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| Author | : Brian Joseph Gillespie |
| Publisher | : SAGE Publications |
| Total Pages | : 305 |
| Release | : 2020-02-07 |
| Genre | : Business & Economics |
| ISBN | : 1544344031 |
Geared toward social and behavioural statistics students, especially those with no background in computer science, this handy guide contains basic information on statistics in the R language.
| Author | : Barry H. Cohen |
| Publisher | : John Wiley & Sons |
| Total Pages | : 291 |
| Release | : 2004-04-01 |
| Genre | : Psychology |
| ISBN | : 0471480762 |
Master the essential statistical skills used in social andbehavioral sciences Essentials of Statistics for the Social and Behavioral Sciencesdistills the overwhelming amount of material covered inintroductory statistics courses into a handy, practical resourcefor students and professionals. This accessible guide covers basicto advanced concepts in a clear, concrete, and readablestyle. Essentials of Statistics for the Social and Behavioral Sciencesguides you to a better understanding of basic concepts ofstatistical methods. Numerous practical tips are presented forselecting appropriate statistical procedures. In addition, thisuseful guide demonstrates how to evaluate and interpret statisticaldata, provides numerous formulas for calculating statistics fromtables of summary statistics, and offers a variety of workedexamples. As part of the Essentials of Behavioral Science series, this bookoffers a thorough review of the most relevant statistical conceptsand techniques that will arm you with the tools you'll need forknowledgeable, informed practice. Each concise chapter featuresnumerous callout boxes highlighting key concepts, bulleted points,and extensive illustrative material, as well as "Test Yourself"questions that help you gauge and reinforce your grasp of theinformation covered.
| Author | : Rand Wilcox |
| Publisher | : CRC Press |
| Total Pages | : 862 |
| Release | : 2011-08-05 |
| Genre | : Mathematics |
| ISBN | : 1439834563 |
In addition to learning how to apply classic statistical methods, students need to understand when these methods perform well, and when and why they can be highly unsatisfactory. Modern Statistics for the Social and Behavioral Sciences illustrates how to use R to apply both standard and modern methods to correct known problems with classic techniques. Numerous illustrations provide a conceptual basis for understanding why practical problems with classic methods were missed for so many years, and why modern techniques have practical value. Designed for a two-semester, introductory course for graduate students in the social sciences, this text introduces three major advances in the field: Early studies seemed to suggest that normality can be assumed with relatively small sample sizes due to the central limit theorem. However, crucial issues were missed. Vastly improved methods are now available for dealing with non-normality. The impact of outliers and heavy-tailed distributions on power and our ability to obtain an accurate assessment of how groups differ and variables are related is a practical concern when using standard techniques, regardless of how large the sample size might be. Methods for dealing with this insight are described. The deleterious effects of heteroscedasticity on conventional ANOVA and regression methods are much more serious than once thought. Effective techniques for dealing heteroscedasticity are described and illustrated. Requiring no prior training in statistics, Modern Statistics for the Social and Behavioral Sciences provides a graduate-level introduction to basic, routinely used statistical techniques relevant to the social and behavioral sciences. It describes and illustrates methods developed during the last half century that deal with known problems associated with classic techniques. Espousing the view that no single method is always best, it imparts a general understanding of the relative merits of various techniques so that the choice of method can be made in an informed manner.
| Author | : Ryan Kennedy |
| Publisher | : CRC Press |
| Total Pages | : 198 |
| Release | : 2021-02-23 |
| Genre | : Mathematics |
| ISBN | : 1000353850 |
Introduction to R for Social Scientists: A Tidy Programming Approach introduces the Tidy approach to programming in R for social science research to help quantitative researchers develop a modern technical toolbox. The Tidy approach is built around consistent syntax, common grammar, and stacked code, which contribute to clear, efficient programming. The authors include hundreds of lines of code to demonstrate a suite of techniques for developing and debugging an efficient social science research workflow. To deepen the dedication to teaching Tidy best practices for conducting social science research in R, the authors include numerous examples using real world data including the American National Election Study and the World Indicators Data. While no prior experience in R is assumed, readers are expected to be acquainted with common social science research designs and terminology. Whether used as a reference manual or read from cover to cover, readers will be equipped with a deeper understanding of R and the Tidyverse, as well as a framework for how best to leverage these powerful tools to write tidy, efficient code for solving problems. To this end, the authors provide many suggestions for additional readings and tools to build on the concepts covered. They use all covered techniques in their own work as scholars and practitioners.
