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Introduction to Data Analysis Using IBM SPSS
This textbook book elaborates on the basic understanding and application of statistical tests and data analysis using hypothetical datasets and SPSS version 22.0. It presents step-by-step processes and to-the-point interpretation for quick assimilation and comprehension.Data Analysis Using SPSS,- Begins from the stages of data entry and goes on till editing and data visualization.- Takes the readers through descriptive statistics, frequency, univariate, bivariate and regression analysis, cross-tabulation, linear models, and non-parametric test procedures.- Presents information through dialog boxes and tabular charts, it also incorporates important formulae, calculations, and illustrations of sample data for an in-depth understanding of concepts and results.- Can be used as a textbook as well as a reference book as it helps to build conceptual understanding, gaining software skills, communicate data and its insights to accomplish research assignments, research/ capstone projects and PhD research work.With comprehensive coverage, this book would be useful to the students, researchers, and teacher of the various social sciences disciplines like Psychology, Sociology, Education, Social Work, Agriculture Management, and other allied subjects. It would also be an invaluable companion to professionals and data scientist working in the field of analytics.
R 9,542.75
Introduction to Data Analysis Using IBM SPSS
This textbook book elaborates on the basic understanding and application of statistical tests and data analysis using hypothetical datasets and SPSS version 22.0. It presents step-by-step processes and to-the-point interpretation for quick assimilation and comprehension.Data Analysis Using SPSS,- Begins from the stages of data entry and goes on till editing and data visualization.- Takes the readers through descriptive statistics, frequency, univariate, bivariate and regression analysis, cross-tabulation, linear models, and non-parametric test procedures.- Presents information through dialog boxes and tabular charts, it also incorporates important formulae, calculations, and illustrations of sample data for an in-depth understanding of concepts and results.- Can be used as a textbook as well as a reference book as it helps to build conceptual understanding, gaining software skills, communicate data and its insights to accomplish research assignments, research/ capstone projects and PhD research work.With comprehensive coverage, this book would be useful to the students, researchers, and teacher of the various social sciences disciplines like Psychology, Sociology, Education, Social Work, Agriculture Management, and other allied subjects. It would also be an invaluable companion to professionals and data scientist working in the field of analytics.
R 2,015.00
Quantitative Data Analysis with IBM SPSS 17, 18 & 19
This latest edition has been fully updated to accommodate the needs of users of SPSS Releases 17, 18 and 19 while still being applicable to users of SPSS Releases 15 and 16.As with previous editions, Alan Bryman and Duncan Cramer continue to offer a comprehensive and user-friendly introduction to the widely used IBM SPSS Statistics. The simple, non-technical approach to quantitative data analysis enables the reader to quickly become familiar with SPSS and with the tests available to them.No previous experience of statistics or computing is required as this book provides a step-by-step guide to statistical techniques, including:Non-parametric testsCorrelationSimple and multiple regressionAnalysis of variance and covarianceFactor analysis.This book comes equipped with a comprehensive range of exercises for further practice, and it covers key issues such as sampling, statistical inference, conceptualization and measurement and selection of appropriate tests. The authors have also included a helpful glossary of key terms.The data sets used in Quantitative Data Analysis with IBM SPSS 17, 18 and 19 are available online at http://www.routledgetextbooks.com/textbooks/author/bryman-9780415579193/; in addition, a set of multiple-choice questions and a chapter-by-chapter PowerPoint lecture course are available free of charge to lecturers who adopt the book.
R 2,711.00
Introduction to Secondary Data Analysis with IBM SPSS Statistics
Many professional, high-quality surveys collect data on people′s behaviour, experiences, lifestyles and attitudes. The data they produce is more accessible than ever before. This book provides students with a comprehensive introduction to using this data, as well as transactional data and big data sources, in their own research projects. Here you will find all you need to know about locating, accessing, preparing and analysing secondary data, along with step-by-step instructions for using IBM SPSS Statistics. You will learn how to:Create a robust research question and design that suits secondary analysis Locate, access and explore data online Understand data documentation Check and ′clean′ secondary data Manage and analyse your data to produce meaningful results Replicate analyses of data in published articles and books Using case studies and video animations to illustrate each step of your research, this book provides you with the quantitative analysis skills you′ll need to pass your course, complete your research project and compete in the job market. Exercises throughout the book and on the book′s companion website give you an opportunity to practice, check your understanding and work hands on with real data as you′re learning.
R 2,906.61
Introduction to Secondary Data Analysis with IBM SPSS Statistics
Many professional, high-quality surveys collect data on people′s behaviour, experiences, lifestyles and attitudes. The data they produce is more accessible than ever before. This book provides students with a comprehensive introduction to using this data, as well as transactional data and big data sources, in their own research projects. Here you will find all you need to know about locating, accessing, preparing and analysing secondary data, along with step-by-step instructions for using IBM SPSS Statistics. You will learn how to:Create a robust research question and design that suits secondary analysis Locate, access and explore data online Understand data documentation Check and ′clean′ secondary data Manage and analyse your data to produce meaningful results Replicate analyses of data in published articles and books Using case studies and video animations to illustrate each step of your research, this book provides you with the quantitative analysis skills you′ll need to pass your course, complete your research project and compete in the job market. Exercises throughout the book and on the book′s companion website give you an opportunity to practice, check your understanding and work hands on with real data as you′re learning.
