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Search Results for: statistical-methods-for-the-social-sciences

## Statistical Methods for the Social Sciences

This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. For courses in Statistical Methods for the Social Sciences . Statistical methods applied to social sciences, made accessible to all through an emphasis on concepts Statistical Methods for the Social Sciences introduces statistical methods to students majoring in social science disciplines. With an emphasis on concepts and applications, this book assumes you have no previous knowledge of statistics and only a minimal mathematical background. It contains sufficient material for a two-semester course. The 5th Edition gives you examples and exercises with a variety of “real data.” It includes more illustrations of statistical software for computations and takes advantage of the outstanding applets to explain key concepts, such as sampling distributions and conducting basic data analyses. It continues to downplay mathematics–often a stumbling block for students–while avoiding reliance on an overly simplistic recipe-based approach to statistics.
## Statistics for the Social Sciences

Popular in previous editions, this Third Edition continues to help build students' confidence and ability in doing statistical analysis by slowly moving from concepts that require little computational work to those that require more. Author R. Mark Sirkin once again demonstrates how statistics can be used so that students come to appreciate their usefulness rather than fear them. Statistics for the Social Sciences emphasizes the analysis and interpretation of data to give students a feel for how data interpretation is related to the methods by which the information was obtained.
## Statistical Methods for the Social and Behavioural Sciences

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.
## Statistical Methods for Social Scientists

The aspects of this text which we believe are novel, at least in degree, include: an effort to motivate different sections with practical examples and an empirical orientation; an effort to intersperse several easily motivated examples throughout the book and to maintain some continuity in these examples; and the extensive use of Monte Carlo simulations to demonstrate particular aspects of the problems and estimators being considered. In terms of material being presented, the unique aspects include the first chapter which attempts to address the use of empirical methods in the social sciences, the seventh chapter which considers models with discrete dependent variables and unobserved variables. Clearly these last two topics in particular are quite advanced--more advanced than material that is currently available on the subject. These last two topics are also currently experiencing rapid development and are not adequately described in most other texts.
## Applied Statistical Methods

[NOTE: Is this the current title?] This book describes and explains the entire process of designing and building a distributed object application with the VisualAge Smalltalk Distributed feature.This book contains an overview of the features and architecture of SmallTalk's Distributed feature; sample application components with supporting documentation to illustrate design and coding; and recommendations for building distributed object applications with VisualAge. Learn how to set up the development environment, and special considerations for testing, run-time configurations, optimization and performance tuning.For software development managers, designers and others planning to develop client/server and peer-to-peer applications with distributed objects using VisualAge.
## Student Solutions Manual for Statistical Methods for the Social Sciences

## Statistics for the Social Sciences

This introductory undergraduate textbook is the first statistics textbook built around the General Linear Model.
## A Mathematical Primer for Social Statistics

Beyond the introductory level, learning and effectively using statistical methods in the social sciences requires some knowledge of mathematics. This handy volume introduces the areas of mathematics that are most important to applied social statistics.
## Applied Statistics Using Stata

Clear, intuitive and written with the social science student in mind, this book represents the ideal combination of statistical theory and practice. It focuses on questions that can be answered using statistics and addresses common themes and problems in a straightforward, easy-to-follow manner. The book carefully combines the conceptual aspects of statistics with detailed technical advice providing both the ‘why’ of statistics and the ‘how’. Built upon a variety of engaging examples from across the social sciences it provides a rich collection of statistical methods and models. Students are encouraged to see the impact of theory whilst simultaneously learning how to manipulate software to meet their needs. The book also provides: Original case studies and data sets Practical guidance on how to run and test models in Stata Downloadable Stata programmes created to work alongside chapters A wide range of detailed applications using Stata Step-by-step notes on writing the relevant code. This excellent text will give anyone doing statistical research in the social sciences the theoretical, technical and applied knowledge needed to succeed.
## Advanced and Multivariate Statistical Methods for Social Science Research

Unlike other advanced statistical texts, this book combines the theory and practice behind a number of statistical techniques which students of the social sciences need to evaluate, analyze, and test their research hypotheses.Each chapter discusses the purpose, rationale, and assumptions for using each statistical test, rather than focusing on the memorization of formulas. The tests are further elucidated throughout the text by real examples of analysis. Of particular value to students is the book's detailed discussionof how to utilize SPSS to run each test, read its output, interpret, and write the results.Advanced and Multivariate Statistical Methods for Social Science Research is an indispensable resource for students of disciplines as varied as social work, nursing, public health, psychology, and education.Electronic database files are available for student and instructor use.http://lyceumbooks.com/StudentResources.htm
## Monte Carlo Simulation and Resampling Methods for Social Science

