Data Analysis with SPSS is designed to teach students how to explore data in a systematic manner using the most popular professional social statistics program on the market today.   Written in ten manageable chapters, this book first introduces students to the approach researchers use to frame research questions and the logic of establishing causal relations. Students are then oriented to the SPSS program and how to examine data sets. Subsequent chapters guide them through univariate analysis, bivariate analysis, graphic analysis, and multivariate analysis. Students conclude their course by learning how to write a research report and by engaging in their own research project.   Each book is packaged with a disk containing the GSS (General Social Survey) file and the States data files. The GSS file contains 100 variables generated from interviews with 2,900 people, concerning their behaviors and attitudes on a wide variety of issues such as abortion, religion, prejudice, sexuality, and politics. The States data allows comparison of all 50 states with 400 variables indicating issues such as unemployment, environment, criminality, population, and education. Students will ultimately use these data to conduct their own independent research project with SPSS.

Table of Contents:
Brief Chapter 1: Key Concepts in Social Science Research   Chapter 2: Getting Started: Accessing, Examining, and Saving Data  Chapter 3: Univariate Analysis: Descriptive Statistics Chapter 4: Constructing Variables  Chapter 5: Assessing Association through Bivariate Analysis   Chapter 6: Comparing Groups through Bivariate Analysis   Chapter 7: Multivariate Analysis with Linear Regression    Chapter 8  Multivariate Analysis with Logistic Regression Chapter 9: Writing a Research Report   Chapter 10: Research Projects    Comprehensive     *Each chapter begins with “Overview,” and concludes with “Summary,” “Key Terms", and “Exercises.”   Chapter1: Key Concepts in Social Science Research                 Why Do We Need Statistics                 Framing Topics Into Research Questions                 Theory and Hypothesis                  Population and Samples             Relationships and Causality                      Data                         Chapter 2: Getting Started: Accessing, Examining,and Saving Data                     Initial Settings                 The Layout of SPSS             Types of Variables                 Defining and Saving a New Data Set                 Managing Data Sets: Dropping and Adding Variables                 Merging and Importing Files                 Loading and Examining an Existing File                 Chapter 3: Univariate Analysis: Descriptive Statistics                 Why Do Researchers Perform Univariate Analysis?                 Exploring Distributions of Scale Variables                 Exploring Distributions of Categorical Variables                  Chapter 4: Constructing Variables                 Why Construct New Variables?                 Recoding Existing Variables                 Computing New Variables                 Recording Computations Using Syntax       Chapter 5: Assessing Association through Bivariate Analysis                    Why Do We Need Significance Tests?                 Cross Tabulations                 Bar Charts                 Correlations              Scatter Plots      Chapter 6: Comparing Groups through Bivariate Analysis                   One-Way Analysis of Variance                 Post-hoc Tests                 Assumptions of ANOVA                   Graphing the Results of ANOVA             T tests                             Chapter 7: Multivariate Analysis with Linear Regression                 The Advantages of Multivariate Analysis                 Linear Regression: A Bivariate Example                 Multiple Linear Regression                 Other Concerns In Applying Linear Regression                 Assumptions of Regression             Dummy Variables                 Outliers                 Causality       Chapter 8: Multivariate Analysis with Logistic Regression                     What Is Logistic Regression?                 When Can I Do a Logistic Regression?                 Understanding the Relationships through Probabilities                 Logistic Regression: A Bivariate Example                 Multivariate Logistic Regression: An Example                 Interpreting Logistic Regression Output                 Using Multivariate Logistic Regression Coefficients to Make Predictions             Using Multivariate Coefficients to Graph a Logistic Regression Line       Chapter 9: Writing a Research Report                 Overview                 Writing Style and Audience                 The Structure of a Report                 References and Further Reading                 Chapter 10: Research Projects                 Potential Research Projects                 Research Project 1: Racism                 Research Project 2: Suicide                 Research Project 3: Criminality                 Research Project 4: Welfare                  Research Project 5: Sexual Behavior                  Research Project 6: Education                 Research Project 7: Health             Research Project 8: Happiness             Research Project 9: Your Topic                  Appendix 1:  STATES.SAV Descriptives       Appendix 2:  GSS98.SAV File Information        Appendix 3:  Variable Label Abbreviations     Permissions      Index