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This is an advanced undergraduate - or postgraduate - level text designed for courses in research methods and intermediate quantitative methods offered in departments of psychology, education, sociology and communication. Equally emphasizing the collection and analysis of research data, students should be able to plan an original study, collect and analyze data and report the results of the study in a professional manner.
This book is written for behavioral scientists who want to consider adding R to their existing set of statistical tools, or want to switch to R as their main computation tool. The authors aim primarily to help practitioners of behavioral research make the transition to R. The focus is to provide practical advice on some of the widely-used statistical methods in behavioral research, using a set of notes and annotated examples. The book will also help beginners learn more about statistics and behavioral research. These are statistical techniques used by psychologists who do research on human subjects, but of course they are also relevant to researchers in others fields that do similar kinds of research. The authors emphasize practical data analytic skills so that they can be quickly incorporated into readers’ own research.
Decision analysis is a technology designed to help individuals and organizations make wise inferences and decisions. It synthesises ideas from economics, statistics, psychology, operations research, and other disciplines. A great deal of behavioural research is relevant to decision analysis; behavioural scientists have both suggested easy and natural ways to describe and quantify problems and shown the kind of errors to which unaided intuitive judgements can lead. This long-awaited book offers the4first integrative presentation of the principles of decision analysis in a behavioural context. The authors break new ground on a variety of technical topics (sensitivity analysis, the value-utility distinction, multistage inference, attitudes toward risk), and attempt to make intuitive sense out of what have been treated in the literature as endemic biases and other errors of human judgement. Those interested in artificial intelligence will find it the easiest presentation of hierarchical Bayesian inference available.
Strategies and Tactics of Behavioral Research and Practice focuses on the most effective methods for measuring and evaluating changes in behavior. The authors provide the rationale for different procedures for measuring behavior and designing within-subject comparisons between control and intervention conditions. The text explains the strengths and weaknesses of methodological alternatives for every topic so that behavioral researchers and practitioners can make the best decisions in each situation. This classic text has been extensively revised to be more accessible and practical. Not only does it feature much more discussion of how research methods are relevant to today’s practitioners, it also includes additional examples based on field research and service delivery scenarios. With expanded coverage on creating experimental designs, as well as new chapters on behavioral assessment, the statistical analysis of data, and ethical issues associated with research methods, this book provides a strong foundation for direct behavioral measurement, within-subject research design, and interpretation of behavioral interventions. Enriched with more pedagogical features, including key terms, tables summarizing important points, figures to help readers visualize text, and updated examples and suggested readings, this book is an invaluable resource for students taking courses in research methods. This book is appropriate for researchers and practitioners in behavior analysis, psychology, education, social work, and other social and health science programs that address questions about behavior in research or practice settings.
The branch of clinical psychology known as behavior modification or, synonymously, applied behavior analysis, has grown substantially from humble beginnings in the 1960s. Many colleges and universities now offer courses in applied behavior analysis, and more than a few grant degrees in the area. Although they remain controversial, behavior mod ification procedures have been used to good advantage in dealing with a range of problem behaviors and are now rather widely employed in schools, residential institutions, and other therapeutic facilities. The two hallmarks of applied behavior analysis are utilization of the principles of operant conditioning to improve human behavior and utilization of scientific research methodology to assess the effectiveness of treatments. The present text provides an overview of several issues peculiar to applied behavior analysis research methodology. Six general areas of concern are (a) trends in applied behavior analysis research, (b) assessment and measurement issues, (c) experimental designs and strategies, (d) interpretation of findings, (e) ethical issues in applied behavior analysis, and (f) the societal impact of studies in the area. As evidenced by a sizable number of recently published articles, these topics are of considerable interest to behavior analysts. They also are relevant for students of scientific epistemology and general psychological research methods.
This very practical, how-to text provides the beginning researcher with the basics of applied behavior analysis research methods. In 10 logical steps, this text covers all of the elements of single-subject research design and it provides practical information for designing, implementing, and evaluating studies. Using a pocketbook format, the authors provide novice researcher with a "steps-for-success" approach that is brief, to-the-point, and clearly delineated.
