Major perspectives concerning stress are presented with the goal of clarifying the nature of what has proved to be a heuristic but vague construct. Current conceptualizations of stress are challenged as being too phenomenological and ambiguous, and consequently, not given to direct empirical testing. Indeed, it is argued that researchers have tended to avoid the problem of defining stress, choosing to study stress without reference to a clear framework. A new stress model called the model of conservation of resources is presented as an alternative. This resource-oriented model is based on the supposition that people strive to retain, project, and build resources and that what is threatening to them is the potential or actual loss of these valued resources. Implications of the model of conservation of resources for new research directions are discussed.

Conservation of resources. A new attempt at conceptualizing stress.

An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models Multivariate techniques, including classification and ordination, are then introduced Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature The book is supported by a website that provides all data sets, questions for each chapter and links to software

/pdf/experimental-design-and-data-analysis-for-biologists-8ybva8d53x.pdf

Experimental Design and Data Analysis for Biologists

Preface Chapter 1 The challenge Chapter 2 The nature of the evidence: A synthesis of meta-analyses Chapter 3 The argument: Visible teaching and visible learning Chapter 4: The contributions from the student Chapter 5 The contributions from the home Chapter 6 The contributions from the school Chapter 7 The contributions from the teacher Chapter 8 The contributions from the curricula Chapter 9 The contributions from teaching approaches - I Chapter 10 The contributions from teaching approaches - II Chapter 11: Bringing it all together Appendix A: The 800 meta-analyses Appendix B: The meta-analyses by rank order References

Visible Learning: A Synthesis of Over 800 Meta-Analyses Relating to Achievement

While heated arguments between practitioners of qualitative and quantitative research have begun to test the very integrity of the social sciences, Gary King, Robert Keohane, and Sidney Verba have produced a farsighted and timely book that promises to sharpen and strengthen a wide range of research performed in this field. These leading scholars, each representing diverse academic traditions, have developed a unified approach to valid descriptive and causal inference in qualitative research, where numerical measurement is either impossible or undesirable. Their book demonstrates that the same logic of inference underlies both good quantitative and good qualitative research designs, and their approach applies equally to each. Providing precepts intended to stimulate and discipline thought, the authors explore issues related to framing research questions, measuring the accuracy of data and uncertainty of empirical inferences, discovering causal effects, and generally improving qualitative research. Among the specific topics they address are interpretation and inference, comparative case studies, constructing causal theories, dependent and explanatory variables, the limits of random selection, selection bias, and errors in measurement. Mathematical notation is occasionally used to clarify concepts, but no prior knowledge of mathematics or statistics is assumed. The unified logic of inference that this book explicates will be enormously useful to qualitative researchers of all traditions and substantive fields.

Designing Social Inquiry: Scientific Inference in Qualitative Research

Structural Equations with Latent Variables.

The logic of scientific discovery

Unauthorized freud: Doubters confront a legend

While there has been considerable progress in the study of fallacies over the twenty years since the publication of Hamblin's excellent monograph on the subject, the treatment of the fallacies of language still remains much as it has always been. The usual suspects-equivocation, amphiboly, accent, composition, and division-are rounded up, and covered in an abbreviated fashion. In this paper I would like to discuss another fallacy of language, hedging, with an eye to broadening our view of fallacies of language. Specifically, I want to accomplish three things. First, I want to convince the reader that hedging is indeed common (and so should be added to the admittedly burgeoning list of standard fallacies). Second, I want to give an adequate theoretical (i.e., linguistic) treatment of the fallacy. Third, I want to show how the standard treatment of fallacies of language can be modified to include not only hedging, but other fallacies as well. The fallacy of which I speak is exemplified by the following paradigm dialogue:

https://informallogic.ca/index.php/informal_logic/article/download/2649/2090

Gary James Jason

Papers

The logic of scientific discovery

Unauthorized freud: Doubters confront a legend

Hedging as a Fallacy of Language

Is there a case for ad hominem arguments

The nature of the argumentum ad baculum