Why Weightlifters Grunt: A Classroom Exercise That Introduces Students to Evolution

A classroom exercise is described in which college students take part in creating and supporting an evolutionary hypothesis that explains effort grunting. The exercise holds their interest throughout and readies them to understand hypotheses of animal and plant evolution. It informs them about the dependence of cultural evolution upon biological evolution, and it connects widely to curricula

How About It, Writer?

This book contains lists of 1,616 great opening sentences from nonfiction pieces, 1,633 great titles, and hundreds of great transition sentences, ways of saying “for example,” and ways of closing nonfiction pieces -- along with instructions and illustrations for using them as models for your writing. For instance, suppose you are writing a piece and you feel that your opening sentence is not as good as it could be, but you don’t see how to create a better one. To escape the mental block, just go to the list of 1,616 opening sentences and look around for hints. In minutes the sentence you’re wishing for will flash into your mind. You won’t be plagiarizing; the sentence you create will be one of a kind. The author, a university professor, compiled the lists from reading more than 12,000 published essays

Wildlife Science: Gaining Reliable Knowledge

Two scientific methods called induction and retroduction form the basis for almost all wildlife research. Induction is used to establish reliable associations among sets of facts, whereas retroduction is used to establish research hypotheses about the fact-giving processes driving nature. A 3rd scientific method, the hypothetico-deductive (H-D), is a means for testing research hypotheses, i.e., for gauging their reliability. The H-D method is rarely used in wildlife science. Instead, research hypotheses are proposed, and either made into a law through verbal repetition or lose favor and are forgotten. I develop the thesis that wildlife research should use the H-D method to test research hypotheses, using the threshold- of-security hypothesis for winter mortality for illustration. I show that persistent confusions about the definitions of concepts like carrying capacity, correlation and cause-and-effect, and the reliability of know 1- edge gained from computer simulation models stem from either inadequate or misused scientific methods

Use of Cluster Analysis in Leisure Research

Cluster analysis is the generic name of data analysis techniques for appraising similarities among a set of objects described by measurements made on their attributes. In leisure research, the need to use cluster analysis appears, for example, when similarities among a sample of recreation participants (the objects), given a measure of individual participation in different activities (the attributes), is investigated. The principles of cluster analysis are explained through an example, the more commonly used methods are identified and, where applicable, their application in leisure research is referenced, and advice on using these techniques is offered

Cluster Analysis for Researchers

This book explains and illustrates the most frequently used methods of hierarchical cluster analysis so that they can be understood and practiced by researchers with limited backgrounds in mathematics and statistics. Widely applicable in research, these methods are used to determine clusters of similar objects. For example, ecologists use cluster analysis to determine which plots (i.e. objects) in a forest are similar with respect to vegetation growing on them; medical researchers use cluster analysis to determine which brands of products the public perceives similarly. In all fields of research, there exists this basic and recurring need to determine clusters of objects. This book will ground you in the basic methods of cluster analysis and guide you in all phases of their use. You will learn how to recognize when you have a research problem that requires cluster analysis, how to decide upon the most appropriate kind of data to collect, how to choose the best method of cluster analysis for your problem, how to obtain a computer program to perform the necessary calculations, and how to interpret the results

CLUSTAR-PC: Interactive Program for Hierarchical Cluster Analysis

CLUSTAR-PC performs hierarchical cluster analysis and provides for (a) 6 methods of data standardization, (b) 14 commonly used (dis)similarity coefficients for binary or multistate data, (c) 9 coefficients for interval/ratio scaled data, and (d) 4 clustering methods-single linkage, complete linkage, average linkage, and minimum variance (Ward\u27s method). It also allows for data matrices with attributes measured on a mixture of binary and interval/ ratio scales. Dendrograms are produced, and the program will label the branches according to names the user specifics