Data Analysis with SPSS: A First Course in Applied Statistics, 4th edition

Published by Pearson (December 27, 2010) © 2012

  • Stephen A. Sweet Ithaca College
  • Karen A. Grace-Martin Cornell University
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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:

1. BRIEF

2. COMPREHENSIVE

 

BRIEF TABLE OF CONTENTS

 

Chapter 1  •   Key Concepts in Social Science Research        

Chapter 2  •   Getting Started: Accessing, Examining, and Saving  

Chapter 3   •  Univariate Analysis: Descriptive Statistics         

Chapter 4   • Constructing Variables     

Chapter 5   •  Assessing Association through Bivariate Analysis          

Chapter 6   •  Comparing Group Means through Bivariate Analysis            

Chapter 7   •  Modeling Relationships of Multiple Variables with Linear Regression                                 

Chapter 8   •  Logistic Regression        

Chapter 9   •  Writing a Research Report         

Chapter 10 •  Research Projects                      

                           

COMPREHENSIVE TABLE OF CONTENTS 

 

Chapter 1  • Key Concepts in Social Science Research      

            Overview         

            Framing Topics Into Research Questions           

            Theories and Hypotheses            

            Population and Samples                        

            Relationships and Causality        

            Data Sets                                      

            Parts of a Data Set                        

            Reliability and Validity                 

            Summary         

            Key Terms       

            Exercises            

 

Chapter 2 •  Getting Started: Accessing, Examining, and Saving Data                      

            Overview                

            The Layout of SPSS     

            Types of Variables       

            Initial Settings                           

            Defining and Saving a New Data Set                 

            Managing Data Sets: Dropping and Adding Variables, Merging Data Sets

            Dropping and Adding Variables                         

            Merging and Importing Files                                  

            Loading and Examining an Existing File     

            Summary         

            Key Terms            

            Exercises            

 

Chapter 3  •  Univariate Analysis: Descriptive Statistics        

            Overview           

            Why Do Researchers Perform Univariate Analysis?                   

            Exploring Distributions of Scale Variables                                      

            Exploring Distributions of Categorical Variables               

            Summary         

            Key Terms      

            Exercises          

 

Chapter 4  • Constructing Variables     

            Overview         

            Why Construct New Variables From Existing Data?      

            Recoding Existing Variables                  

            Computing New Variables                    

            Recording Computations Using Syntax   

            Minimizing Missing Values in Computing New Variables       

            Summary         

            Key Terms       

            Exercises         

 

Chapter 5  •  Assessing Association through Bivariate Analysis          

            Overview         

            Why Do We Need Significance Tests?            

            Analyzing Bivariate Relationships Between Two Categorical Variables   

            Analyzing Bivariate Relationships Between Two Scale Variables            

            Summary            

            Key Terms      

            Exercises         

 

Chapter 6  •  Comparing Group Means through Bivariate Analysis            

            Overview            

            One-Way Analysis of Variance    

            Post-hoc Tests                            

            Assumptions of ANOVA             

            Graphing the Results of ANOVA          

            T tests              

            Summary         

            Key Terms      

            Exercises         

 

Chapter 7  •  Modeling Relationships of Multiple Variables with Linear Regression                          

            Overview          

            The Advantages of Modeling Relationships in Multiple Regression           

            Linear Regression: A Bivariate Example                                      

            Multiple Linear Regression        

            Other Concerns In Applying Linear Regression    

            Building Multiple Variable Models         

            Summary              

            Key Terms       

            Exercises             

 

Chapter 8  •  Logistic Regression        

            Overview            

            What Is Logistic Regression?                

            When Can I Use a Logistic Regression?             

            Understanding  Relationships through Probabilities          

            Logistic Regression: A Bivariate Example                         

            Multiple Variable Logistic Regression: An Example           

            Summary             

            Key Terms          

            Exercises           

 

Chapter 9  •  Writing a Research Report         

            Overview         

            Writing Style and Audience                      

            The Structure of a Report                        

            Summary             

            Key Terms           

            Exercises         

           

Chapter 10  •  Research Projects                      

            Potential Research Projects                      

            Research Project 1: Racism                      

            Research Project 2: Suicide                       

            Research Project 3: Criminality                 

            Research Project 4: Welfare and Other Public Aid Consumption                  

            Research Project 5: Sexual Behavior          

            Research Project 6: Education                 

            Research Project 7: Health                   

            Research Project 8: Happiness              

            Research Project 9: Your Topic                            

 

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