| Author | : David B. Flora |
| Publisher | : SAGE |
| Total Pages | : 769 |
| Release | : 2017-12-11 |
| Genre | : Social Science |
| ISBN | : 1526421925 |
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 | : Adela-Maria Isvoranu |
| Publisher | : Taylor & Francis |
| Total Pages | : 261 |
| Release | : 2022-04-28 |
| Genre | : Psychology |
| ISBN | : 100054107X |
A systematic, innovative introduction to the field of network analysis, Network Psychometrics with R: A Guide for Behavioral and Social Scientists provides a comprehensive overview of and guide to both the theoretical foundations of network psychometrics as well as modelling techniques developed from this perspective. Written by pioneers in the field, this textbook showcases cutting-edge methods in an easily accessible format, accompanied by problem sets and code. After working through this book, readers will be able to understand the theoretical foundations behind network modelling, infer network topology, and estimate network parameters from different sources of data. This book features an introduction on the statistical programming language R that guides readers on how to analyse network structures and their stability using R. While Network Psychometrics with R is written in the context of social and behavioral science, the methods introduced in this book are widely applicable to data sets from related fields of study. Additionally, while the text is written in a non-technical manner, technical content is highlighted in textboxes for the interested reader. Network Psychometrics with R is ideal for instructors and students of undergraduate and graduate level courses and workshops in the field of network psychometrics as well as established researchers looking to master new methods. This book is accompanied by a companion website with resources for both students and lecturers.
| Author | : Jacob Cohen |
| Publisher | : Routledge |
| Total Pages | : 625 |
| Release | : 2013-05-13 |
| Genre | : Psychology |
| ISBN | : 1134742770 |
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 | : William E. Wagner, III |
| Publisher | : SAGE Publications |
| Total Pages | : 142 |
| Release | : 2018-02-28 |
| Genre | : Social Science |
| ISBN | : 1544321090 |
Using and Interpreting Statistics in the Social, Behavioral, and Health Sciences is designed to be paired with any undergraduate introduction to research methods text used by students in a variety of disciplines. It introduces students to statistics at the conceptual level—examining the meaning of statistics, and why researchers use a particular statistical technique, rather than computational skills. Focusing on descriptive statistics, and some more advanced topics such as tests of significance, measures of association, and regression analysis, this brief, inexpensive text is the perfect companion to help students who have not yet taken an introductory statistics course or are confused by the statistics used in the articles they are reading.
| Author | : Rand Wilcox |
| Publisher | : CRC Press |
| Total Pages | : 1084 |
| Release | : 2017-08-15 |
| Genre | : Mathematics |
| ISBN | : 149879680X |
Requiring no prior training, Modern Statistics for the Social and Behavioral Sciences provides a two-semester, graduate-level introduction to basic statistical techniques that takes into account recent advances and insights that are typically ignored in an introductory course. Hundreds of journal articles make it clear that basic techniques, routinely taught and used, can perform poorly when dealing with skewed distributions, outliers, heteroscedasticity (unequal variances) and curvature. Methods for dealing with these concerns have been derived and can provide a deeper, more accurate and more nuanced understanding of data. A conceptual basis is provided for understanding when and why standard methods can have poor power and yield misleading measures of effect size. Modern techniques for dealing with known concerns are described and illustrated. Features: Presents an in-depth description of both classic and modern methods Explains and illustrates why recent advances can provide more power and a deeper understanding of data Provides numerous illustrations using the software R Includes an R package with over 1300 functions Includes a solution manual giving detailed answers to all of the exercises This second edition describes many recent advances relevant to basic techniques. For example, a vast array of new and improved methods is now available for dealing with regression, including substantially improved ANCOVA techniques. The coverage of multiple comparison procedures has been expanded and new ANOVA techniques are described. Rand Wilcox is a professor of psychology at the University of Southern California. He is the author of 13 other statistics books and the creator of the R package WRS. He currently serves as an associate editor for five statistics journals. He is a fellow of the Association for Psychological Science and an elected member of the International Statistical Institute.
| Author | : Adela-Maria Isvoranu |
| Publisher | : Routledge |
| Total Pages | : 269 |
| Release | : 2022-04-28 |
| Genre | : Psychology |
| ISBN | : 1000541118 |
A systematic, innovative introduction to the field of network analysis, Network Psychometrics with R: A Guide for Behavioral and Social Scientists provides a comprehensive overview of and guide to both the theoretical foundations of network psychometrics as well as modelling techniques developed from this perspective. Written by pioneers in the field, this textbook showcases cutting-edge methods in an easily accessible format, accompanied by problem sets and code. After working through this book, readers will be able to understand the theoretical foundations behind network modelling, infer network topology, and estimate network parameters from different sources of data. This book features an introduction on the statistical programming language R that guides readers on how to analyse network structures and their stability using R. While Network Psychometrics with R is written in the context of social and behavioral science, the methods introduced in this book are widely applicable to data sets from related fields of study. Additionally, while the text is written in a non-technical manner, technical content is highlighted in textboxes for the interested reader. Network Psychometrics with R is ideal for instructors and students of undergraduate and graduate level courses and workshops in the field of network psychometrics as well as established researchers looking to master new methods. This book is accompanied by a companion website with resources for both students and lecturers.
