environment (e.g., a traditional research university). If the tools are found to be useful, this may significantly change how some research tasks are conducted: While large, apriori unknown crowds of “irrational agents” (i.e. humans) are used to support scientists (and teams thereof) in several research tasks through the internet, the usefulness and robustness of these interactions as well as scientifically important factors like quality and validity of research results are tested in a systematic manner. The research is highly interdisciplinary and is done in collaboration with scientists from sociology, psychology, management science, economics, computer science, and artificial intelligence. After a pre-study, extensive data collection has been conducted and the data is currently being analyzed. The paper presents ideas and hypotheses and opens the discussion for further input.
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Hiby, E. F., Rooney, N. J., & Bradshaw, J. W. S. (2004). Dog training methods: their use, effectiveness and interaction with behaviour and welfare. Anim. Welf., 13(1), 63–69.
Abstract: Historically, pet dogs were trained using mainly negative reinforcement or punishment, but positive reinforcement using rewards has recently become more popular. The methods used may have different impacts on the dogs� welfare. We distributed a questionnaire to 364 dog owners in order to examine the relative effectiveness of different training methods and their effects upon a pet dog�s behaviour. When asked how they trained their dog on seven basic tasks, 66% reported using vocal punishment, 12% used physical punishment, 60% praise (social reward), 51% food rewards and 11% play. The owner�s ratings for their dog�s obedience during eight tasks correlated positively with the number of tasks which they trained using rewards (P<0.01), but not using punishment (P=0.5). When asked whether their dog exhibited any of 16 common problematic behaviours, the number of problems reported by the owners correlated with the number of tasks for which their dog was trained using punishment (P<0.001), but not using rewards (P=0.17). Exhibition of problematic behaviours may be indicative of compromised welfare, because such behaviours can be caused byor result ina state of anxiety and may lead to a dog being relinquished or abandoned. Because punishment was associated with an increased incidence of problematic behaviours, we conclude that it may represent a welfare concern without concurrent benefits in obedience. We suggest that positive training methods may be more useful to the pet-owning community.
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