Download or read book Handbook of Univariate and Multivariate Data Analysis with IBM SPSS written by Robert Ho and published by CRC Press. This book was released on 2013-10-25 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Handbook of Univariate and Multivariate Data Analysis with IBM SPSS Second Edition written by Robert Ho and published by CRC Press. This book was released on 2013-10-25 with total page 588 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 correlation New section on how to deal with missing data Coverage of tests of assumptions, such as linearity, outliers, normality, homogeneity of variance-covariance matrices, and multicollinearity Discussions 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.

Download or read book Handbook of Univariate and Multivariate Data Analysis and Interpretation with SPSS written by Robert Ho and published by CRC Press. This book was released on 2006-03-24 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many statistics texts tend to focus more on the theory and mathematics underlying statistical tests than on their applications and interpretation. This can leave readers with little understanding of how to apply statistical tests or how to interpret their findings. While the SPSS statistical software has done much to alleviate the frustrations of s

Download or read book Handbook of Univariate and Multivariate Data Analysis and Interpretation written by Robert Ho and published by . This book was released on 2000 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multivariate Methods and Forecasting with IBM SPSS Statistics written by Abdulkader Aljandali and published by Springer. This book was released on 2017-07-06 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the second of a two-part guide to quantitative analysis using the IBM SPSS Statistics software package; this volume focuses on multivariate statistical methods and advanced forecasting techniques. More often than not, regression models involve more than one independent variable. For example, forecasting methods are commonly applied to aggregates such as inflation rates, unemployment, exchange rates, etc., that have complex relationships with determining variables. This book introduces multivariate regression models and provides examples to help understand theory underpinning the model. The book presents the fundamentals of multivariate regression and then moves on to examine several related techniques that have application in business-orientated fields such as logistic and multinomial regression. Forecasting tools such as the Box-Jenkins approach to time series modeling are introduced, as well as exponential smoothing and naïve techniques. This part also covers hot topics such as Factor Analysis, Discriminant Analysis and Multidimensional Scaling (MDS).

Download or read book Himalayan Quality of Life written by Benjamin L. Saitluanga and published by Springer. This book was released on 2017-04-18 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book is a study of intra-urban inequality in quality of life (QOL) in Aizawl city. The main objectives of the study include analysis of processes and patterns of social differentiation along the three-dimensional space of Aizawl city as well as analysis of spatial inequality in QOL at the lowest administrative structure of the city. An investigation into spatial pattern of residential differentiation was done at both horizontal and vertical spaces. Spatial variation in well-being of residents of Aizawl city and the quality of their immediate environment was also studied by taking both objective and subjective indicators. The study employed a number of descriptive, inferential and multivariate statistical techniques including correlation, factor analysis, principal component analysis, cluster analysis and spatial autocorrelation methods like Moran’s I and Local Indicators of Spatial Association (LISA). Mapping techniques and graphical methods like Choropleth map, histogram and line graph were also used. With the help of factor analysis, the social space of Aizawl city was found to be differentiated along socio-economic status, family status, household size status, workers status and ethnic status. The most important factor determining residential differentiation was socio-economic status. Choropleth map of factor scores reveals that the inner city localities were dominated by high socio-economic class while poorer people dominated the peripheries. Non-local ethnic minorities were few but concentrated in some adjoining peripheral localities as well as in inner city localities which have been inhabited by their ancestors since the colonial period. Vertical pattern of residential differentiation was also analyzed by taking income variable as a proxy of socio-economic status. Multi-storey buildings in Aizawl city were co-inhabited by both richer people and poorer people. The richer people were found at the top floors while the poorer people occupied the basement floors. Normally, the owners of the buildings were found at the top floors while the basement floors were dominated by the renters. Spatial variation in QOL was measured with the help of principal component analysis as a weighting technique by taking variables pertaining to both objective and subjective QOL dimensions. The values of composite QOL index showed that the central localities have scored better than their peripheral counterparts. Correlation analysis of the relationship between objective indicators and subjective indicators provided a low positive value indicating the absence of relationship between the two dimensions of quality of life. Spatial autocorrelation analysis was also performed to see the pattern of clustering of spatially weighted QOL variables across Local Councils. With the help of Global Moran’s I, spatial clusters and spatial outliers were observed for objective dimension of QOL within the study area. The value of Moran’s I was found to be insignificant for subjective QOL dimension indicating the absence of significant pattern of clustering. The study also identified 7 social areas of Aizawl city on the basis of factor scores and composite scores of QOL variables calculated for all Local Councils. The identification of clusters was taken out with the help of hierarchical clustering method of cluster analysis. These clusters were labeled appropriate names and their characteristics were described in detail. The thesis concluded with recommendation of designating these social areas as ‘social development planning zones’ for obtaining inclusive development.