R 5,810.94
Performing Data Analysis Using IBM SPSS
Features easy-to-follow insight and clear guidelines to perform data analysis using IBM SPSS® Performing Data Analysis Using IBM SPSS® uniquely addresses the presented statistical procedures with an example problem, detailed analysis, and the related data sets. Data entry procedures, variable naming, and step-by-step instructions for all analyses are provided in addition to IBM SPSS point-and-click methods, including details on how to view and manipulate output. Designed as a user’s guide for students and other interested readers to perform statistical data analysis with IBM SPSS, this book addresses the needs, level of sophistication, and interest in introductory statistical methodology on the part of readers in social and behavioral science, business, health-related, and education programs. Each chapter of Performing Data Analysis Using IBM SPSS covers a particular statistical procedure and offers the following: an example problem or analysis goal, together with a data set; IBM SPSS analysis with step-by-step analysis setup and accompanying screen shots; and IBM SPSS output with screen shots and narrative on how to read or interpret the results of the analysis. The book provides in-depth chapter coverage of: IBM SPSS statistical outputDescriptive statistics proceduresScore distribution assumption evaluationsBivariate correlationRegressing (predicting) quantitative and categorical variablesSurvival analysist TestANOVA and ANCOVAMultivariate group differencesMultidimensional scalingCluster analysisNonparametric procedures for frequency data Performing Data Analysis Using IBM SPSS is an excellent text for upper-undergraduate and graduate-level students in courses on social, behavioral, and health sciences as well as secondary education, research design, and statistics. Also an excellent reference, the book is ideal for professionals and researchers in the social, behavioral, and health sciences; applied statisticians; and practitioners working in industry.
R 3,471.00
Quantitative Analysis and IBM® SPSS® Statistics
This guide is for practicing statisticians and data scientists who use IBM SPSS for statistical analysis of big data in business and finance. This is the first of a two-part guide to SPSS for Windows, introducing data entry into SPSS, along with elementary statistical and graphical methods for summarizing and presenting data. Part I also covers the rudiments of hypothesis testing and business forecasting while Part II will present multivariate statistical methods, more advanced forecasting methods, and multivariate methods.IBM SPSS Statistics offers a powerful set of statistical and information analysis systems that run on a wide variety of personal computers. The software is built around routines that have been developed, tested, and widely used for more than 20 years. As such, IBM SPSS Statistics is extensively used in industry, commerce, banking, local and national governments, and education. Just a small subset of users of the package include the major clearing banks, the BBC, British Gas, British Airways, British Telecom, the Consumer Association, Eurotunnel, GSK, TfL, the NHS, Shell, Unilever, and W.H.S.Although the emphasis in this guide is on applications of IBM SPSS Statistics, there is a need for users to be aware of the statistical assumptions and rationales underpinning correct and meaningful application of the techniques available in the package; therefore, such assumptions are discussed, and methods of assessing their validity are described. Also presented is the logic underlying the computation of the more commonly used test statistics in the area of hypothesis testing. Mathematical background is kept to a minimum.
R 2,015.00
Handbook of Univariate and Multivariate Data Analysis with IBM SPSS
Using the same accessible, hands-on approach as its best-selling predecessor, the Handbook of Univariate and Multivariate Data Analysis with IBM SPSS, Second Edition explains how to apply statistical tests to experimental findings, identify the assumptions underlying the tests, and interpret the findings. This second edition now covers more topics and has been updated with the SPSS statistical package for Windows.New to the Second Edition Three new chapters on multiple discriminant analysis, logistic regression, and canonical correlationNew section on how to deal with missing dataCoverage of tests of assumptions, such as linearity, outliers, normality, homogeneity of variance-covariance matrices, and multicollinearityDiscussions of the calculation of Type I error and the procedure for testing statistical significance between two correlation coefficients obtained from two samples Expanded coverage of factor analysis, path analysis (test of the mediation hypothesis), and structural equation modeling Suitable for both newcomers and seasoned researchers in the social sciences, the handbook offers a clear guide to selecting the right statistical test, executing a wide range of univariate and multivariate statistical tests via the Windows and syntax methods, and interpreting the output results. The SPSS syntax files used for executing the statistical tests can be found in the appendix. Data sets employed in the examples are available on the book’s CRC Press web page.
R 2,846.99
Handbook of Univariate and Multivariate Data Analysis with IBM SPSS
Using the same accessible, hands-on approach as its best-selling predecessor, the Handbook of Univariate and Multivariate Data Analysis with IBM SPSS, Second Edition explains how to apply statistical tests to experimental findings, identify the assumptions underlying the tests, and interpret the findings. This second edition now covers more topics and has been updated with the SPSS statistical package for Windows.New to the Second Edition Three new chapters on multiple discriminant analysis, logistic regression, and canonical correlationNew section on how to deal with missing dataCoverage of tests of assumptions, such as linearity, outliers, normality, homogeneity of variance-covariance matrices, and multicollinearityDiscussions of the calculation of Type I error and the procedure for testing statistical significance between two correlation coefficients obtained from two samples Expanded coverage of factor analysis, path analysis (test of the mediation hypothesis), and structural equation modelingSuitable for both newcomers and seasoned researchers in the social sciences, the handbook offers a clear guide to selecting the right statistical test, executing a wide range of univariate and multivariate statistical tests via the Windows and syntax methods, and interpreting the output results. The SPSS syntax files used for executing the statistical tests can be found in the appendix. Data sets employed in the examples are available on the book’s CRC Press web page.
R 5,690.92
Quantitative Analysis and IBM® SPSS® Statistics
This guide is for practicing statisticians and data scientists who use IBM SPSS for statistical analysis of big data in business and finance. This is the first of a two-part guide to SPSS for Windows, introducing data entry into SPSS, along with elementary statistical and graphical methods for summarizing and presenting data. Part I also covers the rudiments of hypothesis testing and business forecasting while Part II will present multivariate statistical methods, more advanced forecasting methods, and multivariate methods.IBM SPSS Statistics offers a powerful set of statistical and information analysis systems that run on a wide variety of personal computers. The software is built around routines that have been developed, tested, and widely used for more than 20 years. As such, IBM SPSS Statistics is extensively used in industry, commerce, banking, local and national governments, and education. Just a small subset of users of the package include the major clearing banks, the BBC, British Gas, British Airways, British Telecom, the Consumer Association, Eurotunnel, GSK, TfL, the NHS, Shell, Unilever, and W.H.S.Although the emphasis in this guide is on applications of IBM SPSS Statistics, there is a need for users to be aware of the statistical assumptions and rationales underpinning correct and meaningful application of the techniques available in the package; therefore, such assumptions are discussed, and methods of assessing their validity are described. Also presented is the logic underlying the computation of the more commonly used test statistics in the area of hypothesis testing. Mathematical background is kept to a minimum.
