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Master data management & analysis techniques with IBM SPSS Statistics 24About This BookLeverage the power of IBM SPSS Statistics to perform efficient statistical analysis of your dataChoose the right statistical technique to analyze different types of data and build efficient models from your data with easeOvercome any hurdle that you might come across while learning the different SPSS Statistics concepts with clear instructions, tips and tricksWho This Book Is For

This book is designed for analysts and researchers who need to work with data to discover meaningful patterns but do not have the time (or inclination) to become programmers. We assume a foundational understanding of statistics such as one would learn in a basic course or two on statistical techniques and methods.

What You Will LearnInstall and set up SPSS to create a working environment for analyticsTechniques for exploring data visually and statistically, assessing data quality and addressing issues related to missing dataHow to import different kinds of data and work with itOrganize data for analytical purposes (create new data elements, sampling, weighting, subsetting, and restructure your data)Discover basic relationships among data elements (bivariate data patterns, differences in means, correlations)Explore multivariate relationshipsLeverage the offerings to draw accurate insights from your research, and benefit your decision-makingIn Detail

SPSS Statistics is a software package used for logical batched and non-batched statistical analysis. Analytical tools such as SPSS can readily provide even a novice user with an overwhelming amount of information and a broad range of options for analyzing patterns in the data.

The journey starts with installing and configuring SPSS Statistics for first use and exploring the data to understand its potential (as well as its limitations). Use the right statistical analysis technique such as regression, classification and more, and analyze your data in the best possible manner. Work with graphs and charts to visualize your findings. With this information in hand, the discovery of patterns within the data can be undertaken. Finally, the high level objective of developing predictive models that can be applied to other situations will be addressed.

By the end of this book, you will have a firm understanding of the various statistical analysis techniques offered by SPSS Statistics, and be able to master its use for data analysis with ease.

Style and approach

Provides a practical orientation to understanding a set of data and examining the key relationships among the data elements. Shows useful visualizations to enhance understanding and interpretation. Outlines a roadmap that focuses the process so decision regarding how to proceed can be made easily.

Designed as a text for the undergraduate and postgraduate students of psychology, education, sociology, demography and economics, this comprehensive book explains the theoretical and computational aspects of statistics. Since the students of social sciences often find it difficult to comprehend the statistical techniques due to complex mathematical steps involved, this book explains each concept and related statistical derivations or formulae in a simple and clear manner.


The text provides solutions to basic concepts and problems using a number of illustrations. In addition, it demonstrates the simplest way of using SPSS software for statistical analysis. SPSS screen images are used to make the ideas more clear to the readers. This is preceded by theoretical details and solved examples so that even those having minimal knowledge of computer can use SPSS easily and comprehend the complex intermediate steps involved in statistical analysis. Besides the undergraduate and postgraduate students of social sciences, the researchers and professionals in this field should find this book immensely useful.


The Second Edition of the book has been prepared on the basis of the feedback received from the readers. As per their demand, a new chapter based on multivariate analysis, i.e., Factor analysis has been introduced. Many other chapters have been modified and updated to make them more effective and simple for the readers. Most importantly, screenshots of the latest version of SPSS have been incorporated in the relevant chapters to keep the students abreast with the developments in tools and techniques of statistics.
In social science research, differences among groups or changes over time are a common focus of study. While means and variances are typically the basis for statistical methods used in this research, the underlying social theory often implies properties of distributions that are not well captured by these summary measures. Examples include the current controversies regarding growing inequality in earnings, racial diferences in test scores, socio-economic correlates of birth outcomes, and the impact of smoking on survival and health. The distributional differences that animate the debates in these fields are complex. They comprise the usual mean-shifts and changes in variance, but also more subtle comparisons of changes in the upper and lower tails of distributions. Survey and census data on such attributes contain a wealth of distributional information, but traditional methods of data analysis leave much of this information untapped. In this monograph, we present methods for full comparative distributional analysis. The methods are based on the relative distribution, a nonparametric complete summary of the information required for scale--invariant comparisons between two distributions. The relative distribution provides a general integrated framework for analysis. It offers a graphical component that simplifies exploratory data analysis and display, a statistically valid basis for the development of hypothesis-driven summary measures, and the potential for decomposition that enables one to examine complex hypotheses regarding the origins of distributional changes within and between groups. The monograph is written for data analysts and those interested in measurement, and it can serve as a textbook for a course on distributional methods. The presentation is application oriented,
Dive deeper into SPSS Statistics for more efficient, accurate, and sophisticated data analysis and visualization

