Social Sciences Data & Statistics Resources

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.

Enables readers to start doing actual data analysis fast for a truly hands-on learning experience This concise and very easy-to-use primer introduces readers to a host of computational tools useful for making sense out of data, whether that data come from the social, behavioral, or natural sciences. The book places great emphasis on both data analysis and drawing conclusions from empirical observations. It also provides formulas where needed in many places, while always remaining focused on concepts rather than mathematical abstraction. SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics offers a variety of popular statistical analyses and data management tasks using SPSS that readers can immediately apply as needed for their own research, and emphasizes many helpful computational tools used in the discovery of empirical patterns. The book begins with a review of essential statistical principles before introducing readers to SPSS. The book then goes on to offer chapters on: Exploratory Data Analysis, Basic Statistics, and Visual Displays; Data Management in SPSS; Inferential Tests on Correlations, Counts, and Means; Power Analysis and Estimating Sample Size; Analysis of Variance - Fixed and Random Effects; Repeated Measures ANOVA; Simple and Multiple Linear Regression; Logistic Regression; Multivariate Analysis of Variance (MANOVA) and Discriminant Analysis; Principal Components Analysis; Exploratory Factor Analysis; and Non-Parametric Tests. This helpful resource allows readers to: Understand data analysis in practice rather than delving too deeply into abstract mathematical concepts Make use of computational tools used by data analysis professionals. Focus on real-world application to apply concepts from the book to actual research Assuming only minimal, prior knowledge of statistics, SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics is an excellent "how-to" book for undergraduate and graduate students alike. This book is also a welcome resource for researchers and professionals who require a quick, go-to source for performing essential statistical analyses and data management tasks.

An Introduction to Statistics and Data Analysis Using Stata®: From Research Design to Final Report provides a step-by-step introduction for statistics, data analysis, or research methods classes using Stata software. Concise descriptions emphasize the concepts behind statistics rather than the derivations of the formulas. With real-world examples from a variety of disciplines and extensive detail on the commands in Stata, this text provides an integrated approach to statistical analysis, research design, and report writing for social science students.

The R language is widely used by statisticians for data analysis, and the popularity of R programming has therefore increased substantially in recent years. The emerging Internet of Things (IoT) gathers increasing amounts of data that can be analyzed to gain useful insights into trends. R for Data Analysis in easy steps has an easy-to-follow style that will appeal to anyone who wants to produce graphic visualizations to gain insights from gathered data. R for Data Analysis in easy steps begins by explaining core programming principles of the R programming language, which stores data in "vectors" from which simple graphs can be plotted. Next, the book describes how to create "matrices" to store and manipulate data from which graphs can be plotted to provide better insights. This book then demonstrates how to create "data frames" from imported data sets, and how to employ the "Grammar of Graphics" to produce advanced visualizations that can best illustrate useful insights from your data. R for Data Analysis in easy steps contains separate chapters on the major features of the R programming language. There are complete example programs that demonstrate how to create Line graphs, Bar charts, Histograms, Scatter graphs, Box plots, and more. The code for each R script is listed, together with screenshots that illustrate the actual output when that script has been executed. The free, downloadable example R code is provided for clearer understanding. By the end of this book you will have gained a sound understanding of R programming, and be able to write your own scripts that can be executed to produce graphic visualizations for data analysis. You need have no previous knowledge of any programming language, so it's ideal for the newcomer to computer programming.

R Programming Fundamentals, focused on R and the R ecosystem, introduces you to the tools for working with data. You’ll start by understanding how to set up R and RStudio, followed by exploring R packages, functions, data structures, control flow, and loops. Once you have grasped the basics, you’ll move on to studying data visualization and graphics. You’ll learn how to build statistical and advanced plots using the powerful ggplot2 library.

Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you'll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language. Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It's ideal for analysts new to Python and for Python programmers new to scientific computing. Use the IPython interactive shell as your primary development environment Learn basic and advanced NumPy (Numerical Python) features Get started with data analysis tools in the pandas library Use high-performance tools to load, clean, transform, merge, and reshape data Create scatter plots and static or interactive visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Measure data by points in time, whether it's specific instances, fixed periods, or intervals Learn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples

Turn jumbles of numbers into graphics, insights, and answers with Excel With Microsoft Excel, you can, well, excel at data analysis. And Excel Data Analysis For Dummies can help, with clear and easy explanations of the mountain of features for creating, visualizing, and analyzing data. PivotTables, charts, what-if analysis, statistical functions--it's all in here, with examples and ideas for Excel users of all skill levels. This latest edition covers the most recent updates to Excel and Microsoft 365. You'll beef up your data skills and learn powerful techniques for turning numbers into knowledge. For students, researchers, and business professionals, Excel is the spreadsheet and data application of choice--and Dummies is the best choice for learning how to make those numbers sing. Learn how to use Excel's built-in data analysis features and write your own functions to explore patterns in your data Create striking charts and visualizations, and discover multiple ways to tell the stories hidden in the numbers Clean up large datasets and identify statistical operations that will answer your questions Perform financial calculations, database operations, and more--without leaving Excel Excel Data Analysis For Dummies is the go-to resource for Excel users who are looking for better ways to crunch the numbers.