Handbook of Statistical Modeling for the Social and Behavioral Sciences PDF Download
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Author: G. Arminger
Publisher: Springer Science & Business Media
ISBN: 1489912924
Category : Psychology
Languages : en
Pages : 603
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Book Description
Contributors thoroughly survey the most important statistical models used in empirical reserch in the social and behavioral sciences. Following a common format, each chapter introduces a model, illustrates the types of problems and data for which the model is best used, provides numerous examples that draw upon familiar models or procedures, and includes material on software that can be used to estimate the models studied. This handbook will aid researchers, methodologists, graduate students, and statisticians to understand and resolve common modeling problems.
Author: G. Arminger
Publisher: Springer Science & Business Media
ISBN: 1489912924
Category : Psychology
Languages : en
Pages : 603
Get Book Here
Book Description
Contributors thoroughly survey the most important statistical models used in empirical reserch in the social and behavioral sciences. Following a common format, each chapter introduces a model, illustrates the types of problems and data for which the model is best used, provides numerous examples that draw upon familiar models or procedures, and includes material on software that can be used to estimate the models studied. This handbook will aid researchers, methodologists, graduate students, and statisticians to understand and resolve common modeling problems.
Author: David B. Flora
Publisher: SAGE
ISBN: 1526421925
Category : Social Science
Languages : en
Pages : 786
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Book Description
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: G. Arminger
Publisher: Springer Science & Business Media
ISBN: 9780306448058
Category : Mathematics
Languages : en
Pages : 592
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Book Description
Contributors thoroughly survey the most important statistical models used in empirical reserch in the social and behavioral sciences. Following a common format, each chapter introduces a model, illustrates the types of problems and data for which the model is best used, provides numerous examples that draw upon familiar models or procedures, and includes material on software that can be used to estimate the models studied. This handbook will aid researchers, methodologists, graduate students, and statisticians to understand and resolve common modeling problems.
Author: Barry H. Cohen
Publisher: John Wiley & Sons
ISBN: 0471480762
Category : Psychology
Languages : en
Pages : 291
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Book Description
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: Xiaofeng Steven Liu
Publisher: Routledge
ISBN: 1136464182
Category : Psychology
Languages : en
Pages : 285
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Book Description
This is the first book to demonstrate the application of power analysis to the newer more advanced statistical techniques that are increasingly used in the social and behavioral sciences. Both basic and advanced designs are covered. Readers are shown how to apply power analysis to techniques such as hierarchical linear modeling, meta-analysis, and structural equation modeling. Each chapter opens with a review of the statistical procedure and then proceeds to derive the power functions. This is followed by examples that demonstrate how to produce power tables and charts. The book clearly shows how to calculate power by providing open code for every design and procedure in R, SAS, and SPSS. Readers can verify the power computation using the computer programs on the book's website. There is a growing requirement to include power analysis to justify sample sizes in grant proposals. Most chapters are self-standing and can be read in any order without much disruption.This book will help readers do just that. Sample computer code in R, SPSS, and SAS at www.routledge.com/9781848729810 are written to tabulate power values and produce power curves that can be included in a grant proposal. Organized according to various techniques, chapters 1 – 3 introduce the basics of statistical power and sample size issues including the historical origin, hypothesis testing, and the use of statistical power in t tests and confidence intervals. Chapters 4 - 6 cover common statistical procedures -- analysis of variance, linear regression (both simple regression and multiple regression), correlation, analysis of covariance, and multivariate analysis. Chapters 7 - 11 review the new statistical procedures -- multi-level models, meta-analysis, structural equation models, and longitudinal studies. The appendixes contain a tutorial about R and show the statistical theory of power analysis. Intended as a supplement for graduate courses on quantitative methods, multivariate statistics, hierarchical linear modeling (HLM) and/or multilevel modeling and SEM taught in psychology, education, human development, nursing, and social and life sciences, this is the first text on statistical power for advanced procedures. Researchers and practitioners in these fields also appreciate the book‘s unique coverage of the use of statistical power analysis to determine sample size in planning a study. A prerequisite of basic through multivariate statistics is assumed.
Author: Brian S. Everitt
Publisher: CRC Press
ISBN: 1439807701
Category : Business & Economics
Languages : en
Pages : 324
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Book Description
Multivariable Modeling and Multivariate Analysis for the Behavioral Sciences shows students how to apply statistical methods to behavioral science data in a sensible manner. Assuming some familiarity with introductory statistics, the book analyzes a host of real-world data to provide useful answers to real-life issues.The author begins by exploring
Author: Taylor & Francis Group
Publisher: Routledge
ISBN: 9781032111438
Category :
Languages : en
Pages : 848
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Book Description
The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. The first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. The second volume focuses on foundations and advances in data science, statistical modeling, and machine learning. It covers a range of key issues, including the management of big data in terms of record linkage, streaming, and missing data. Machine learning, agent-based and statistical modeling, as well as data quality in relation to digital-trace and textual data, as well as probability-, non-probability-, and crowdsourced samples represent further foci. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors.
Author: Renee R. Ha
Publisher: SAGE
ISBN: 1452237298
Category : Mathematics
Languages : en
Pages : 433
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Book Description
Designed to engage students and lower their "fear factor", Integrative Statistics for the Social and Behavioral Sciences is a concise, user-friendly text that prepares students to use statistics in the real world. Providing depth and breadth of statistical tests, the text focuses on choosing the appropriate statistical analysis, and shows how to interpret the output and present the results.
Author: Valen E. Johnson
Publisher: Springer Science & Business Media
ISBN: 0387227024
Category : Social Science
Languages : en
Pages : 258
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Book Description
Ordinal Data Modeling is a comprehensive treatment of ordinal data models from both likelihood and Bayesian perspectives. A unique feature of this text is its emphasis on applications. All models developed in the book are motivated by real datasets, and considerable attention is devoted to the description of diagnostic plots and residual analyses. Software and datasets used for all analyses described in the text are available on websites listed in the preface.
Author: Wicher Bergsma
Publisher: Springer Science & Business Media
ISBN: 0387096108
Category : Science
Languages : en
Pages : 274
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Book Description
Marginal Models for Dependent, Clustered, and Longitudinal Categorical Data provides a comprehensive overview of the basic principles of marginal modeling and offers a wide range of possible applications. Marginal models are often the best choice for answering important research questions when dependent observations are involved, as the many real world examples in this book show. In the social, behavioral, educational, economic, and biomedical sciences, data are often collected in ways that introduce dependencies in the observations to be compared. For example, the same respondents are interviewed at several occasions, several members of networks or groups are interviewed within the same survey, or, within families, both children and parents are investigated. Statistical methods that take the dependencies in the data into account must then be used, e.g., when observations at time one and time two are compared in longitudinal studies. At present, researchers almost automatically turn to multi-level models or to GEE estimation to deal with these dependencies. Despite the enormous potential and applicability of these recent developments, they require restrictive assumptions on the nature of the dependencies in the data. The marginal models of this book provide another way of dealing with these dependencies, without the need for such assumptions, and can be used to answer research questions directly at the intended marginal level. The maximum likelihood method, with its attractive statistical properties, is used for fitting the models. This book has mainly been written with applied researchers in mind. It includes many real world examples, explains the types of research questions for which marginal modeling is useful, and provides a detailed description of how to apply marginal models for a great diversity of research questions. All these examples are presented on the book's website (www.cmm.st), along with user friendly programs.