R 2,015.00
SPSS Survival Manual: A Step by Step Guide to Data Analysis using IBM SPSS
The SPSS Survival Manual throws a lifeline to students and researchers grappling with this powerful data analysis software.In her bestselling guide, Julie Pallant takes you through the entire research process, helping you choose the right data analysis technique for your project. This edition has been updated to include up to SPSS version 26. From the formulation of research questions, to the design of the study and analysis of data, to reporting the results, Julie discusses basic and advanced statistical techniques. She outlines each technique clearly, with step-by-step procedures for performing the analysis, a detailed guide to interpreting data output and an example of how to present the results in a report.For both beginners and experienced users in Psychology, Sociology, Health Sciences, Medicine, Education, Business and related disciplines, the SPSS Survival Manual is an essential text. It is illustrated throughout with screen grabs, examples of output and tips, and is also further supported by a website with sample data and guidelines on report writing.This seventh edition is fully revised and updated to accommodate changes to IBM SPSS procedures.
R 2,011.00
Quantitative Data Analysis using SPSS: An Introduction for Health and Social Sciences
"This is an ideal introductory book for budding researchers who are embarking on the development and then analysis of data, and in this case, more specifically questionnaires using partly or exclusively closed questions amenable to statistical analysis."Primary Health Care Research and Development"The text is a welcome addition for nursing students at both undergraduate and postgraduate level research. Having reviewed the text I can only inform you how a student described a chapter in the book recently when she borrowed it. 'The language is clear and unambiguous'. I will be strongly encouraging students to either purchase the text ... with the purpose of giving them a foundation in statistics."William Evans, Institute of Technology Tralee, IrelandThis accessible book is essential reading for those looking for a short and simple guide to basic data analysis. Written for the complete beginner, the book is the ideal companion when undertaking quantitative data analysis for the first time using SPSS. The book uses a simple example of quantitative data analysis that would be typical to the health field to take you through the process of data analysis step by step. The example used is a doctor who conducts a questionnaire survey of 30 patients to assess a specific service. The data from these questionnaires is given to you for analysis, and the book leads you through the process required to analyse this data. Handy screenshots illustrate each step of the process so you can try out the analysis for yourself, and apply it to your own research with ease.Topics covered include: Questionnaires and how to analyse them Coding the data for SPSS, setting up an SPSS database and entering the data Descriptive statistics and illustrating the data using graphs Cross-tabulation and the Chi-square statistic Correlation: examining relationships between interval data Examining differences between two sets of scores Reporting the results and presenting the dataQuantitative Data Analysis Using SPSS is helpful for any students in health and social sciences with little or no experience of quantitative data analysis and statistics.
R 1,356.00
Research Methods and Data Analysis for Business Decisions
This introductory textbook presents research methods and data analysis tools in non-technical language. It explains the research process and the basics of qualitative and quantitative data analysis, including procedures and methods, analysis, interpretation, and applications using hands-on data examples in QDA Miner Lite and IBM SPSS Statistics software. The book is divided into four parts that address study and research design; data collection, qualitative methods and surveys; statistical methods, including hypothesis testing, regression, cluster and factor analysis; and reporting. The intended audience is business and social science students learning scientific research methods, however, given its business context, the book will be equally useful for decision-makers in businesses and organizations.
R 3,110.60
Research Methods and Data Analysis for Business Decisions
This introductory textbook presents research methods and data analysis tools in non-technical language. It explains the research process and the basics of qualitative and quantitative data analysis, including procedures and methods, analysis, interpretation, and applications using hands-on data examples in QDA Miner Lite and IBM SPSS Statistics software. The book is divided into four parts that address study and research design; data collection, qualitative methods and surveys; statistical methods, including hypothesis testing, regression, cluster and factor analysis; and reporting. The intended audience is business and social science students learning scientific research methods, however, given its business context, the book will be equally useful for decision-makers in businesses and organizations.
R 3,608.93
Introduction to Statistics with SPSS
Introduction to Statistics with SPSS offers an introduction to statistics that can be used before, during or after a course on statistics.Covering a wide range of terms and techniques, including simple and multiple regressions, this book guides the student to enter data from a simple research project into a computer, provide an adequate analysis of the data and present a report on the findings.
R 11,148.81
Data Analysis
This is an introductory textbook in data analysis and statistics, designed for students in the first year of a social sciences degree.
R 3,810.00
Step-By-Step Introduction to Statistics for Business
A clear and concise introduction to statistics for business and management students, demonstrating how important statistics are in the business decision-making process and covering everything from conducting a survey and collecting data, to summarizing statistical data, and presenting findings. Each chapter features a real-world business situation and accompanying dataset, the reader is then encouraged to identify the correct statistical concept in the chapter and solve the problem outlined. Offering students a chance to use the newly learned theory in a practical way. New to the second edition: A "Review of Essential Mathematics" prologue, featuring tests and further links to help students refresh their knowledge of the core mathematical concepts used to calculate basic statistics. Updated screenshots on using IBM SPSS and Excel. A "Statistics in the Real World" feature included at the end of each chapter, demonstrating how statistics are applied in real-world business settings and research, accompanied by reflective questions. Updated case studies, examples and diagrams, illustrating key points and helping to reinforce learning. The book is accompanied by free online resources including step-by-step video tutorials on how to use Excel and IBM SPSS, datasets and worked solutions, an Instructors’ Manual, Testbank, and PowerPoint presentation slides for lecturers. Essential reading for business students wanting to know how to use statistics in a business setting.