SPSS Statistics for Data Analysis and Visualization goes beyond the basics of SPSS Statistics to show you advanced techniques that exploit the full capabilities of SPSS. The authors explain when and why to use each technique, and then walk you through the execution with a pragmatic, nuts and bolts example. Coverage includes extensive, in-depth discussion of advanced statistical techniques, data visualization, predictive analytics, and SPSS programming, including automation and integration with other languages like R and Python. You'll learn the best methods to power through an analysis, with more efficient, elegant, and accurate code.

IBM SPSS Statistics is complex: true mastery requires a deep understanding of statistical theory, the user interface, and programming. Most users don't encounter all of the methods SPSS offers, leaving many little-known modules undiscovered. This book walks you through tools you may have never noticed, and shows you how they can be used to streamline your workflow and enable you to produce more accurate results.

Conduct a more efficient and accurate analysis Display complex relationships and create better visualizations Model complex interactions and master predictive analytics Integrate R and Python with SPSS Statistics for more efficient, more powerful code

These "hidden tools" can help you produce charts that simply wouldn't be possible any other way, and the support for other programming languages gives you better options for solving complex problems. If you're ready to take advantage of everything this powerful software package has to offer, SPSS Statistics for Data Analysis and Visualization is the expert-led training you need.

A New York Times bestseller

"Brilliant, funny…the best math teacher you never had." —San Francisco Chronicle

Once considered tedious, the field of statistics is rapidly evolving into a discipline Hal Varian, chief economist at Google, has actually called "sexy." From batting averages and political polls to game shows and medical research, the real-world application of statistics continues to grow by leaps and bounds. How can we catch schools that cheat on standardized tests? How does Netflix know which movies you’ll like? What is causing the rising incidence of autism? As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more.

For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.

And in Wheelan’s trademark style, there’s not a dull page in sight. You’ll encounter clever Schlitz Beer marketers leveraging basic probability, an International Sausage Festival illuminating the tenets of the central limit theorem, and a head-scratching choice from the famous game show Let’s Make a Deal—and you’ll come away with insights each time. With the wit, accessibility, and sheer fun that turned Naked Economics into a bestseller, Wheelan defies the odds yet again by bringing another essential, formerly unglamorous discipline to life.

A one-of-a-kind compilation of modern statistical methods designed to support and advance research across the social sciences

Statistics in the Social Sciences: Current Methodological Developments presents new and exciting statistical methodologies to help advance research and data analysis across the many disciplines in the social sciences. Quantitative methods in various subfields, from psychology to economics, are under demand for constant development and refinement. This volume features invited overview papers, as well as original research presented at the Sixth Annual Winemiller Conference: Methodological Developments of Statistics in the Social Sciences, an international meeting that focused on fostering collaboration among mathematical statisticians and social science researchers.

The book provides an accessible and insightful look at modern approaches to identifying and describing current, effective methodologies that ultimately add value to various fields of social science research. With contributions from leading international experts on the topic, the book features in-depth coverage of modern quantitative social sciences topics, including:

Correlation Structures

Structural Equation Models and Recent Extensions

Order-Constrained Proximity Matrix Representations

Multi-objective and Multi-dimensional Scaling

Differences in Bayesian and Non-Bayesian Inference

Bootstrap Test of Shape Invariance across Distributions

Statistical Software for the Social Sciences

Statistics in the Social Sciences: Current Methodological Developments is an excellent supplement for graduate courses on social science statistics in both statistics departments and quantitative social sciences programs. It is also a valuable reference for researchers and practitioners in the fields of psychology, sociology, economics, and market research.

