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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.
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
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
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.
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 output Descriptive statistics procedures Score distribution assumption evaluations Bivariate correlation Regressing (predicting) quantitative and categorical variables Survival analysis t Test ANOVA and ANCOVA Multivariate group differences Multidimensional scaling Cluster analysis Nonparametric 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.
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.
The updated Second Edition of Alan C. Elliott and Wayne A. Woodward’s "cut to the chase" IBM SPSS guide quickly explains the when, where, and how of statistical data analysis as it is used for real-world decision making in a wide variety of disciplines. This one-stop reference provides succinct guidelines for performing an analysis using SPSS software, avoiding pitfalls, interpreting results, and reporting outcomes. Written from a practical perspective, IBM SPSS by Example, Second Edition provides a wealth of information—from assumptions and design to computation, interpretation, and presentation of results—to help users save time, money, and frustration.
An up-to-date, comprehensive treatment of a classic text on missing data in statistics The topic of missing data has gained considerable attention in recent decades. This new edition by two acknowledged experts on the subject offers an up-to-date account of practical methodology for handling missing data problems. Blending theory and application, authors Roderick Little and Donald Rubin review historical approaches to the subject and describe simple methods for multivariate analysis with missing values. They then provide a coherent theory for analysis of problems based on likelihoods derived from statistical models for the data and the missing data mechanism, and then they apply the theory to a wide range of important missing data problems. Statistical Analysis with Missing Data, Third Edition starts by introducing readers to the subject and approaches toward solving it. It looks at the patterns and mechanisms that create the missing data, as well as a taxonomy of missing data. It then goes on to examine missing data in experiments, before discussing complete-case and available-case analysis, including weighting methods. The new edition expands its coverage to include recent work on topics such as nonresponse in sample surveys, causal inference, diagnostic methods, and sensitivity analysis, among a host of other topics. An updated “classic” written by renowned authorities on the subject Features over 150 exercises (including many new ones) Covers recent work on important methods like multiple imputation, robust alternatives to weighting, and Bayesian methods Revises previous topics based on past student feedback and class experience Contains an updated and expanded bibliography The authors were awarded The Karl Pearson Prize in 2017 by the International Statistical Institute, for a research contribution that has had profound influence on statistical theory, methodology or applications. Their work "has been no less than defining and transforming." (ISI) Statistical Analysis with Missing Data, Third Edition is an ideal textbook for upper undergraduate and/or beginning graduate level students of the subject. It is also an excellent source of information for applied statisticians and practitioners in government and industry.
This book will help you gain a master of business administration (MBA) degree. Think you’ve got what it takes to become a future leader? An MBA could help you achieve those goals. Intensive, competitive and highly respected, the Master of Business Administration (MBA) is an elite professional qualification. This book provides best reports with good grades. Reading the papers, you can get a sense of how to write a good paper to get good grades. This is a book that tells you how to get good grades on MBA courses in the U.S. For the MBA course, students have to take a total of 36 credits. Each class is worth 3 credits and the students should take 12 classes. It's a series of 12 books, one book for each subject. This book is a collection of best answers for the "Applied Data Analysis" subject.
The third edition of Research Methods for Political Science retains its effective approach to helping students learn what to research, why to research and how to research. The text integrates both quantitative and qualitative approaches to research in one volume and covers such important topics as research design, specifying research problems, designing questionnaires and writing questions, designing and carrying out qualitative research and analyzing both quantitative and qualitative research data. Heavily illustrated, classroom tested, exceptionally readable and engaging, the text presents statistical methods in a conversational tone to help students surmount "math phobia." Updates to this new edition include: Research topics chapters have been upgraded and expanded. Two mixed methods design chapters have been added. A new chapter on hermeneutic analysis designs and research with large data sets. The chapter on multivariate statistics has been expanded, with an expanded discussion on logistic regression. Tools on how to prepare and present research findings are now featured in the appendix, allowing instructors more flexibility when teaching their courses. Research Methods for Political Science will give students the confidence and knowledge they need to understand the methods and basics skills for data collection, presentation and analysis.