R 7,709.00
Introduction to Structural Equation Modeling Using IBM SPSS Statistics and EQS
This student orientated guide to structural equation modeling promotes theoretical understanding and inspires students with the confidence to successfully apply SEM. Assuming no previous experience, and a minimum of mathematical knowledge, this is an invaluable companion for students taking introductory SEM courses in any discipline. Niels Blunch shines a light on each step of the structural equation modeling process, providing a detailed introduction to SPSS and EQS with a focus on EQS′ excellent graphical interface. He also sets out best practice for data entry and programming, and uses real life data to show how SEM is applied in research. The book includes: Learning objectives, key concepts and questions for further discussion in each chapter. Helpful diagrams and screenshots to expand on concepts covered in the texts. A wide variety of examples from multiple disciplines and real world contexts. Exercises for each chapter on an accompanying . A detailed glossary. Clear, engaging and built around key software, this is an ideal introduction for anyone new to SEM.
R 3,536.58
Data Analysis
Statistics lectures have been a source of much bewilderment and frustration for generations of students. This book attempts to remedy the situation by expounding a logical and unified approach to the whole subject of data analysis.This text is intended as a tutorial guide for senior undergraduates and research students in science and engineering. After explaining the basic principles of Bayesian probability theory, their use is illustrated with a variety of examples ranging from elementary parameter estimation to image processing. Other topics covered include reliability analysis, multivariate optimization, least-squares and maximum likelihood, error-propagation, hypothesis testing, maximum entropy and experimentaldesign.The Second Edition of this successful tutorial book contains a new chapter on extensions to the ubiquitous least-squares procedure, allowing for the straightforward handling of outliers and unknown correlated noise, and a cutting-edge contribution from John Skilling on a novel numerical technique for Bayesian computation called 'nested sampling'.
R 2,591.00
Discovering Statistics Using IBM SPSS Statistics
With its unique combination of humour and step-by-step instruction, this award-winning book is the statistics lifesaver for everyone. From initial theory through to regression, factor analysis and multilevel modelling, Andy Field animates statistics and SPSS software with his famously bizarre examples and activities.Features:• Flexible coverage to support students across disciplines and degree programmes• Can support classroom or lab learning and assessment• Analysis of real data with opportunities to practice statistical skills• Highlights common misconceptions and errors• A revamped online resource that uses video, case studies, datasets, testbanks and more to help students negotiate project work, master data management techniques, and apply key writing and employability skills• Covers the range of versions of IBM SPSS Statistics©.All the online resources above (video, case studies, datasets, testbanks) can be easily integrated into your institution′s virtual learning environment or learning management system. This allows you to customize and curate content for use in module preparation, delivery and assessment.
R 7,523.00
Data Analysis
The fourth edition of this successful textbook presents a comprehensive introduction to statistical and numerical methods for the evaluation of empirical and experimental data. Equal weight is given to statistical theory and practical problems. The concise mathematical treatment of the subject matter is illustrated by many examples and for the present edition a library of Java programs has been developed. It comprises methods of numerical data analysis and graphical representation as well as many example programs and solutions to programming problems.The book is conceived both as an introduction and as a work of reference. In particular it addresses itself to students, scientists and practitioners in science and engineering as a help in the analysis of their data in laboratory courses, in working for bachelor or master degrees, in thesis work, and in research and professional work.
R 2,461.98
Introduction to Data Analysis
Covering the general process of data analysis to finding, collecting, organizing, and presenting data, this book offers a complete introduction to the fundamentals of data analysis. Using real-world case studies as illustrations, it helps readers understand theories behind and develop techniques for conducting quantitative, qualitative, and mixed methods data analysis. With an easy-to-follow organization and clear, jargon-free language, it helps readers not only become proficient data analysts, but also develop the critical thinking skills necessary to assess analyses presented by others in both academic research and the popular media. It includes advice on: - Data analysis frameworks - Validity and credibility of data - Sampling techniques - Data management - The big data phenomenon - Data visualisation - Effective data communication Whether you are new to data analysis or looking for a quick-reference guide to key principles of the process, this book will help you uncover nuances, complexities, patterns, and relationships among all types of data.
R 2,920.90
Introduction to Data Analysis using Aggregation Functions in R
This textbook helps future data analysts comprehend aggregation function theory and methods in an accessible way, focusing on a fundamental understanding of the data and summarization tools. Offering a broad overview of recent trends in aggregation research, it complements any study in statistical or machine learning techniques. Readers will learn how to program key functions in R without obtaining an extensive programming background.Sections of the textbook cover background information and context, aggregating data with averaging functions, power means, and weighted averages including the Borda count. It explains how to transform data using normalization or scaling and standardization, as well as log, polynomial, and rank transforms. The section on averaging with interaction introduces OWS functions and the Choquet integral, simple functions that allow the handling of non-independent inputs. The final chapters examine software analysis with an emphasis on parameter identification rather than technical aspects.This textbook is designed for students studying computer science or business who are interested in tools for summarizing and interpreting data, without requiring a strong mathematical background. It is also suitable for those working on sophisticated data science techniques who seek a better conception of fundamental data aggregation. Solutions to the practice questions are included in the textbook.