Originally published in 1977, these examples of research and scholarly argument were collected in honor of Professor Sidney W. Bijou. In the language of academics, they constitute a Festschrift: a festival of scholarly writing, performed to celebrate the career of a person who produced, and stimulated others to produce, exactly such contributions throughout a long, valuable, and productive professional history. Since 1955, Dr Bijou had worked almost exclusively within the approach variously labelled as the functional analysis of behavior, the experimental analysis of behavior, operant conditioning, or Skinnerian psychology. From his point of view, it seems clear, the first of these labels was the correct one. It was the principle of objective, direct, observable analysis that attracted him.
Rigorous, yet readable. The author presents the material with sufficient elaboration, explanation, and examples that not only interest the student, but make it understandable. Introduction to Behavioral Research Methods incorporates the four basic approaches to behavioral research (descriptive research, correlational research, experimental research, and quasi-experimental research), and shows students how to conceptualise questions, measure variables, design studies, and analyse data. Chapters on research ethics and scientific writing (including the most recent version of APA style) round out the book. Throughout each chapter, boxes on “Developing Your Research Skills” and “Behavioral Research Case Study” provide practical examples and pique student interest. The full text downloaded to your computer With eBooks you can: search for key concepts, words and phrases make highlights and notes as you study share your notes with friends eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps. Upon purchase, you'll gain instant access to this eBook. Time limit The eBooks products do not have an expiry date. You will continue to access your digital ebook products whilst you have your Bookshelf installed.
Guides readers through the quantitative data analysis process including contextualizing data within a research situation, connecting data to the appropriate statistical tests, and drawing valid conclusions Introduction to Quantitative Data Analysis in the Behavioral and Social Sciences presents a clear and accessible introduction to the basics of quantitative data analysis and focuses on how to use statistical tests as a key tool for analyzing research data. The book presents the entire data analysis process as a cyclical, multiphase process and addresses the processes of exploratory analysis, decision-making for performing parametric or nonparametric analysis, and practical significance determination. In addition, the author details how data analysis is used to reveal the underlying patterns and relationships between the variables and connects those trends to the data’s contextual situation. Filling the gap in quantitative data analysis literature, this book teaches the methods and thought processes behind data analysis, rather than how to perform the study itself or how to perform individual statistical tests. With a clear and conversational style, readers are provided with a better understanding of the overall structure and methodology behind performing a data analysis as well as the needed techniques to make informed, meaningful decisions during data analysis. The book features numerous data analysis examples in order to emphasize the decision and thought processes that are best followed, and self-contained sections throughout separate the statistical data analysis from the detailed discussion of the concepts allowing readers to reference a specific section of the book for immediate solutions to problems and/or applications. Introduction to Quantitative Data Analysis in the Behavioral and Social Sciences also features coverage of the following: • The overall methodology and research mind-set for how to approach quantitative data analysis and how to use statistics tests as part of research data analysis • A comprehensive understanding of the data, its connection to a research situation, and the most appropriate statistical tests for the data • Numerous data analysis problems and worked-out examples to illustrate the decision and thought processes that reveal underlying patterns and trends • Detailed examples of the main concepts to aid readers in gaining the needed skills to perform a full analysis of research problems • A conversational tone to effectively introduce readers to the basics of how to perform data analysis as well as make meaningful decisions during data analysis Introduction to Quantitative Data Analysis in the Behavioral and Social Sciences is an ideal textbook for upper-undergraduate and graduate-level research method courses in the behavioral and social sciences, statistics, and engineering. This book is also an appropriate reference for practitioners who require a review of quantitative research methods. Michael J. Albers, Ph.D., is Professor in the Department of English at East Carolina University. His research interests include information design with a focus on answering real-world questions, the presentation of complex information, and human–information interaction. Dr. Albers received his Ph.D. in Technical Communication and Rhetoric from Texas Tech University.
Harness the full power of the behavioral data in your company by learning tools specifically designed for behavioral data analysis. Common data science algorithms and predictive analytics tools treat customer behavioral data, such as clicks on a website or purchases in a supermarket, the same as any other data. Instead, this practical guide introduces powerful methods specifically tailored for behavioral data analysis. Advanced experimental design helps you get the most out of your A/B tests, while causal diagrams allow you to tease out the causes of behaviors even when you can't run experiments. Written in an accessible style for data scientists, business analysts, and behavioral scientists, thispractical book provides complete examples and exercises in R and Python to help you gain more insight from your data--immediately. Understand the specifics of behavioral data Explore the differences between measurement and prediction Learn how to clean and prepare behavioral data Design and analyze experiments to drive optimal business decisions Use behavioral data to understand and measure cause and effect Segment customers in a transparent and insightful way