Additional Thoughts on Rigor in Wildlife Science: Unappreciated Impediments

Traditionally, most scientists accepted reductionist and mechanistic approaches as the rigorous way to do science. Sells et al. (2018) recently raised the argument about reliability in wildlife science. Chamberlin (1890), Platt (1964), Romesburg (1981, 1991, 2009), and Williams (1997) were rightly referenced as very influential papers. My intention in this letter is not to refute the essence of the Sells et al. (2018) commentary but to add seldom addressed but important aspects that influence the attainment of rigor and certainty in wildlife studies. The elements of a rigorous approach (i.e., strong inference) as described by Platt (1964) included devising alternative hypotheses, devising ≥1 crucial experiments that will exclude ≥1 of the hypotheses, and carrying out the experiment to get a clean result. The process was then repeated using logical inductive trees (i.e., a continually bifurcated statement hypotheses approach) to obtain the essential cause for the effect. Platt (1964) agreed with Popper (1959) that science advanced only by disproof. He argued that this was a hard doctrine and leads to disputations between scientists, but that Chamberlin\u27s (1890) method of multiple working hypotheses helped to remove that difficulty. Platt (1964) emphasized inductive inference and crucial and critical experiments whereby alternate hypotheses are refuted. Romesburg (1981) explained that in wildlife biology, induction (reliable associations) and retroduction (developing hypotheses) were the basis for almost all wildlife research but were not sufficient. He proposed the hypothetical‐deductive (H‐D) method as a more reliable approach. Citing Harvey (1969), and Popper (1962), Romesburg (1981:294) explained that “Starting with the research hypothesis, usually obtained by retroduction, predictions are made about other classes of facts that should be true if the research hypothesis is actually true.” The hypothesis is then tested indirectly by using logic to deduce one or more test consequences (Romesburg 2014). Data are then collected in a statistical framework. Romesburg (1981) distinguished between a research hypothesis (i.e., a conjecture about some process) versus a statistical hypothesis (i.e., a conjecture about classes of facts encompassed by the process). Williams (1997) clearly explained the differences between necessary and sufficient causation and gave examples of the coherent logic both entailed. He summarized that the science endeavor included theory, hypotheses, predictions, observations, and comparison of predictions against data, and argued that inductive and deductive logic were required for testing hypotheses. Importantly, Williams (1997:1014) recognized that wildlife biology often involves simultaneous complementary explanatory factors, requiring “the framing of many scientifically interesting issues about cause and effect in terms of the relative contribution of multiple causal factors.” Over the years, many others have addressed the issue of rigor and reliability in the Journal of Wildlife Management (JWM) and the Wildlife Society Bulletin (WSB) either directly (McNab 1983, Eberhardt 1988, Anderson 2001) or indirectly (Steidl et al. 1997, Guthery et al. 2001). This is not a complete list and is limited primarily to JWM and WSB but gives an idea of the wide interest in achieving reliable results from wildlife studies

How small and medium enterprises are using social networks? Evidence from the Algarve region

The evolution of internet created new opportunities for small and medium enterprises (SME), among which are social networks. This work aims at analyzing the potential of these networks for the SME in Algarve, creating a questionnaire for the purpose. The empirical study revealed that some firms have already an integrated business strategy with social networks, as well as a group in the firm responsible for it. Most of their managers consider that social networks enhance performance, but few really measure these results. A categorical principal component analysis identified two dimensions of social networks’ use: social networks for product-client interaction and knowledge; and social networks with potential for marketing. A supplementary analysis (hierarchical clustering) identified three patterns of SME’s involvement in social networks: cluster Social Net Level 1, cluster Social Net Level 2 and cluster Social Net Level 3. These groups validated the results described above, indicating a sustainable methodological approach

Why Tenth Graders Fail to Finish High School: A Dropout Typology Latent Class Analysis

A large percentage of the students who drop out of K-12 schools in the United States do so at the end of high school, at some point after grade 10. Yet we know little about the differences between different types of students who drop out of the end of high school. The purpose of this study is to examine a typology of high school dropouts from a large nationally representative dataset (ELS:2002) using latent class analysis (LCA). We found three significantly different types of dropouts; Quiet, Jaded, and Involved. Based on this typology of three subgroups, we discuss implications for future dropout intervention research, policy, and practice

Characterization of complex networks: A survey of measurements

Each complex network (or class of networks) presents specific topological features which characterize its connectivity and highly influence the dynamics of processes executed on the network. The analysis, discrimination, and synthesis of complex networks therefore rely on the use of measurements capable of expressing the most relevant topological features. This article presents a survey of such measurements. It includes general considerations about complex network characterization, a brief review of the principal models, and the presentation of the main existing measurements. Important related issues covered in this work comprise the representation of the evolution of complex networks in terms of trajectories in several measurement spaces, the analysis of the correlations between some of the most traditional measurements, perturbation analysis, as well as the use of multivariate statistics for feature selection and network classification. Depending on the network and the analysis task one has in mind, a specific set of features may be chosen. It is hoped that the present survey will help the proper application and interpretation of measurements.Comment: A working manuscript with 78 pages, 32 figures. Suggestions of measurements for inclusion are welcomed by the author

The Development of a Conceptual Framework and Tools to Assess Undergraduates' Principled Use of Models in Cellular Biology

Recent science education reform has been marked by a shift away from a focus on facts toward deep, rich, conceptual understanding. This requires assessment that also focuses on conceptual understanding rather than recall of facts. This study outlines our development of a new assessment framework and tool—a taxonomy— which, unlike existing frameworks and tools, is grounded firmly in a framework that considers the critical role that models play in science. It also provides instructors a resource for assessing students' ability to reason about models that are central to the organization of key scientific concepts. We describe preliminary data arising from the application of our tool to exam questions used by instructors of a large-enrollment cell and molecular biology course over a 5-yr period during which time our framework and the assessment tool were increasingly used. Students were increasingly able to describe and manipulate models of the processes and systems being studied in this course as measured by assessment items. However, their ability to apply these models in new contexts did not improve. Finally, we discuss the implications of our results and the future directions for our research