R 3,303.22
Data Analysis
This book is intended for psychology majors and graduate students who are conducting experiments for the first time and are faced with the task of making sense out of their data. This much needed "how-to-do-it" text illustrates the application of statistical methods to the data from small samples. It also serves as a handbook, with twenty-two tables presented at the end of the text that will allow the student to carry out virtually every computation necessary in analyzing his data. Almost all of the examples and illustrations are drawn from actual experiments so that the student can see how professional scientists examine their data. The book also shows students the kinds of data that are encountered in psychological research, the kinds of questions investigators seek to answer, and how these questions are approached.The author asserts that statistics is not an abstract discipline but a tool in research. However, the book also imparts a philosophy of data analysis and its meaning, a concern for questions of the function of data analysis and the interpretations that legitimately can be drawn from data. In brief, Data Analysis asks: What kinds of data are met in psychological research? What can we do with these data? What can we conclude as a result of this doing?The book will be invaluable for students who, even though they may have taken a previous statistics course, are still unsure of what statistical techniques should be used in interpreting their data.
R 2,795.85
Data Analysis for the Social Sciences
′This book fosters in-depth understanding of the logic underpinning the most common statistical tests within the behavioural sciences. By emphasising the shared ground between these tests, the author provides crucial scaffolding for students as they embark upon their research journey.′ —Ruth Horry, Psychology, Swansea University ′This unique text presents the conceptual underpinnings of statistics as well as the computation and application of statistics to real-life situations--a combination rarely covered in one book. A must-have for students learning statistical techniques and a go-to handbook for experienced researchers.′ —Barbra Teater, Social Work, College of Staten Island, City University of New York Accessible, engaging, and informative, this book will help any social science student approach statistics with confidence. With a well-paced and well-judged integrated approach rather than a simple linear trajectory, this book progresses at a realistic speed that matches the pace at which statistics novices actually learn. Packed with global, interdisciplinary examples that ground statistical theory and concepts in real-world situations, it shows students not only how to apply newfound knowledge using IBM SPSS Statistics, but also why they would want to. Spanning statistics basics like variables, constants, and sampling through to t-tests, multiple regression and factor analysis, it builds statistical literacy while also covering key research principles like research questions, error types and results reliability. It shows you how to: Describe data with graphs, tables, and numbersCalculate probability and value distributionsTest a priori and post hoc hypothesesConduct Chi-squared tests and observational studiesStructure ANOVA, ANCOVA, and factorial designs Supported by lots of visuals and a website with interactive demonstrations, author video, and practice datasets, this book is the student-focused companion to support students through their statistics journeys.
R 6,385.00
Data Analysis
Written at a level appropriate for the advanced undergraduate course on data analysis, this accessible volume introduces the reader to the "art" of data analysis from data-gathering to multiple regression in which a dependent variable is influenced by several independent variables. The book focuses on the interpretation of a statistical result, in particular those that come from non-experimental social research. Using a consistent data set throughout the book in order to illustrate the various analytic techniques, the author covers such topics as univariate statistics, measures of association, the statistical significance of the relationship between two variables, and simple regression where the dependent variable is influenced by a single independent variable. The last chapter offers analysis recommendations. Data Analysis will provide social science researchers with the tools to select and evaluate statistical tests appropriate for their particular research question.
R 1,371.73
Introduction to Structural Equation Modeling Using IBM SPSS Statistics and Amos
This comprehensive Second Edition offers readers a complete guide to carrying out research projects involving structural equation modeling (SEM). Updated to include extensive analysis of AMOS′ graphical interface, a new chapter on latent curve models and detailed explanations of the structural equation modeling process, this second edition is the ideal guide for those new to the field. The book includes: Learning objectives, key concepts and questions for further discussion in each chapter.Helpful diagrams and screenshots to expand on concepts covered in the texts.Real life examples from a variety of disciplines to show how SEM is applied in real research contexts.Exercises for each chapter on an accompanying companion website.A new glossary. Assuming no previous experience of the subject, and a minimum of mathematical knowledge, this is the ideal guide for those new to SEM and an invaluable companion for students taking introductory SEM courses in any discipline. Niels J. Blunch was formerly in the Department of Marketing and Statistics at the University of Aarhus, Denmark
R 3,979.43
Statistical Data Analysis Using Your Personal Computer
What should you see when you′re analyzing real data using one of the major statistical packages, such as SPSS, SAS or Microsoft Excel? This book will show you, and will walk you through the output from a variety of statistical outcomes, such as data reflecting a single common factor. Through the use of actual demonstrations, the authors supply readers with the computer programs necessary to simulate data sets with the statistical properties (usually multivariate) that are often assumed of real data. The reader is then shown how to analyze these data sets and how to interpret the results. The book begins with a general introduction to doing research and tips for using the three statistical packages. The authors next explore how to create data structures and perform univariate, bivariate, and multivariate simulations. They then show how to use the simulations to understand common statistical algorithms and their outputs when doing a basic correlation analysis, exploratory factor analysis, confirmatory factor analysis, multidimensional scaling, multiple regression, discriminate analysis, classification analysis and MANOVA. Throughout the book, the authors provide the reader with helpful guides, such as: *Hint boxes to give readers tips for executing particular techniques using the statistical software packages. *Steps that show each stage of a procedure, such as importing an Excel file into SAS. *Problems end each chapter so the reader can practice the techniques described. *Web Site with the SAS and SPSS programs and sample data.
R 7,104.15
Data Analysis for the Social Sciences
′This book fosters in-depth understanding of the logic underpinning the most common statistical tests within the behavioural sciences. By emphasising the shared ground between these tests, the author provides crucial scaffolding for students as they embark upon their research journey.′ —Ruth Horry, Psychology, Swansea University ′This unique text presents the conceptual underpinnings of statistics as well as the computation and application of statistics to real-life situations--a combination rarely covered in one book. A must-have for students learning statistical techniques and a go-to handbook for experienced researchers.′ —Barbra Teater, Social Work, College of Staten Island, City University of New York Accessible, engaging, and informative, this book will help any social science student approach statistics with confidence. With a well-paced and well-judged integrated approach rather than a simple linear trajectory, this book progresses at a realistic speed that matches the pace at which statistics novices actually learn. Packed with global, interdisciplinary examples that ground statistical theory and concepts in real-world situations, it shows students not only how to apply newfound knowledge using IBM SPSS Statistics, but also why they would want to. Spanning statistics basics like variables, constants, and sampling through to t-tests, multiple regression and factor analysis, it builds statistical literacy while also covering key research principles like research questions, error types and results reliability. It shows you how to: Describe data with graphs, tables, and numbersCalculate probability and value distributionsTest a priori and post hoc hypothesesConduct Chi-squared tests and observational studiesStructure ANOVA, ANCOVA, and factorial designs Supported by lots of visuals and a website with interactive demonstrations, author video, and practice datasets, this book is the student-focused companion to support students through their statistics journeys.