USE EXCEL’S STATISTICAL TOOLS TO TRANSFORM YOUR DATA INTO KNOWLEDGE



Nationally recognized Excel expert Conrad Carlberg shows you how to use Excel 2016 to perform core statistical tasks every business professional, student, and researcher should master. Using real-world examples and downloadable workbooks, Carlberg helps you choose the right technique for each problem and get the most out of Excel’s statistical features. Along the way, he clarifies confusing statistical terminology and helps you avoid common mistakes.

You’ll learn how to use correlation and regression, analyze variance and covariance, and test statistical hypotheses using the normal, binomial, t, and F distributions. To help you make accurate inferences based on samples from a population, Carlberg offers insightful coverage of crucial topics ranging from experimental design to the statistical power of F tests. Updated for Excel 2016, this guide covers both modern consistency functions and legacy compatibility functions.


Becoming an expert with Excel statistics has never been easier! In this book, you’ll find crystal-clear instructions, insider insights, and complete step-by-step guidance.


Master Excel’s most useful descriptive and inferential statistical tools Understand how values cluster together or disperse, and how variables move or classify jointly Tell the truth with statistics—and recognize when others don’t Infer a population’s characteristics from a sample’s frequency distribution Explore correlation and regression to learn how variables move in tandem Use Excel consistency functions such as STDEV.S( ) and STDEV.P( ) Test differences between two means using z tests, t tests, and Excel’s Data Analysis Add-in Identify skewed distributions using Excel’s new built-in box-and-whisker plots and histograms Evaluate statistical power and control risk Explore how randomized block and split plot designs alter the derivation of F-ratios Use coded multiple regression analysis to perform ANOVA with unbalanced factorial designs Analyze covariance with ANCOVA, and properly use multiple covariance Take advantage of Recommended PivotTables, Quick Analysis, and other Excel 2016 shortcuts
`This book is highly recommended for libraries and departments to adopt. If I had to teach a statistics class for sociology students this would be a book I would surely choose. The book achieves two very important goals: it teaches students a software package and trains them in the statistical analysis of sociological data' - Journal of Applied Statistics

This fully revised, expanded and updated Second Edition of the best-selling textbook by Jane Fielding and Nigel Gilbert provides a comprehensive yet accessible guide to quantitative data analysis. Designed to help take the fear out of the use of numbers in social research, this textbook introduces students to statistics as a powerful means of revealing patterns in human behaviour.

The textbook covers everything typically included in an introductory course on social statistics for students in the social sciences and the authors have taken the opportunity of this Second Edition to bring the data sources as current as possible. The book is full of up-to-date examples and useful and clear illustrations using the latest SPSS software.

While maintaining the student-friendly elements of the first, such as chapter summaries, exercises at the end of each chapter, and a glossary of key terms, new features to this edition include:

- Updated examples and references

SPSS coverage and screen-shots now incorporate the current version 14.0 and are used to demonstrate the latest social statistics datasets;

- Additions to content include a brand new section on developing a coding frame and an additional discussion of weighting counts as a means of analyzing published statistics;

- Enhanced design aids navigation which is further simplified by the addition of core objectives for each chapter and bullet-pointed chapter summaries;

- The updated Website at http:/www.soc.surrey.ac.uk/uss/index.html reflects changes made to the text and provides updated datasets;

A valuable and practical guide for students dealing with the large amounts of data that are typically collected in social surveys, the Second Edition of Understanding Social Statistics is an essential textbook for courses on statistics and quantitative research across the social sciences.