Download or read book JMP for Basic Univariate and Multivariate Statistics written by Ann Lehman and published by SAS Institute. This book was released on 2013 with total page 559 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to manage JMP data and perform the statistical analyses most commonly used in research in the social sciences and other fields with JMP for Basic Univariate and Multivariate Statistics: Methods for Researchers and Social Scientists, Second Edition. Updated for JMP 10 and including new features on the statistical platforms, this book offers clearly written instructions to guide you through the basic concepts of research and data analysis, enabling you to easily perform statistical analyses and solve problems in real-world research. Step by step, you'll discover how to obtain descriptive and inferential statistics, summarize results clearly in a way that is suitable for publication, perform a wide range of JMP analyses, interpret the results, and more. Topics include screening data for errors selecting subsets computing the coefficient alpha reliability index (Cronbach's alpha) for a multiple-item scale performing bivariate analyses for all types of variables performing a one-way analysis of variance (ANOVA), multiple regression, and a one-way multivariate analysis of variance (MANOVA) Advanced topics include analyzing models with interactions and repeated measures. There is also comprehensive coverage of principle components with emphasis on graphical interpretation. This user-friendly book introduces researchers and students of the social sciences to JMP and to elementary statistical procedures, while the more advanced statistical procedures that are presented make it an invaluable reference guide for experienced researchers as well.

Download or read book Applied Univariate Bivariate and Multivariate Statistics written by Daniel J. Denis and published by John Wiley & Sons. This book was released on 2021-04-01 with total page 578 pages. Available in PDF, EPUB and Kindle. Book excerpt: AN UPDATED GUIDE TO STATISTICAL MODELING TECHNIQUES USED IN THE SOCIAL AND NATURAL SCIENCES This revised and updated second edition of Applied Univariate, Bivariate, and Multivariate Statistics: Understanding Statistics for Social and Natural Scientists, with Applications in SPSS and R contains an accessible introduction to statistical modeling techniques commonly used in the social and natural sciences. The text offers a blend of statistical theory and methodology and reviews both the technical and theoretical aspects of good data analysis. Featuring applied resources at various levels, the book includes statistical techniques using software packages such as R and SPSS®. To promote a more in-depth interpretation of statistical techniques across the sciences, the book surveys some of the technical arguments underlying formulas and equations. The second edition has been designed to be more approachable by minimizing theoretical or technical jargon and maximizing conceptual understanding with easy-to-apply software examples. This important text: Offers demonstrations of statistical techniques using software packages such as R and SPSS® Contains examples of hypothetical and real data with statistical analyses Provides historical and philosophical insights into many of the techniques used in modern science Includes a companion website that features further instructional details, additional data sets, and solutions to selected exercises Written for students of social and applied sciences, Applied Univariate, Bivariate, and Multivariate Statistics, Second Edition offers a thorough introduction to the world of statistical modeling techniques in the sciences.

Download or read book Applied Univariate Bivariate and Multivariate Statistics Using Python written by Daniel J. Denis and published by John Wiley & Sons Incorporated. This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Applied Univariate, Bivariate, and Multivariate Statistics Using Python A practical, "how-to" reference for anyone performing essential statistical analyses and data management tasks in Python Applied Univariate, Bivariate, and Multivariate Statistics Using Python delivers a comprehensive introduction to a wide range of statistical methods performed using Python in a single, one-stop reference. The book contains user-friendly guidance and instructions on using Python to run a variety of statistical procedures without getting bogged down in unnecessary theory. Throughout, the author emphasizes a set of computational tools used in the discovery of empirical patterns, as well as several popular statistical analyses and data management tasks that can be immediately applied. Most of the datasets used in the book are small enough to be easily entered into Python manually, though they can also be downloaded for free from www.datapsyc.com. Only minimal knowledge of statistics is assumed, making the book perfect for those seeking an easily accessible toolkit for statistical analysis with Python. Applied Univariate, Bivariate, and Multivariate Statistics Using Python represents the fastest way to learn how to analyze data with Python. Readers will also benefit from the inclusion of: A review of essential statistical principles, including types of data, measurement, significance tests, significance levels, and type I and type II errors An introduction to Python, exploring how to communicate with Python A treatment of exploratory data analysis, basic statistics and visual displays, including frequencies and descriptives, q-q plots, box-and-whisker plots, and data management An introduction to topics such as ANOVA, MANOVA and discriminant analysis, regression, principal components analysis, factor analysis, cluster analysis, among others, exploring the nature of what these techniques can vs. cannot do on a methodological level Perfect for undergraduate and graduate students in the social, behavioral, and natural sciences, Applied Univariate, Bivariate, and Multivariate Statistics Using Python will also earn a place in the libraries of researchers and data analysts seeking a quick go-to resource for univariate, bivariate, and multivariate analysis in Python.