R 3,590.00
Introduction to Statistics and Data Analysis
Now in its second edition, this introductory statistics textbook conveys the essential concepts and tools needed to develop and nurture statistical thinking. It presents descriptive, inductive and explorative statistical methods and guides the reader through the process of quantitative data analysis. This revised and extended edition features new chapters on logistic regression, simple random sampling, including bootstrapping, and causal inference.The text is primarily intended for undergraduate students in disciplines such as business administration, the social sciences, medicine, politics, and macroeconomics. It features a wealth of examples, exercises and solutions with computer code in the statistical programming language R, as well as supplementary material that will enable the reader to quickly adapt the methods to their own applications.
R 4,096.79
Introduction to Data Analysis
Covering the general process of data analysis to finding, collecting, organizing, and presenting data, this book offers a complete introduction to the fundamentals of data analysis. Using real-world case studies as illustrations, it helps readers understand theories behind and develop techniques for conducting quantitative, qualitative, and mixed methods data analysis. With an easy-to-follow organization and clear, jargon-free language, it helps readers not only become proficient data analysts, but also develop the critical thinking skills necessary to assess analyses presented by others in both academic research and the popular media. It includes advice on: - Data analysis frameworks - Validity and credibility of data - Sampling techniques - Data management - The big data phenomenon - Data visualisation - Effective data communication Whether you are new to data analysis or looking for a quick-reference guide to key principles of the process, this book will help you uncover nuances, complexities, patterns, and relationships among all types of data.
R 5,545.34
Introduction to Bayesian Data Analysis for Cognitive Science
This book introduces Bayesian data analysis and Bayesian cognitive modeling to students and researchers in cognitive science (e.g., linguistics, psycholinguistics, psychology, computer science), with a particular focus on modeling data from planned experiments. The book relies on the probabilistic programming language Stan and the R package brms, which is a front-end to Stan. The book only assumes that the reader is familiar with the statistical programming language R, and has basic high school exposure to pre-calculus mathematics; some of the important mathematical constructs needed for the book are introduced in the first chapter.Through this book, the reader will be able to develop a practical ability to apply Bayesian modeling within their own field. The book begins with an informal introduction to foundational topics such as probability theory, and univariate and bi-/multivariate discrete and continuous random variables. Then, the application of Bayes' rule for statistical inference is introduced with several simple analytical examples that require no computing software; the main insight here is that the posterior distribution of a parameter is a compromise between the prior and the likelihood functions. The book then gradually builds up the regression framework using the brms package in R, ultimately leading to hierarchical regression modeling (aka the linear mixed model). Along the way, there is detailed discussion about the topic of prior selection, and developing a well-defined workflow. Later chapters introduce the Stan programming language, and cover advanced topics using practical examples: contrast coding, model comparison using Bayes factors and cross-validation, hierarchical models and reparameterization, defining custom distributions, measurement error models and meta-analysis, and finally, some examples of cognitive models: multinomial processing trees, finite mixture models, and accumulator models. Additional chapters, appendices, and exercises are provided as online materials and can be accessed here: https://github.com/bnicenboim/bayescogsci.
R 3,544.27
Introduction to Structural Equation Modeling Using IBM SPSS Statistics and Amos
This comprehensive Second Edition offers readers a complete guide to carrying out research projects involving structural equation modeling (SEM). Updated to include extensive analysis of AMOS′ graphical interface, a new chapter on latent curve models and detailed explanations of the structural equation modeling process, this second edition is the ideal guide for those new to the field. The book includes: Learning objectives, key concepts and questions for further discussion in each chapter.Helpful diagrams and screenshots to expand on concepts covered in the texts.Real life examples from a variety of disciplines to show how SEM is applied in real research contexts.Exercises for each chapter on an accompanying companion website.A new glossary. Assuming no previous experience of the subject, and a minimum of mathematical knowledge, this is the ideal guide for those new to SEM and an invaluable companion for students taking introductory SEM courses in any discipline. Niels J. Blunch was formerly in the Department of Marketing and Statistics at the University of Aarhus, Denmark
R 7,012.18
SPSS Guide to the New Statistical Analysis of Data
This companion to The New Statistical Analysis of Data by Anderson and Finn provides a hands-on guide to data analysis using SPSS. Included with this guide are instructions for obtaining the data sets to be analysed via the World Wide Web. First, the authors provide a brief review of using SPSS, and then, corresponding to the organisation of The New Statistical Analysis of Data, readers participate in analysing many of the data sets discussed in the book. In so doing, students both learn how to conduct reasonably sophisticated statistical analyses using SPSS whilst at the same time gaining an insight into the nature and purpose of statistical investigation.
R 3,100.58
Data Analysis
Data analysis has been a hot topic for a number of years, and many future data scientists have backgrounds that are relatively light in mathematics. This slim volume provides a very approachable guide to the techniques of the subject, designed with such people in mind. Formulae are kept to a minimum, but the book's scope is broad, introducing the basic ideas of probability and statistics and more advanced techniques such as generalised linear models, classificationusing logistic regression, and support-vector machines.An essential feature of the book is that it does not tie to any particular software. The methods introduced in this book could also be implemented using any other statistical software and applying any major statistical package. Academically, the book amounts to a first course, practical for those at the undergraduate level, either as part of a mathematics/statistics degree or as a data-oriented option for a non-mathematics degree. The book appeals to would-be data scientists who may be formula shy. However, it could also be a relevant purchase for statisticians and mathematicians, for whom data science is a new departure, overall appealing to any computer-literate reader with data to analyse.
