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.
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.
In this refreshing book, experienced author and academic Neil Burdess shows that statistics are not the result of some mysterious "black magic", but rather the result of some very basic arithmetic. Getting rid of confusing x's and y's, he shows that it's the intellectual questions that come before and after the calculations that are important: (i) What are the best statistics to use with your data? and (ii) What do the calculated statistics tell you?
Statistics: A Short, Clear Guide aims to help students make sense of the logic of statistics and to decide how best to use statistics to analyse their own data. What's more, it is not reliant on students having access to any particular kind of statistical software package.
This is a very useful book for any student in the social sciences doing a statistics course or needing to do statistics for themselves for the first time.
The book is pedagogically well developed and contains many screen dumps and exercises, glossary terms and worked examples. Divided into two parts, Applied Statistics Using SPSS covers :
1. A self-study guide for learning how to use SPSS.
2. A reference guide for selecting the appropriate statistical technique and a stepwise do-it-yourself guide for analysing data and interpreting the results.
3. Readers of the book can download the SPSS data file that is used for most of the examples throughout the book here.
Geared explicitly for undergraduate needs, this is an easy to follow SPSS book that should provide a step-by-step guide to research design and data analysis using SPSS.
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.
It introduces the intuitive thinking behind standard procedures, explores the process of informal reasoning, and uses conceptual frameworks to provide a foundation for students new to statistics. It showcases the expertise we have all developed from living in a data saturated society, increases our statistical literacy and gives us the tools needed to approach statistical mathematics with confidence.
Key topics include:Variability Standard Distributions Correlation Relationship Sampling Inference
An engaging, informal introduction this book sets out the conceptual tools required by anyone undertaking statistical procedures for the first time or for anyone needing a fresh perspective whilst studying the work of others.
The second edition has been revised throughout and includes many new examples. A new companion website, garnerjoyofstats.com, features a data set covering close to 120 countries and 10 variables, student exercises, and a full suite of instructor support materials, including power points for lectures, lab guides, and a test bank.
For more information visit www.garnerjoyofstats.com.
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:
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.
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.
An accessible introduction to statistics in the criminal justice field.
Elementary Statistics in Criminal Justice Research, Fourth Edition, provides an introduction to statistics for students in criminal justice and criminology. Created specifically for students who many not have strong backgrounds in mathematics, the text focuses primarily on the statistical theories and methods that criminal justice students need to understand. This text was adapted from the best-selling Elementary Statistics in Social Research, and provides broad and accessible coverage that will appeal to students and instructors alike.
Built upon a variety of engaging examples from across the social sciences it provides a rich collection of statistical methods and models. Students are encouraged to see the impact of theory whilst simultaneously learning how to manipulate software to meet their needs.
The book also provides:Original case studies and data sets Practical guidance on how to run and test models in Stata Downloadable Stata programmes created to work alongside chapters A wide range of detailed applications using Stata Step-by-step notes on writing the relevant code.
This excellent text will give anyone doing statistical research in the social sciences the theoretical, technical and applied knowledge needed to succeed.
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.
- Comprehensive guide informing how to use a range of advanced statistical methods such as MANOVA, path analysis and logistical regression;
- Inter-disciplinary: ideal for students studying upper level statistical methods in any subject across the social sciences;
- Practical guide: case studies, further reading, key terms explained in order to help the non-mathematically orientated student get ahead with their research.
Building on undergraduate statistical grounding, Understanding and Using Advanced Statistics provides the upper-level researcher with the knowledge of what advanced statistics do, how they should be used, and what their output means.
Beginning with an explanation of the differences between deterministic and probabilistic models, Brown then introduces the reader to chaotic dynamics. Other topics covered are finding settings in which chaos can be measured, estimating chaos using nonlinear least squares and specifying catastrophe models. Finally a nonlinear system of equations that models catastrophe using real survey data is estimated.
The SAGE Dictionary of Statisticsprovides students and researchers with an accessible and definitive resource to use when studying statistics in the social sciences, reading research reports and undertaking data analysis. Written by leading academics in the field of methodology and statistics, the Dictionary will be an essential study guide for the first-time researcher as well as a primary resource for more advanced study.
This is a practical and concise dictionary that serves the everyday uses of statistics across the whole range of social science disciplines. It offers basic and straightforward definitions of key concepts, followed by more detailed step-by-step explanations of situating specific methods and techniques. It also contains lists of related concepts to help the user to draw connections across various fields and increase their overall understand of a specific technique. A list of key readings helps to reinforce the aim of the Dictionary as an invaluable learning resource.
Designed specifically for students and those new to research, and written in a lively and engaging manner, this Dictionary is an essential reference work for students and researchers across the social sciences.
Doing Statistics With SPSSis 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.
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:
Key FeaturesReaders learn how to construct geometric spaces from relevant data, formulate questions of interest, and link statistical interpretation to geometric representations.They also learn how to perform structured data analysis and to draw inferential conclusions from MCA.The text uses real examples to help explain concepts.The authors stress the distinctive capacity of MCA to handle full-scale research studies.
This supplementary text is appropriate for any graduate-level, intermediate, or advanced statistics course across the social and behavioral sciences, as well as for individual researchers.
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In regard to many important types of dataâsuch as contingency tables, correlation tables, other double-entry tables and various geographical-statistical distributionsâcommon graphical methods seem to be inadequate. A new deviceâthe Graphical Rational Patterns (GRP)âwhich 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.