[Ebook PDF] Using SPSS for Windows and Macintosh, 8th Edition

• Delivery: Can be download Immediately after purchasing
• Version: Only PDF Version.
• Compatible Devices: Can be read on any devices (Kindle, NOOK, Android/IOS devices, Windows, MAC)
• Quality: High Quality. No missing contents. Printable

_____________________________________________________________

Using SPSS for Windows and Macintosh, 8th Edition

ISBN-13: 978-0134319889
ISBN-10: 0134319885

Author: Samuel B. Green (Author), Neil J. Salkind (Author)

This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book.

For courses in Political and Social Statistics

Using the popular SPSS software to de-mystify statistics

Using SPSS for Windows and Macintosh guides students through basic SPSS techniques, using step-by-step descriptions and explaining in detail how to avoid common pitfalls in the study of statistics. Authors Samuel Green and Neil Salkind provide extensive substantive information about each statistical technique, including a brief discussion of the technique, examples of how the statistic is applied, a sample data set that can be analyzed with the statistic, a discussion of the analysis results, practice exercises, and more. The Eighth Edition has been updated for SPSS version 23 (Windows / Mac), and now offers added accessibility and increased problem solving.

PREFACE
It’s our pleasure to be part of the eighth edition of Using SPSS for Windows and Macintosh: Analyzing and Understanding Data. Our objective has been to make each revision of our book more accessible and readable, so that readers can properly conduct statistical analyses with SPSS and make appropriate interpretations of the obtained results.
The development of easy-to-use statistical software like SPSS has changed the way statistics is being taught and learned. No longer do students have to learn a system of elaborate code to conduct simple or complex analyses. Instead, students simply enter their data into the easyto-use Data Editor. They can then select items from a drop-down menu to make appropriate transformations of variables, click options from another menu to create graphs of distributions of variables, select among various statistical analyses by clicking on appropriate options, and more. With a minimal amount of time and effort, the output is
displayed, showing the results. Researchers also have benefited from applications like SPSS. They do not have to spend time reacquainting themselves with the ins and outs of a statistical software package
or learning new programs for conducting analyses that take hours to master. They also do not have to teach assistants how to write code to produce analyses or examine and reexamine code that has reduced error messages that do not really indicate what is wrong. Everyone can just point and click. More sophisticated users can use the syntax features.
In general, programs like SPSS have made life easier for students who are learning statistics, for teachers who are teaching statistics, and for researchers who are applying statistics. Nevertheless, many users of these programs find “doing statistics” an arduous, unenjoyable task. They still are faced with many potential obstacles, and they feel overwhelmed and stressed rather than challenged and excited about the potential for mastering these important skills. What are some of the obstacles that students, in particular, face when they are trying to conduct statistical analyses with SPSS?
• Obstacle 1: Although SPSS is easy to use, many students and first-time users find it very complex. They have to learn how to input data into the Data Editor, save and retrieve data, make transformations to data, conduct analyses, manipulate output, create graphs, edit graphs, and so on.
• Obstacle 2: Students can feel helpless. Although they know how to point and click, they are frequently confronted with new dialog boxes with many decisions to make. Their instructor does not have the time to talk about each of the options, so students feel as if they are making uninformed decisions.
• Obstacle 3: The amount of output and numbers produced by any statistical procedure is enough to cower most researchers if they are forced to explain their meaning. How can students who are taking statistics for the first time feel confident about interpreting output from an SPSS procedure? In trying to understand output, they are likely to face language problems. For example, “What is a significant F value? Is it the same as the p value that the instructor is talking about? No, it couldn’t be, or she or he would have told us.” Researchers, graduate students, and more advanced undergraduate students are going to face additional obstacles.
• Obstacle 4: Users can think of a number of different ways to analyze their data, but they are unsure about which way would yield the most understanding of their results and not violate the assumptions underlying the analyses.
• Obstacle 5: Even if users make all good decisions about statistical approaches and understand the output, they still must write a Results section that conforms to the American Psychological Association (APA) format. Using SPSS for Windows and Macintosh: Analyzing and Understanding Data for Version 23 of SPSS helps readers overcome all of the obstacles discussed earlier. The book is divided into 10 units, which are as follows:
Units 1 to 4 guide students through the most basic of SPSS techniques and use a step-by-step description to master such techniques. Unit 1, “Getting Started with SPSS,” shows the student how to get started using SPSS, including a survey of the main menus, a description of how to use SPSS Help, and a brief tour of what SPSS can do.
Unit 2, “Creating and Working with Data Files,” goes through the steps of defining variables, showing how data are entered and edited, how to use the Data Editor and the data view screens, how to print SPSS data files, and how to import and export information to and from SPSS.
Unit 3, “Working with Data,” describes how to find and replace data, recode and compute values, sort data, and merge and split files.
Unit 4, “Working with SPSS Graphs and Output for Windows,” teaches the student how to create and enhance SPSS charts as well as how to work with SPSS output including pivot tables. SPSS Windows (version 23) and Macintosh (version 23) differ in the way that graphics are created and edited, and, thus, there is a separate section covering each—Lesson 16A for Windows and Lesson
16B for the Macintosh. SPSS is becoming increasingly cross-platform, and if you know the Windows version, you can easily adapt to the Macintosh version (and vice versa).
Each unit from 5 through 10 presents a set of statistical techniques and a step-by-step description of how to conduct the statistical analyses. This is not, however, a “cookbook” format. We provide extensive substantive information about each statistical technique, including a brief discussion of the statistical technique under consideration, examples of how the statistic is applied, the assumptions underlying the statistic, a description of the effect size for the statistic, a sample data set that can be analyzed with the statistic, the research question associated with the data set, step-by-step instructions for how to complete the analysis using the sample data set, a discussion of the results of the analysis, a visual display of the results using SPSS graphic options, a Results section describing the results in APA format, alternative analytical techniques (when available), and practice exercises.
Unit 5, “Creating Variables and Computing Descriptive Statistics,” shows how to create new variables from existing ones and discusses the basic procedures for describing qualitative and quantitative variables.
Unit 6, “t Test Procedures,” focuses on comparing means and shows how to use a variety of techniques, including independent and dependent t tests and the onesample t test.
Unit 7, “Univariate and Multivariate Analysis-ofVariance Techniques,” focuses on the family of analysisof-variance techniques, including one-way and two-way analyses of variance, analysis of covariance, and multivariate analysis of variance.
Unit 8, “Correlation, Regression, and Discriminant Analysis Procedures,” includes simple techniques such as bivariate correlational analysis and bivariate regression analysis, as well as more complex analyses such as partial correlational analysis, multiple linear regression, and discriminant analysis.
Unit 9, “Scaling Procedures,” focuses on factor analysis, reliability estimation, and item analysis.
Unit 10, “Nonparametric Procedures,” discusses a variety of nonparametric techniques, including such tests as the binomial, one-sample chi-square, Kruskal-Wallis, McNemar, Friedman, and Cochran tests.