[PDF] Statistical Analysis Of Behavioural Data eBook

Author : Yuelin Li
Publisher : Springer Science & Business Media
Page : 247 pages
File Size : 16,80 MB
Release : 2011-12-02
Category : Social Science
ISBN : 1461412382

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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.

Author : Patsy Haccou
Publisher : Oxford University Press, USA
Page : 396 pages
File Size : 23,65 MB
Release : 1994
Category : Mathematics
ISBN : 9780198548508

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This is a how-and-why-to-do-it book for students and scientists in all the behavioral sciences. It presents sophisticated statistical methods for analyzing continuous-time records of behavior, and integrates many recent developments in ethology, mathematical modeling, statistics, and technology. These new methods are explicitly designed to handle sequential or simultaneous acts where neither the duration nor the sequence of the acts is predetermined, which is often the case if the time scale on which behavior is studied is relatively short. The authors show how to analyze behavioral data starting with a basic model, the continuous time Markov chain. They then indicate how and when this model can be generalized and demonstrate the suitability of their approach for detecting, for example, the effects of different experimental treatments or of gradual changes in the social or physical environment. Competitive interactions such as predator-prey or host-parasite are also good subjects for this type of analysis. There are eight chapters and many worked examples, leading the reader through the mathematical processes and their applications. Students and researchers in all fields of behavioral science will find this book incomparably useful for planning and performing data analysis.

Author : Dana Dunn
Publisher : McGraw-Hill Humanities, Social Sciences & World Languages
Page : 0 pages
File Size : 46,12 MB
Release : 2001
Category : Psychology
ISBN : 9780072347647

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Dana Dunn combines the quantitative aspects of statistics with written explanations of what the results of statistical tests mean in a way that students will understand. He incorporates APA style in examples and an appendix to expose students to the expected style of prose. For students with math anxiety or who just need a refresher on basic mathematical functions, he has included an appendix so that faculty are not forced to spend class time reviewing these basic concepts. The book includes a student friendly system of pedagogy to ensure student success. Where possible, Dunn has included examples and projects for students to conduct research on their own lives to draw personalized meaning from the world of statistics.

Author : Jacob Cohen
Publisher : Routledge
Page : 625 pages
File Size : 21,49 MB
Release : 2013-05-13
Category : Psychology
ISBN : 1134742770

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Statistical Power Analysis is a nontechnical guide to power analysis in research planning that provides users of applied statistics with the tools they need for more effective analysis. The Second Edition includes: * a chapter covering power analysis in set correlation and multivariate methods; * a chapter considering effect size, psychometric reliability, and the efficacy of "qualifying" dependent variables and; * expanded power and sample size tables for multiple regression/correlation.

Author : Jonathon D. Brown
Publisher : Springer
Page : 526 pages
File Size : 38,6 MB
Release : 2019-04-30
Category : Social Science
ISBN : 3319935496

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This book demonstrates the importance of computer-generated statistical analyses in behavioral science research, particularly those using the R software environment. Statistical methods are being increasingly developed and refined by computer scientists, with expertise in writing efficient and elegant computer code. Unfortunately, many researchers lack this programming background, leaving them to accept on faith the black-box output that emerges from the sophisticated statistical models they frequently use. Building on the author’s previous volume, Linear Models in Matrix Form, this text bridges the gap between computer science and research application, providing easy-to-follow computer code for many statistical analyses using the R software environment. The text opens with a foundational section on linear algebra, then covers a variety of advanced topics, including robust regression, model selection based on bias and efficiency, nonlinear models and optimization routines, generalized linear models, and survival and time-series analysis. Each section concludes with a presentation of the computer code used to illuminate the analysis, as well as pointers to packages in R that can be used for similar analyses and nonstandard cases. The accessible code and breadth of topics make this book an ideal tool for graduate students or researchers in the behavioral sciences who are interested in performing advanced statistical analyses without having a sophisticated background in computer science and mathematics.

Author : Florent Buisson
Publisher : "O'Reilly Media, Inc."
Page : 361 pages
File Size : 25,53 MB
Release : 2021-06-15
Category : Business & Economics
ISBN : 1492061344

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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

Author : Kimmo Vehkalahti
Publisher : CRC Press
Page : 439 pages
File Size : 20,31 MB
Release : 2018-12-19
Category : Mathematics
ISBN : 1351202251

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Multivariate Analysis for the Behavioral Sciences, Second Edition is designed to show how a variety of statistical methods can be used to analyse data collected by psychologists and other behavioral scientists. Assuming some familiarity with introductory statistics, the book begins by briefly describing a variety of study designs used in the behavioral sciences, and the concept of models for data analysis. The contentious issues of p-values and confidence intervals are also discussed in the introductory chapter. After describing graphical methods, the book covers regression methods, including simple linear regression, multiple regression, locally weighted regression, generalized linear models, logistic regression, and survival analysis. There are further chapters covering longitudinal data and missing values, before the last seven chapters deal with multivariate analysis, including principal components analysis, factor analysis, multidimensional scaling, correspondence analysis, and cluster analysis. Features: Presents an accessible introduction to multivariate analysis for behavioral scientists Contains a large number of real data sets, including cognitive behavioral therapy, crime rates, and drug usage Includes nearly 100 exercises for course use or self-study Supplemented by a GitHub repository with all datasets and R code for the examples and exercises Theoretical details are separated from the main body of the text Suitable for anyone working in the behavioral sciences with a basic grasp of statistics

Author : David B. Flora
Publisher : SAGE
Page : 786 pages
File Size : 17,9 MB
Release : 2017-12-11
Category : Social Science
ISBN : 1526421925

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Statistical methods in modern research increasingly entail developing, estimating and testing models for data. Rather than rigid methods of data analysis, the need today is for more flexible methods for modelling data. In this logical, easy-to-follow and exceptionally clear book, David Flora provides a comprehensive survey of the major statistical procedures currently used. His innovative model-based approach teaches you how to: Understand and choose the right statistical model to fit your data Match substantive theory and statistical models Apply statistical procedures hands-on, with example data analyses Develop and use graphs to understand data and fit models to data Work with statistical modeling principles using any software package Learn by applying, with input and output files for R, SAS, SPSS, and Mplus. Statistical Methods for the Social and Behavioural Sciences: A Model Based Approach is the essential guide for those looking to extend their understanding of the principles of statistics, and begin using the right statistical modeling method for their own data. It is particularly suited to second or advanced courses in statistical methods across the social and behavioural sciences.

Author : Thomas Moeslund
Publisher : MDPI
Page : 300 pages
File Size : 45,33 MB
Release : 2020-06-17
Category : Technology & Engineering
ISBN : 3039362283

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This Special Issue focused on novel vision-based approaches, mainly related to computer vision and machine learning, for the automatic analysis of human behaviour. We solicited submissions on the following topics: information theory-based pattern classification, biometric recognition, multimodal human analysis, low resolution human activity analysis, face analysis, abnormal behaviour analysis, unsupervised human analysis scenarios, 3D/4D human pose and shape estimation, human analysis in virtual/augmented reality, affective computing, social signal processing, personality computing, activity recognition, human tracking in the wild, and application of information-theoretic concepts for human behaviour analysis. In the end, 15 papers were accepted for this special issue. These papers, that are reviewed in this editorial, analyse human behaviour from the aforementioned perspectives, defining in most of the cases the state of the art in their corresponding field.