R 1,508.00
Introduction to Structural Equation Modeling Using IBM SPSS Statistics and EQS
This student orientated guide to structural equation modeling promotes theoretical understanding and inspires students with the confidence to successfully apply SEM. Assuming no previous experience, and a minimum of mathematical knowledge, this is an invaluable companion for students taking introductory SEM courses in any discipline. Niels Blunch shines a light on each step of the structural equation modeling process, providing a detailed introduction to SPSS and EQS with a focus on EQS′ excellent graphical interface. He also sets out best practice for data entry and programming, and uses real life data to show how SEM is applied in research. The book includes: Learning objectives, key concepts and questions for further discussion in each chapter. Helpful diagrams and screenshots to expand on concepts covered in the texts. A wide variety of examples from multiple disciplines and real world contexts. Exercises for each chapter on an accompanying . A detailed glossary. Clear, engaging and built around key software, this is an ideal introduction for anyone new to SEM.
R 6,159.87
Statistics Using IBM SPSS
Written in a clear and lively tone, Statistics Using IBM SPSS provides a data-centric approach to statistics with integrated SPSS (version 22) commands, ensuring that students gain both a deep conceptual understanding of statistics and practical facility with the leading statistical software package. With one hundred worked examples, the textbook guides students through statistical practice using real data and avoids complicated mathematics. Numerous end-of-chapter exercises allow students to apply and test their understanding of chapter topics, with detailed answers available online. The third edition has been updated throughout and includes a new chapter on research design, new topics (including weighted mean, resampling with the bootstrap, the role of the syntax file in workflow management, and regression to the mean) and new examples and exercises. Student learning is supported by a rich suite of online resources, including answers to end-of-chapter exercises, real data sets, PowerPoint slides, and a test bank.
R 4,171.39
Introduction to Bayesian Data Analysis for Cognitive Science
This book introduces Bayesian data analysis and Bayesian cognitive modeling to students and researchers in cognitive science (e.g., linguistics, psycholinguistics, psychology, computer science), with a particular focus on modeling data from planned experiments. The book relies on the probabilistic programming language Stan and the R package brms, which is a front-end to Stan. The book only assumes that the reader is familiar with the statistical programming language R, and has basic high school exposure to pre-calculus mathematics; some of the important mathematical constructs needed for the book are introduced in the first chapter.Through this book, the reader will be able to develop a practical ability to apply Bayesian modeling within their own field. The book begins with an informal introduction to foundational topics such as probability theory, and univariate and bi-/multivariate discrete and continuous random variables. Then, the application of Bayes' rule for statistical inference is introduced with several simple analytical examples that require no computing software; the main insight here is that the posterior distribution of a parameter is a compromise between the prior and the likelihood functions. The book then gradually builds up the regression framework using the brms package in R, ultimately leading to hierarchical regression modeling (aka the linear mixed model). Along the way, there is detailed discussion about the topic of prior selection, and developing a well-defined workflow. Later chapters introduce the Stan programming language, and cover advanced topics using practical examples: contrast coding, model comparison using Bayes factors and cross-validation, hierarchical models and reparameterization, defining custom distributions, measurement error models and meta-analysis, and finally, some examples of cognitive models: multinomial processing trees, finite mixture models, and accumulator models. Additional chapters, appendices, and exercises are provided as online materials and can be accessed here: https://github.com/bnicenboim/bayescogsci.
R 9,441.21
Exploratory Data Analysis in Business and Economics
In a world in which we are constantly surrounded by data, figures, and statistics, it is imperative to understand and to be able to use quantitative methods. Statistical models and methods are among the most important tools in economic analysis, decision-making and business planning. This textbook, “Exploratory Data Analysis in Business and Economics”, aims to familiarise students of economics and business as well as practitioners in firms with the basic principles, techniques, and applications of descriptive statistics and data analysis. Drawing on practical examples from business settings, it demonstrates the basic descriptive methods of univariate and bivariate analysis. The textbook covers a range of subject matter, from data collection and scaling to the presentation and univariate analysis of quantitative data, and also includes analytic procedures for assessing bivariate relationships. It does not confine itself to presenting descriptive statistics, but also addresses the use of computer programmes such as Excel, SPSS, and STATA, thus treating all of the topics typically covered in a university course on descriptive statistics. The German edition of this textbook is one of the “bestsellers” on the German market for literature in statistics.
R 3,540.07
Statistical Data Analysis Using Your Personal Computer
What should you see when you′re analyzing real data using one of the major statistical packages, such as SPSS, SAS or Microsoft Excel? This book will show you, and will walk you through the output from a variety of statistical outcomes, such as data reflecting a single common factor. Through the use of actual demonstrations, the authors supply readers with the computer programs necessary to simulate data sets with the statistical properties (usually multivariate) that are often assumed of real data. The reader is then shown how to analyze these data sets and how to interpret the results. The book begins with a general introduction to doing research and tips for using the three statistical packages. The authors next explore how to create data structures and perform univariate, bivariate, and multivariate simulations. They then show how to use the simulations to understand common statistical algorithms and their outputs when doing a basic correlation analysis, exploratory factor analysis, confirmatory factor analysis, multidimensional scaling, multiple regression, discriminate analysis, classification analysis and MANOVA. Throughout the book, the authors provide the reader with helpful guides, such as: *Hint boxes to give readers tips for executing particular techniques using the statistical software packages. *Steps that show each stage of a procedure, such as importing an Excel file into SAS. *Problems end each chapter so the reader can practice the techniques described. *Web Site with the SAS and SPSS programs and sample data.
