For courses in Statistics and Research Methods.
Student Friendly, Step-by-Step Approach. Designed to be hands-on with the user performing the analyses alongside on their computer as they read through each chapter.
Without question, statistics is one of the most challenging courses for students in the social and behavioral sciences. Enrolling in their first statistics course, students are often apprehensive or extremely anxious toward the subject matter. And while SPSS is one of the more easy-to-use statistical software programs available, for anxious students who realize they not only have to learn statistics but also new software, the task can seem insurmountable. Keenly aware of students’ anxiety with statistics (and the fact that this anxiety can affect performance), Ronald Yockey has written SPSS Demystified: A Step-by -Step Guide to Successful Data Analysis, 2/e. Through a comprehensive, step-by-step approach, this text is consistently and specifically designed to both alleviate anxiety toward the subject matter and build a successful experience analyzing data in SPSS.
Datasets available for download: www.pearsonhighered.com/yockey
Chapter 1: Introduction
Starting SPSS
Data Editor Window
Variable View Window
Data View Window
Viewer (Output) window
Saving files
Printing files
Chapter Exercises
Chapter 2: Descriptive Statistics
Frequencies
Measures of Central Tendency
Analysis of Groups Using the Compare Means Procedure
Chapter Exercises
Chapter 3: Graphical Procedures
Bar Charts
Histograms
Scatterplots
Boxplots
Chapter Exercises
Chapter 4: Reliability Analysis
Example
Research Question
Data Entry and Analysis in SPSS
Chapter Exercises
Chapter 5: One Sample t Test
Example
Hypotheses and Research Question
Data Entry and Analysis in SPSS
Assumptions of the One Sample t Test
Chapter Exercises
Chapter 6: Independent Samples t Test
Example
Hypotheses and Research Question
Data Entry and Analysis in SPSS
Assumptions of the Independent Samples t Test
Chapter Exercises
Chapter 7: Dependent Samples t Test
Example
Hypotheses and Research Question
Data Entry and Analysis in SPSS
Assumptions of the Dependent Samples t Test
Chapter Exercises
Chapter 8: One–Way Between Subjects ANOVA
Example
Hypotheses and Research Question
Data Entry and Analysis in SPSS
Assumptions of the One–Way Between Subjects ANOVA
Chapter Exercises
Chapter 9: Two–Way Between Subjects ANOVA
Example
Hypotheses and Research Questions
Data Entry and Analysis in SPSS
Assumptions of the Two–Way Between Subjects ANOVA
Chapter Exercises
Chapter 10: One–Way Within Subjects ANOVA
Example
Hypotheses and Research Question
Data Entry and Analysis in SPSS
Assumptions of the One–Way Within Subjects ANOVA
Chapter Exercises
Chapter 11: One–Between One–Within Subjects ANOVA
Example
Hypotheses and Research Questions
Data Entry and Analysis in SPSS
Assumptions of the One–Between One–Within Subjects ANOVA
Chapter Exercises
Chapter 12: Correlation
Example
Hypotheses and Research Question
Data Entry and Analysis in SPSS
Assumptions of Correlation
Chapter Exercises
Chapter 13: Simple Regression
Example
Hypotheses and Research Question
Data Entry and Analysis in SPSS
Assumptions of Simple Regression
Chapter Exercises
Chapter 14: Multiple Regression
Example
Hypotheses and Research Questions
Data Entry and Analysis in SPSS
Assumptions of Multiple Regression
Chapter Exercises
Chapter 15: Chi–Square Goodness of Fit Test
Example
Hypotheses and Research Question
Data Entry and Analysis in SPSS
Assumptions of the Chi–Square Goodness of Fit Test
Chapter Exercises
Chapter 16: Chi–Square Test of Independence
Example
Hypotheses and Research Question
Data Entry and Analysis in SPSS
Assumptions of Chi–Square Test of Independence
Chapter Exercises
Appendix A: Data Transformations and Other Procedures
Appendix B: Importing Files
Appendix C: Solutions to Chapter Exercises
References
Student Friendly, Step-by-Step Approach.
· Designed to be hands-on with the user performing the analyses alongside on their computer as they read through each chapter. To help the reader stay on-track as they perform the analyses, a step-by-step approach is used, beginning with creating the variables in SPSS and ending with writing the results in the format of the American Psychological Association.
o Screen shots of each step in SPSS are also included, and call-out boxes are used to highlight important information in the results.
· Four step process of data analysis. In each chapter, the process of data analysis is divided into four easy–to–follow steps, including:
o Step 1: Create the variables – Variables are created, including entering value labels as necessary.
o Step 2: Enter the data – The correct structuring of the data file is illustrated.
o Step 3: Analyze the data – How to run the correct analysis using the pull–down menus in SPSS is illustrated.
o Step 4: Interpret the results – Each table of output is discussed, one table at a time, with sample write–up of the results in APA format provided for chapters 5 – 16.
· Screen shots. Several screen shots are included in each chapter, helping the reader stay on–track as they progress through each chapter.
· Call—out boxes. Call-out boxes are used to highlight important information and to alert the reader to areas of potential confusion in SPSS (e.g., what to enter in the Test Value box for the one–sample t test).
· Research question and null and alternative hypotheses. Research question(s) and the null and alternative hypotheses for each procedure are presented to help better connect the data to the research question and hypotheses of interest (applies to chapters 5–16).
· Effect sizes. How to calculate, report, and interpret effect sizes is presented (the reporting of effect sizes is recommended by the APA and is required by several journals for manuscript submission; applies to chapters 5–16).
· Assumptions. The assumptions of each inferential procedure are provided, along with the impact of violating the assumption on the accuracy of the procedure, allowing the reader to assess whether their data meet the requirements for a statistical procedure of interest (applies to chapters 5–16).
· Exercises are included at the end of each chapter
Broad Coverage.
· Designed primarily for students in introductory statistics and research methods courses and for those analyzing data for a research study.
o To this end, procedures that are most commonly covered in these courses are presented. Examples include:
Structured Organization.
Divided into two sections.
· Section 1 introduces:
o SPSS software program (Chapter 1).
o Covers descriptive statistics (Chapter 2).
o Discusses how to use SPSS to produce a variety of graphs (Chapter 3).
o Concludes with a chapter on estimating the internal consistency reliability of a scale using Coefficient Alpha (Chapter 4).
· Section 2 covers inferential statistics, including:
o t tests (Chapters 5–7),
o Analysis of Variance procedures (Chapters 8–11)
o Correlation (Chapter 12)
o Simple and Multiple Regression (Chapters 13–14)
o Chi–square procedures (Chapters 15–16).
· Data transformations and importing Excel files into SPSS are covered in Appendices A and B.
Datasets available for download: www.pearsonhighered.com/yockey