Download or read book The Chicago Guide to Writing about Multivariate Analysis Second Edition written by Jane E. Miller and published by University of Chicago Press. This book was released on 2013-09-26 with total page 576 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many different people, from social scientists to government agencies to business professionals, depend on the results of multivariate models to inform their decisions. Researchers use these advanced statistical techniques to analyze relationships among multiple variables, such as how exercise and weight relate to the risk of heart disease, or how unemployment and interest rates affect economic growth. Yet, despite the widespread need to plainly and effectively explain the results of multivariate analyses to varied audiences, few are properly taught this critical skill. The Chicago Guide to Writing about Multivariate Analysis is the book researchers turn to when looking for guidance on how to clearly present statistical results and break through the jargon that often clouds writing about applications of statistical analysis. This new edition features even more topics and real-world examples, making it the must-have resource for anyone who needs to communicate complex research results. For this second edition, Jane E. Miller includes four new chapters that cover writing about interactions, writing about event history analysis, writing about multilevel models, and the “Goldilocks principle” for choosing the right size contrast for interpreting results for different variables. In addition, she has updated or added numerous examples, while retaining her clear voice and focus on writers thinking critically about their intended audience and objective. Online podcasts, templates, and an updated study guide will help readers apply skills from the book to their own projects and courses. This continues to be the only book that brings together all of the steps involved in communicating findings based on multivariate analysis—finding data, creating variables, estimating statistical models, calculating overall effects, organizing ideas, designing tables and charts, and writing prose—in a single volume. When aligned with Miller’s twelve fundamental principles for quantitative writing, this approach will empower readers—whether students or experienced researchers—to communicate their findings clearly and effectively.

Download or read book MULTIVARIATE DATA ANALYSIS written by R. Shanthi and published by MJP Publisher. This book was released on 2019-06-10 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multivariate Data Analysis Introduction to SPSS Outliers Normality Test of Linearity Data Transformation Bootstrapping Homoscedasticity Introduction to IBM SPSS – AMOS Multivariate Analysis of Variance (MANOVA) One Way Manova in SPSS Multiple Regression Analysis Binary Logistic Regression Factor Analysis Exploratory Factor Analysis Confirmatory Factor Analysis Cluster Analysis K - Mean Cluster Analysis Hierarchical Cluster Analysis Discriminant Analysis Correspondence Analysis Multidimensional Scaling Example - Multidimensional Scaling (ALSCAL) Neural Network Decision Trees Path Analysis Structural Equation Modeling Canonical Correlation

Download or read book Effect Sizes for Research written by Robert J. Grissom and published by Routledge. This book was released on 2012-04-23 with total page 453 pages. Available in PDF, EPUB and Kindle. Book excerpt: Noted for its comprehensive coverage, this greatly expanded new edition now covers the use of univariate and multivariate effect sizes. Many measures and estimators are reviewed along with their application, interpretation, and limitations. Noted for its practical approach, the book features numerous examples using real data for a variety of variables and designs, to help readers apply the material to their own data. Tips on the use of SPSS, SAS, R, and S-Plus are provided. The book's broad disciplinary appeal results from its inclusion of a variety of examples from psychology, medicine, education, and other social sciences. Special attention is paid to confidence intervals, the statistical assumptions of the methods, and robust estimators of effect sizes. The extensive reference section is appreciated by all. With more than 40% new material, highlights of the new editon include: three new multivariate chapters covering effect sizes for analysis of covariance, multiple regression/correlation, and multivariate analysis of variance more learning tools in each chapter including introductions, summaries, "Tips and Pitfalls" and more conceptual and computational questions more coverage of univariate effect sizes, confidence intervals, and effect sizes for repeated measures to reflect their increased use in research more software references for calculating effect sizes and their confidence intervals including SPSS, SAS, R, and S-Plus the data used in the book are now provided on the web along with new data and suggested calculations with IBM SPSS syntax for computational practice. Effect Sizes for Research covers standardized and unstandardized differences between means, correlational measures, strength of association, and parametric and nonparametric measures for between- and within-groups data. Intended as a resource for professionals, researchers, and advanced students in a variety of fields, this book is also an excellent supplement for advanced statistics courses in psychology, education, the social sciences, business, and medicine. A prerequisite of introductory statistics through factorial analysis of variance and chi-square is recommended.