R 4,945.38
Introduction to Applied Multivariate Analysis
This comprehensive text introduces readers to the most commonly used multivariate techniques at an introductory, non-technical level. By focusing on the fundamentals, readers are better prepared for more advanced applied pursuits, particularly on topics that are most critical to the behavioral, social, and educational sciences. Analogies between the already familiar univariate statistics and multivariate statistics are emphasized throughout. The authors examine in detail how each multivariate technique can be implemented using SPSS and SAS and Mplus in the book’s later chapters. Important assumptions are discussed along the way along with tips for how to deal with pitfalls the reader may encounter. Mathematical formulas are used only in their definitional meaning rather than as elements of formal proofs.A book specific website - www.psypress.com/applied-multivariate-analysis - provides files with all of the data used in the text so readers can replicate the results. The Appendix explains the data files and its variables. The software code (for SAS and Mplus) and the menu option selections for SPSS are also discussed in the book. The book is distinguished by its use of latent variable modeling to address multivariate questions specific to behavioral and social scientists including missing data analysis and longitudinal data modeling.Ideal for graduate and advanced undergraduate students in the behavioral, social, and educational sciences, this book will also appeal to researchers in these disciplines who have limited familiarity with multivariate statistics. Recommended prerequisites include an introductory statistics course with exposure to regression analysis and some familiarity with SPSS and SAS.
R 9,350.97
Introduction to Statistical Data Science
This graduate textbook on the statistical approach to data science describes the basic ideas, scientific principles and common techniques for the extraction of mathematical models from observed data. Aimed at young scientists, and motivated by their scientific prospects, it provides first principle derivations of various algorithms and procedures, thereby supplying a solid background for their future specialization to diverse fields and applications.The beginning of the book presents the basics of statistical science, with an exposition on linear models. This is followed by an analysis of some numerical aspects and various regularization techniques, including LASSO, which are particularly important for large scale problems. Decision problems are studied both from the classical hypothesis testing perspective and, particularly, from a modern support-vector perspective, in the linear and non-linear context alike. Underlying the book is the Bayesian approach and the Bayesian interpretation of various algorithms and procedures. This is the key to principal components analysis and canonical correlation analysis, which are explained in detail. Following a chapter on nonlinear inference, including material on neural networks, the book concludes with a discussion on time series analysis and estimating their dynamic models.Featuring examples and exercises partially motivated by engineering applications, this book is intended for graduate students in applied mathematics and engineering with a general background in probability and linear algebra.
R 6,003.84
R 550.00
Introduction to Data Analysis and Graphical Presentation in Biostatistics with R
Through real-world datasets, this book shows the reader how to work with material in biostatistics using the open source software R. These include tools that are critical to dealing with missing data, which is a pressing scientific issue for those engaged in biostatistics. Readers will be equipped to run analyses and make graphical presentations based on the sample dataset and their own data. The hands-on approach will benefit students and ensure the accessibility of this book for readers with a basic understanding of R. Topics include: an introduction to Biostatistics and R, data exploration, descriptive statistics and measures of central tendency, t-Test for independent samples, t-Test for matched pairs, ANOVA, correlation and linear regression, and advice for future work.
R 2,243.80
Introduction to Data Analysis in R
This textbook offers an easy-to-follow, practical guide to modern data analysis using the programming language R. The chapters cover topics such as the fundamentals of programming in R, data collection and preprocessing, including web scraping, data visualization, and statistical methods, including multivariate analysis, and feature exercises at the end of each section. The text requires only basic statistics skills, as it strikes a balance between statistical and mathematical understanding and implementation in R, with a special emphasis on reproducible examples and real-world applications. This textbook is primarily intended for undergraduate students of mathematics, statistics, physics, economics, finance and business who are pursuing a career in data analytics. It will be equally valuable for master students of data science and industry professionals who want to conduct data analyses.
R 3,663.00
Multilevel Modeling of Categorical Outcomes Using IBM SPSS
This is the first workbook that introduces the multilevel approach to modeling with categorical outcomes using IBM SPSS Version 20. Readers learn how to develop, estimate, and interpret multilevel models with categorical outcomes. The authors walk readers through data management, diagnostic tools, model conceptualization, and model specification issues related to single-level and multilevel models with categorical outcomes. Screen shots clearly demonstrate techniques and navigation of the program. Modeling syntax is provided in the appendix. Examples of various types of categorical outcomes demonstrate how to set up each model and interpret the output. Extended examples illustrate the logic of model development, interpretation of output, the context of the research questions, and the steps around which the analyses are structured. Readers can replicate examples in each chapter by using the corresponding data and syntax files available at www.psypress.com/9781848729568. The book opens with a review of multilevel with categorical outcomes, followed by a chapter on IBM SPSS data management techniques to facilitate working with multilevel and longitudinal data sets. Chapters 3 and 4 detail the basics of the single-level and multilevel generalized linear model for various types of categorical outcomes. These chapters review underlying concepts to assist with trouble-shooting common programming and modeling problems. Next population-average and unit-specific longitudinal models for investigating individual or organizational developmental processes are developed. Chapter 6 focuses on single- and multilevel models using multinomial and ordinal data followed by a chapter on models for count data. The book concludes with additional trouble shooting techniques and tips for expanding on the modeling techniques introduced.Ideal as a supplement for graduate level courses and/or professional workshops on multilevel, longitudinal, latent variable modeling, multivariate statistics, and/or advanced quantitative techniques taught in psychology, business, education, health, and sociology, this practical workbook also appeals to researchers in these fields. An excellent follow up to the authors’ highly successful Multilevel and Longitudinal Modeling with IBM SPSS and Introduction to Multilevel Modeling Techniques, 2nd Edition, this book can also be used with any multilevel and/or longitudinal book or as a stand-alone text introducing multilevel modeling with categorical outcomes.
R 3,060.34