Presents new models, methods, and techniques and considers important real-world applications in political science, sociology, economics, marketing, and finance

Emphasizing interdisciplinary coverage, Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian methodology and examines an array of topics in model formulation, estimation, and applications. The book presents recent and trending developments in a diverse, yet closely integrated, set of research topics within the social sciences and facilitates the transmission of new ideas and methodology across disciplines while maintaining manageability, coherence, and a clear focus.

Bayesian Inference in the Social Sciences features innovative methodology and novel applications in addition to new theoretical developments and modeling approaches, including the formulation and analysis of models with partial observability, sample selection, and incomplete data. Additional areas of inquiry include a Bayesian derivation of empirical likelihood and method of moment estimators, and the analysis of treatment effect models with endogeneity. The book emphasizes practical implementation, reviews and extends estimation algorithms, and examines innovative applications in a multitude of fields. Time series techniques and algorithms are discussed for stochastic volatility, dynamic factor, and time-varying parameter models. Additional features include:

Real-world applications and case studies that highlight asset pricing under fat-tailed distributions, price indifference modeling and market segmentation, analysis of dynamic networks, ethnic minorities and civil war, school choice effects, and business cycles and macroeconomic performance State-of-the-art computational tools and Markov chain Monte Carlo algorithms with related materials available via the book’s supplemental website Interdisciplinary coverage from well-known international scholars and practitioners
Bayesian Inference in the Social Sciences is an ideal reference for researchers in economics, political science, sociology, and business as well as an excellent resource for academic, government, and regulation agencies. The book is also useful for graduate-level courses in applied econometrics, statistics, mathematical modeling and simulation, numerical methods, computational analysis, and the social sciences.
`This book is to be commended, particularly, for putting the tool of statistics into the familiar context of a research study. In so doing it emphasizes the neglected pre-analysis stages of a research study. Indeed the performing of a data analysis, this book reminds us, should be the mere icing on an already well cooked cake' - Psychology Learning & Teaching

Doing Statistics With SPSS

is derived from the authors' many years of experience teaching undergraduates data handling using SPSS. It assumes no prior understanding beyond that of basic mathematical operations and is therefore suitable for anyone undertaking an introductory statistics course as part of a science based undergraduate programme. The text will: enable the reader to make informed choices about what statistical tests to employ; what assumptions are made in using a particular test; demonstrate how to execute the analysis using SPSS; and guide the reader in his/her interpretation of its output. Each chapter ends with an exercise and provides detailed instructions on how to run the analysis using SPSS release 10. Learning is further guided by pointing the reader to particular aspects of the SPSS output and by having the reader engage with specified items of information from the SPSS results.

This text is more complete than the alternatives that usually fall into one of two camps. They either provide an explanation of the concepts but no instructions on how to execute the analysis with SPSS, or they are a manual which instructs the reader on how to drive the software but with minimal explanation of what it all means. This book offers the best elements of both in a style that is economical and accessible.

Doing Statistics with SPSS will be essential reading for undergraduates in psychology and health-related disciplines, and likely to be of invaluable use to many other students in the social sciences taking a course in statistics.

HIGHLIGHTS THE USE OF BAYESIAN STATISTICS TO GAIN INSIGHTS FROM EMPIRICAL DATA

Featuring an accessible approach, Bayesian Methods for Management and Business: Pragmatic Solutions for Real Problems demonstrates how Bayesian statistics can help to provide insights into important issues facing business and management. The book draws on multidisciplinary applications and examples and utilizes the freely available software WinBUGS and R to illustrate the integration of Bayesian statistics within data-rich environments.

Computational issues are discussed and integrated with coverage of linear models, sensitivity analysis, Markov Chain Monte Carlo (MCMC), and model comparison. In addition, more advanced models including hierarchal models, generalized linear models, and latent variable models are presented to further bridge the theory and application in real-world usage.