Download or read book A Step by Step Approach to Using SAS for Univariate Multivariate Statistics written by Norm O'Rourke and published by SAS Institute. This book was released on 2005 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: Providing practice data inspired by actual studies, this book explains how to choose the right statistic, understand the assumptions underlying the procedure, prepare an SAS program for an analysis, interpret the output, and summarize the analysis and results according to the format prescribed in the Publication Manual of the American Psychological Association.

Download or read book Multivariate Analysis written by Klaus Backhaus and published by Springer Nature. This book was released on 2023-06-28 with total page 618 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data can be extremely valuable if we are able to extract information from them. This is why multivariate data analysis is essential for business and science. This book offers an easy-to-understand introduction to the most relevant methods of multivariate data analysis. It is strictly application-oriented, requires little knowledge of mathematics and statistics, demonstrates the procedures with numerical examples and illustrates each method via a case study solved with IBM’s statistical software package SPSS. Extensions of the methods and links to other procedures are discussed and recommendations for application are given. An introductory chapter presents the basic ideas of the multivariate methods covered in the book and refreshes statistical basics which are relevant to all methods. For the 2nd edition, all chapters were checked and calculated using the current version of IBM SPSS. Contents Introduction to empirical data analysis Regression analysis Analysis of variance Discriminant analysis Logistic regression Contingency analysis Factor analysis Cluster analysis Conjoint analysis The original German version is now available in its 17th edition. In 2015, this book was honored by the Federal Association of German Market and Social Researchers as “the textbook that has shaped market research and practice in German-speaking countries”. A Chinese version is available in its 3rd edition. On the website www.multivariate-methods.info, the authors further analyze the data with Excel and R and provide additional material to facilitate the understanding of the different multivariate methods. In addition, interactive flashcards are available to the reader for reviewing selected focal points. Download the Springer Nature Flashcards App and use exclusive content to test your knowledge.

Download or read book Social Science Research Design and Statistics written by Alfred P. Rovai and published by . This book was released on 2012-09-01 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book integrates social science research methods and the descriptions of 46 univariate, bivariate, and multivariate tests to include a description of the purpose, assumptions, example research question and hypothesis, SPSS procedure, and interpretation of SPSS output for each test. Included throughout the book are various sidebars highlighting key points, images and SPSS screenshots to assist understanding the material presented, self-test reviews at the end of each chapter, a decision tree to facilitate identification of the proper statistical test, examples of SPSS output with accompanying analysis and interpretations, links to relevant web sites, and a comprehensive glossary. Underpinning all these features is a concise, easy to understand explanation of the material.

Download or read book Approaching Multivariate Analysis 2nd Edition written by Pat Dugard and published by Taylor & Francis. This book was released on 2022-06-30 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: This fully updated new edition not only provides an introduction to a range of advanced statistical techniques that are used in psychology, but has been expanded to include new chapters describing methods and examples of particular interest to medical researchers. It takes a very practical approach, aimed at enabling readers to begin using the methods to tackle their own problems. This book provides a non-mathematical introduction to multivariate methods, with an emphasis on helping the reader gain an intuitive understanding of what each method is for, what it does and how it does it. The first chapter briefly reviews the main concepts of univariate and bivariate methods and provides an overview of the multivariate methods that will be discussed, bringing out the relationships among them, and summarising how to recognise what types of problem each of them may be appropriate for tackling. In the remaining chapters, introductions to the methods and important conceptual points are followed by the presentation of typical applications from psychology and medicine, using examples with fabricated data. Instructions on how to do the analyses and how to make sense of the results are fully illustrated with dialogue boxes and output tables from SPSS, as well as details of how to interpret and report the output, and extracts of SPSS syntax and code from relevant SAS procedures. This book gets students started, and prepares them to approach more comprehensive treatments with confidence. This makes it an ideal text for psychology students, medical students and students or academics in any discipline that uses multivariate methods.

Download or read book Applied Multivariate Data Analysis written by Brian S. Everitt and published by Wiley. This book was released on 2009-04-20 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multivariate analysis plays an important role in the understanding of complex data sets requiring simultaneous examination of all variables. Breaking through the apparent disorder of the information, it provides the means for both describing and exploring data, aiming to extract the underlying patterns and structure. This intermediate-level textbook introduces the reader to the variety of methods by which multivariate statistical analysis may be undertaken. Now in its 2nd edition, 'Applied Multivariate Data Analysis' has been fully expanded and updated, including major chapter revisions as well as new sections on neural networks and random effects models for longitudinal data. Maintaining the easy-going style of the first edition, the authors provide clear explanations of each technique, as well as supporting figures and examples, and minimal technical jargon. With extensive exercises following every chapter, 'Applied Multivariate Data Analysis' is a valuable resource for students on applied statistics courses and applied researchers in many disciplines.