Taking the topics of a quantitative methodology course and illustrating them through Monte Carlo simulation, Monte Carlo Simulation and Resampling Methods for Social Science, by Thomas M. Carsey and Jeffrey J. Harden, examines abstract principles, such as bias, efficiency, and measures of uncertainty in an intuitive, visual way. Instead of thinking in the abstract about what would happen to a particular estimator "in repeated samples," the book uses simulation to actually create those repeated samples and summarize the results. The book includes basic examples appropriate for readers learning the material for the first time, as well as more advanced examples that a researcher might use to evaluate an estimator he or she was using in an actual research project. The book also covers a wide range of topics related to Monte Carlo simulation, such as resampling methods, simulations of substantive theory, simulation of quantities of interest (QI) from model results, and cross-validation. Complete R code from all examples is provided so readers can replicate every analysis presented using R.
## Bayesian Statistics for the Social Sciences

Bridging the gap between traditional classical statistics and a Bayesian approach, David Kaplan provides readers with the concepts and practical skills they need to apply Bayesian methodologies to their data analysis problems. Part I addresses the elements of Bayesian inference, including exchangeability, likelihood, prior/posterior distributions, and the Bayesian central limit theorem. Part II covers Bayesian hypothesis testing, model building, and linear regression analysis, carefully explaining the differences between the Bayesian and frequentist approaches. Part III extends Bayesian statistics to multilevel modeling and modeling for continuous and categorical latent variables. Kaplan closes with a discussion of philosophical issues and argues for an "evidence-based" framework for the practice of Bayesian statistics. User-Friendly Features *Includes worked-through, substantive examples, using large-scale educational and social science databases, such as PISA (Program for International Student Assessment) and the LSAY (Longitudinal Study of American Youth). *Utilizes open-source R software programs available on CRAN (such as MCMCpack and rjags); readers do not have to master the R language and can easily adapt the example programs to fit individual needs. *Shows readers how to carefully warrant priors on the basis of empirical data. *Companion website features data and code for the book's examples, plus other resources.
## Statistical Methods for the Social Sciences, Global Edition

For courses in Statistical Methods for the Social Sciences. Statistical methods applied to social sciences, made accessible to all through an emphasis on concepts Statistical Methods for the Social Sciences introduces statistical methods to students majoring in social science disciplines. With an emphasis on concepts and applications, this book assumes no previous knowledge of statistics and only a minimal mathematical background. It contains sufficient material for a two-semester course. The 5th Edition uses examples and exercises with a variety of "real data." It includes more illustrations of statistical software for computations and takes advantage of the outstanding applets to explain key concepts, such as sampling distributions and conducting basic data analyses. It continues to downplay mathematics--often a stumbling block for students--while avoiding reliance on an overly simplistic recipe-based approach to statistics.
## Using Statistical Methods in Social Science Research

In Using Statistical Methods, Soleman Abu-Bader detects and addresses the gaps between the research and data analysis of the classroom environment and the practitioner's office. This book not only guides social scientists through different tests, but also provides students and researchers alike with information that will help them in their own practice. With focus on the purpose, rationale, and assumptions made by each statistical test, and a plethora of research examples that clearly display their applicability and function in real-world practice, Professor Abu-Bader creates a step-by-step description of the process needed to clearly organize, choose a test or statistical technique, analyze, interpret, and report research findings.
## Making Sense of Statistical Methods in Social Research

Making Sense of Statistical Methods in Social Research is a critical introduction to the use of statistical methods in social research. It provides a unique approach to statistics that concentrates on helping social researchers think about the conceptual basis for the statistical methods they're using. Whereas other statistical methods books instruct students in how to get through the statistics-based elements of their chosen course with as little mathematical knowledge as possible, this book aims to improve students' statistical literacy, with the ultimate goal of turning them into competent researchers. Making Sense of Statistical Methods in Social Research contains careful discussion of the conceptual foundation of statistical methods, specifying what questions they can, or cannot, answer. The logic of each statistical method or procedure is explained, drawing on the historical development of the method, existing publications that apply the method, and methodological discussions. Statistical techniques and procedures are presented not for the purpose of showing how to produce statistics with certain software packages, but as a way of illuminating the underlying logic behind the symbols. The limited statistical knowledge that students gain from straight forward 'how-to' books makes it very hard for students to move beyond introductory statistics courses to postgraduate study and research. This book should help to bridge this gap.
## Causality in Crisis?