Bayesian Methods for Management and Business: Pragmatic Solutions for Real Problems also features:

Numerous real-world examples drawn from multiple management disciplines such as strategy, international business, accounting, and information systems An incremental skill-building presentation based on analyzing data sets with widely applicable models of increasing complexity An accessible treatment of Bayesian statistics that is integrated with a broad range of business and management issues and problems A practical problem-solving approach to illustrate how Bayesian statistics can help to provide insight into important issues facing business and management
Bayesian Methods for Management and Business: Pragmatic Solutions for Real Problems is an important textbook for Bayesian statistics courses at the advanced MBA-level and also for business and management PhD candidates as a first course in methodology. In addition, the book is a useful resource for management scholars and practitioners as well as business academics and practitioners who seek to broaden their methodological skill sets.
In this book an attempt is made to evaluate the adequacy of common diagrammatical, cartographical and other graphical methods in order to represent, in a scientific manner, the various types of statistical data, of which graphical presentation may be sought.

In regard to many important types of data—such as contingency tables, correlation tables, other double-entry tables and various geographical-statistical distributions—common graphical methods seem to be inadequate. A new device—the Graphical Rational Patterns (GRP)—which appears to be of considerable help in graphical presentation of such data, is therefore, introduced. Single GRP enable the representation of any integer /?= 10 ? + a by a pattern formed by u unitary marks of area a (which indicate the units) and by t marks of area 10a (which indicate the tens). As these patterns require very little space, and are enclosed within small square frames, they allow extremely simple representation of any double-entry table. Such single patterns can be used too, to indicate on a geographical map, that a varue n is to be attached to a given area or a given line (isoline, line of traffic-flow, etc.). By the means of GRP, it is possible to solve rationally many problems connected with the construction of a geographical statistical map.

The use of GRP also allows rearrangement at will of the various elements of the graph without changing the entire graph: for instance, individual GRP, or columns or lines of GRP can be removed, and their order changed. The book illustrates possibilities of making alternative comparisons of data; of examining alternative hypotheses, or relationships between data represented; of discovering "lags" and "leads"; and following-up cohorts in the course of time, etc. GRP graphs may be prepared quickly by anyone, by the use of preprinted patterns prepared on adhesive paper.

Examples of applications have been taken from different fields: official, national and international statistics, demography, economics, sociology, geography, anthropology, meteorology, business administration, teaching of statistical methods, etc.

'This engagingly written and nicely opinionated book is a blend of friendly introduction and concisely applicable detail. No-one can recall every statistical formula, but if they have this book they will know where to look' - Professor Jon May, University of Plymouth

'This is one of the best books I have come across for teaching introductory statistics. The illustrative examples are engaging and often humorous and the explanations of 'difficult' concepts are written in a wonderfully clear and intuitive way' - Nick Allum, University of Essex

Selected as an Outstanding Academic Title by Choice Magazine, January 2010

First (and Second) Steps in Statistics, Second Edition provides a clear and concise introduction to the main statistical procedures used in the social and behavioural sciences and is perfect for the statistics student starting their journey.

The rationale and procedure for analyzing data are presented through exciting examples with an emphasis on understanding rather than computation. It is ideally suited for introductory courses in statistics given its gentle beginning, yet progressive treatment of topics. In addition to descriptive statistics, graphs, t-tests, oneway ANOVAs, Chi-square, and simple linear regression, this Second Edition now includes some new, more advanced topic areas as well as a host of additional examples to help students confidently progress through their studies and apply the techniques in lab work, reports and research projects.

Key features of this new edition:

- the reoganization of the first three chapters giving more attention to univariate statistics and providing more examples to work through at this level

- more advanced 'second step' content has been added on factorial ANOVA and multiple regression

- the robust methods chapter from the first edition is now spread throughout the book, and is linked with common teaching practices.

- many more examples have been added to enhance the book's practical potential.

- a host of exercises as well as further reading sections at the end of every chapter.

An accompanying Web page includes information for each chapter using the statistical packages SPSS and R.

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