This textbook will help graduate students in non-statistics disciplines, advanced undergraduate researchers, and research faculty in the health sciences to learn, use and communicate results from many commonly used statistical methods. The material covered, and the manner in which it is presented, describe the entire data analysis process from hypothesis generation to writing the results in a manuscript. Chapters cover, among other topics: one and two-sample proportions, multi-category data, one and two-sample means, analysis of variance, and regression. Throughout the text, the authors explain statistical procedures and concepts using a non-statistical language. This accessible approach is complete with real-world examples and sample write-ups for the Methods and Results sections of scholarly papers. The text also allows for the concurrent use of the programming language R, which is an open-source program created, maintained and updated by the statistical community. R is freely available and easy to download.
Quantitative and Statistical Research Methods This user-friendly textbook teaches students to understand andapply procedural steps in completing quantitative studies. Itexplains statistics while progressing through the steps of thehypothesis-testing process from hypothesis to results. The researchproblems used in the book reflect statistical applications relatedto interesting and important topics. In addition, the book providesa Research Analysis and Interpretation Guide to help studentsanalyze research articles. Designed as a hands-on resource, each chapter covers a singleresearch problem and offers directions for implementing theresearch method from start to finish. Readers will learn howto: Pinpoint research questions and hypotheses Identify, classify, and operationally define the studyvariables Choose appropriate research designs Conduct power analysis Select an appropriate statistic for the problem Use a data set Conduct data screening and analyses using SPSS Interpret the statistics Write the results related to the problem Quantitative and Statistical Research Methods allows students toimmediately, independently, and successfully apply quantitativemethods to their own research projects.
The first all-inclusive introduction to modern statistical research methods in the natural resource sciences The use of Bayesian statistical analysis has become increasingly important to natural resource scientists as a practical tool for solving various research problems. However, many important contemporary methods of applied statistics, such as generalized linear modeling, mixed-effects modeling, and Bayesian statistical analysis and inference, remain relatively unknown among researchers and practitioners in this field. Through its inclusive, hands-on treatment of real-world examples, Contemporary Bayesian and Frequentist Statistical Research Methods for Natural Resource Scientists successfully introduces the key concepts of statistical analysis and inference with an accessible, easy-to-follow approach. The book provides case studies illustrating common problems that exist in the natural resource sciences and presents the statistical knowledge and tools needed for a modern treatment of these issues. Subsequent chapter coverage features: An introduction to the fundamental concepts of Bayesian statistical analysis, including its historical background, conjugate solutions, Bayesian hypothesis testing and decision-making, and Markov Chain Monte Carlo solutions The relevant advantages of using Bayesian statistical analysis, rather than the traditional frequentist approach, to address research problems Two alternative strategies—the a posteriori model selection strategy and the a priori parsimonious model selection strategy using AIC and DIC—to model selection and inference The ideas of generalized linear modeling (GLM), focusing on the most popular GLM of logistic regression An introduction to mixed-effects modeling in S-Plus® and R for analyzing natural resource data sets with varying error structures and dependencies Each statistical concept is accompanied by an illustration of its frequentist application in S-Plus® or R as well as its Bayesian application in WinBUGS. Brief introductions to these software packages are also provided to help the reader fully understand the concepts of the statistical methods that are presented throughout the book. Assuming only a minimal background in introductory statistics, Contemporary Bayesian and Frequentist Statistical Research Methods for Natural Resource Scientists is an ideal text for natural resource students studying statistical research methods at the upper-undergraduate or graduate level and also serves as a valuable problem-solving guide for natural resource scientists across a broad range of disciplines, including biology, wildlife management, forestry management, fisheries management, and the environmental sciences.
Available for bundling with Rao's text, this unique companion shows in great detail how to use SAS to do the statistics described in the text. Written specifically to complement and enhance the SAS material in the book, the SAS Companion uses the same examples used in the text, providing instructions and output for all textual examples. The SAS Companion is an essential tool and a handy reference for students as they work through the books' computing assignments.
This book has been written to meet several needs. Firstly there is a need for a book which integrates statistics, research design, ex periments and report writing so that none is learned in a vacuum, as commonly is the case, isolated from the others. The aim is to make the student an active learner encouraged to carry out experiments, so ex periencing and understanding the design problems and statistical analyses in the practical context where he can see exactly what he is doing and why. The aim is that by the end of the book, the student should be able to evaluate the research of others, to define a problem, formulate a hypothesis about it, design and carry out the experiment, apply the correct statistics, discuss the results and implications, and write it all up in a logical and sensible fashion. The principle is that old pedagogic one of learning by doing. Secondly, there is a need for an introductory text on statistics, research design and experimental work for the many students who meet psychology and social science for the first time. The initiate in behavioural science needs to gain a conceptual understanding of statistical procedures and design techniques in order to carry out his own investigations and to understand and evaluate constructively the investigations of others. However, experience has shown us that many students (and even some fellow teachers) are somewhat reluctant to study this area as they believe it is difficult and involves mathematics.
Research methods and statistics are central to the development of professional competence and evidence based psychological practice. (Noun, masculine) research on the development of psychological literacy. Despite this, many psychology students express little interest in, and in some cases of active dislike of, learning research methods and statistics. This ebook brings together current research, innovative evidence-based practice, and critical discourse.
RESEARCH METHODS AND STATISTICS: A CRITICAL THINKING APPROACH, 4e, successfully illustrates the integration between statistics and research methods by demonstrating the ways to use statistics in analyzing data collected during research. Jackson’s combined text adopts an inviting narrative style that speaks directly to students and draws them into the material, helping them overcome the initial apprehension they may feel at having to learn both subject areas at once. She incorporates a student-friendly critical-thinking approach and presents examples and exercises to which students can relate. Jackson focuses on the logic of the process and the methodology aspect of research. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.
A Gentle Conversation, Third Edition, is meant to be a student-friendly introductionto research methodology and statistics, aimed at allaying students' fears and anxietiesabout studying these topics. Our more conversational approach should help studentsfeel as if the authors are standing by them, explaining concepts and procedures as theyread through the text. We use examples throughout to clarify concepts and strengthen theconnections between statistics, data, and research questions. The authors emphasizeunderstanding not only the manipulation of statistical data, but also what the actualfindings mean in relation to significance issues, samples, and populations. We covereffect size for all statistical inquiries, from correlation to ANOVA.
This supplementary text serves as a manual for SPSS use for social statistics and research methods classes. It is a useful guide for students working independently on a research proposal, project, or paper. Also, it is an excellent resource for instructors to use with some or all of the lab components of their course.