## Statistical Modeling and Inference for Social Science

Written specifically for graduate students and practitioners beginning social science research, Statistical Modeling and Inference for Social Science covers the essential statistical tools, models and theories that make up the social scientist's toolkit. Assuming no prior knowledge of statistics, this textbook introduces students to probability theory, statistical inference and statistical modeling, and emphasizes the connection between statistical procedures and social science theory. Sean Gailmard develops core statistical theory as a set of tools to model and assess relationships between variables - the primary aim of social scientists - and demonstrates the ways in which social scientists express and test substantive theoretical arguments in various models. Chapter exercises guide students in applying concepts to data, extending their grasp of core theoretical concepts. Students gain the ability to create, read and critique statistical applications in their fields of interest.
## Modern Statistics for the Social and Behavioral Sciences

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.
## Social Statistics for a Diverse Society

This Eighth Edition of Social Statistics for a Diverse Society continues to emphasize intuition and common sense, while demonstrating that social science is a constant interplay between methods of inquiry and important social issues. Recognizing that today’s students live in a world of growing diversity and richness of social differences, authors Chava Frankfort-Nachmias and Anna Leon-Guerrero use research examples that show how statistics is a tool for understanding the ways in which race, class, gender, and other categories of experience shape our social world and influence social behavior. In addition, guides for reading and interpreting the research literature help students acquire statistical literacy, while SPSS demonstrations and a rich variety of exercises help them hone their problem-solving skills.
## Statistical Methods for Meta-Analysis

The main purpose of this book is to address the statistical issues for integrating independent studies. There exist a number of papers and books that discuss the mechanics of collecting, coding, and preparing data for a meta-analysis , and we do not deal with these. Because this book concerns methodology, the content necessarily is statistical, and at times mathematical. In order to make the material accessible to a wider audience, we have not provided proofs in the text. Where proofs are given, they are placed as commentary at the end of a chapter. These can be omitted at the discretion of the reader. Throughout the book we describe computational procedures whenever required. Many computations can be completed on a hand calculator, whereas some require the use of a standard statistical package such as SAS, SPSS, or BMD. Readers with experience using a statistical package or who conduct analyses such as multiple regression or analysis of variance should be able to carry out the analyses described with the aid of a statistical package.

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Author: Alan Agresti

Publisher: Pearson

ISBN: 0134512820

Category: Mathematics

Page: 740

View: 6107

Author: R. Mark Sirkin

Publisher: SAGE

ISBN: 9781412905466

Category: Mathematics

Page: 610

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*A Model-Based Approach*

Author: David B. Flora

Publisher: SAGE

ISBN: 1526421925

Category: Social Science

Page: 472

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Author: Eric A Hanushek,John E. Jackson

Publisher: Academic Press

ISBN: 0080918573

Category: Mathematics

Page: 374

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*For Business, Economics, and the Social Sciences*

Author: William Lee Carlson

Publisher: N.A

ISBN: 9780135708477

Category: Mathematics

Page: 1021

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Publisher: Pearson

ISBN: 9780136028130

Category: Business & Economics

Page: 76

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Author: Russell T. Warne

Publisher: Cambridge University Press

ISBN: 1107576970

Category: Mathematics

Page: 601

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Author: John Fox

Publisher: SAGE

ISBN: 1412960800

Category: Mathematics

Page: 170

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*A Guide for the Social Sciences*

Author: Mehmet Mehmetoglu,Tor Georg Jakobsen

Publisher: SAGE

ISBN: 1473987148

Category: Social Science

Page: 376

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Author: Soleman H. Abu-Bader

Publisher: N.A

ISBN: 9780190616397

Category:

Page: 350

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Author: Thomas M. Carsey,Jeffrey J. Harden

Publisher: SAGE Publications

ISBN: 1483324923

Category: Social Science

Page: 304

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Author: David Kaplan

Publisher: Guilford Publications

ISBN: 1462516513

Category: Psychology

Page: 318

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Publisher: N.A

ISBN: 9781292220314

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*With a Complete SPSS Guide*

Author: Soleman H. Abu-Bader

Publisher: Oxford University Press

ISBN: 0190685352

Category: Social Science

Page: N.A

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Author: Keming Yang

Publisher: SAGE

ISBN: 1446243869

Category: Social Science

Page: 216

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*Statistical Methods and the Search for Causal Knowledge in the Social Sciences*

Author: Vaughn R. McKim,Stephen P. Turner

Publisher: N.A

ISBN: N.A

Category: Social Science

Page: 410

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Author: Sean Gailmard

Publisher: Cambridge University Press

ISBN: 1107003148

Category: Business & Economics

Page: 388

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*A Practical Introduction, Second Edition*

Author: Rand Wilcox

Publisher: CRC Press

ISBN: 149879680X

Category: Mathematics

Page: 706

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Author: Chava Frankfort-Nachmias,Anna Leon-Guerrero

Publisher: SAGE Publications

ISBN: 1506347223

Category: Social Science

Page: 544

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Author: Larry V. Hedges,Ingram Olkin

Publisher: Academic Press

ISBN: 0080570658

Category: Mathematics

Page: 369

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