IBM SPSS Essentials
John Wiley & Sons Inc (Verlag)
978-1-119-41742-2 (ISBN)
The newly and thoroughly revised Second Edition of SPSS Essentials delivers a comprehensive guide for students in the social sciences who wish to learn how to use the Statistical Package for the Social Sciences (SPSS) for the effective collection, management, and analysis of data. The accomplished researchers and authors provide readers with the practical nuts and bolts of SPSS usage and data entry, with a particular emphasis on managing and manipulating data.
The book offers an introduction to SPSS, how to navigate it, and a discussion of how to understand the data the reader is working with. It also covers inferential statistics, including topics like hypothesis testing, one-sample Z-testing, T-testing, ANOVAs, correlations, and regression. Five unique appendices round out the text, providing readers with discussions of dealing with real-world data, troubleshooting, advanced data manipulations, and new workbook activities.
SPSS Essentials offers a wide variety of features, including:
A revised chapter order, designed to match the pacing and content of typical undergraduate statistics classes
An explanation of when particular inferential statistics are appropriate for use, given the nature of the data being worked with
Additional material on understanding your data sample, including discussions of SPSS output and how to find the most relevant information
A companion website offering additional problem sets, complete with answers
Perfect for undergraduate students of the social sciences who are just getting started with SPSS, SPSS Essentials also belongs on the bookshelves of advanced placement high school students and practitioners in social science who want to brush up on the fundamentals of this powerful and flexible software package.
JOHN T. KULAS, PhD, is a Professor of Industrial and Organizational Psychology at Montclair State University in Montclair, NJ, United States. RENATA GARCIA PRIETO PALACIOS ROJI, MA, is a PhD candidate in Industrial and Organizational Psychology at Montclair State University in Montclair, NJ, United States. ADAM M. SMITH, PhD, is an associate consultant at Kincentric and adjunct instructor at Wentworth Institute of Technology and Harvard University.
Preface xiii
Acknowledgments xvii
Author Biography xix
Part I Introduction 1
1 What is SPSS? 3
Chapter Learning Objectives 3
What Is SPSS Used For 4
The Power of SPSS 5
SPSS Compared to Other Programs 5
Summary 6
Key Terms 6
Discussion Questions 6
2 Navigating SPSS 7
Chapter Learning Objectives 7
How the Program Works 7
Important File Types 8
Data Files 8
Syntax Files 10
Output Files 10
The “Others” 11
Managing Your SPSS Life 11
The Importance of Maintaining the Raw Data as an “Untouched” File 12
Summary 13
Key Terms 13
Discussion Questions 13
3 Introduction to Data 15
Chapter Learning Objectives 15
Understanding Your Data 16
Independent Versus Dependent Variables 16
Scales of Measurement 16
The SPSS Data Perspective 17
Data Represented by Numbers (Numeric) 17
Data Represented by Words (String) 18
The Other Variable Types 18
Your Data in SPSS – Think Matrices 19
Summary 20
Key Terms 21
Discussion Questions 21
4 Getting Your Data into SPSS 23
Chapter Learning Objectives 23
Before SPSS 24
Specifying Operations Through SPSS 25
Creating a Data Shell 25
Creating Data Files Via Syntax 28
Numeric Versus String Variables 29
Data Entry Within the Syntax File 31
“Saving” Populated Datafiles 33
Having SPSS Auto-Generate Your Syntax 34
Controlling Your “Open” Datafiles 35
Summary 37
Key Terms 38
Discussion Questions 39
References 39
5 Accessing Your Data 41
Chapter Learning Objectives 41
Accessing Your Data Files 42
Get File and Save Outfile 43
Creating Subsets of Data 44
Importing Data from Excel 44
Using the Import Data Wizard 45
The Copy–Paste “Option” (aka This Is a Terrible Idea) 47
Summary 48
Key Terms 48
Discussion Questions 48
6 Defining Your Data 49
Chapter Learning Objectives 49
Annotation 50
Defining Your Dataset 51
Adding Variable Labels 51
Adding Value Labels 52
Summary 54
Key Terms 55
Discussion Questions 55
Part II Statistics 57
7 Descriptive Statistics 59
Chapter Learning Objectives 59
Frequencies 60
Displaying Data Graphically 62
Location and Spread 63
Descriptive Statistics 65
Measures of Central Tendency and Variability 65
A General Note on Analyses 67
A General Note About Output Files 68
Summary 68
Key Terms 68
Discussion Questions 69
8 Hypothesis Testing 71
Chapter Learning Objectives 71
Descriptive Versus Inferential Statistics 72
Hypothesis Testing (A Process for Interpreting Inferential Statistics) 72
Six Steps of Hypothesis Testing 73
Summary 75
Key Terms 75
Discussion Questions 76
9 Z-and T-Tests 77
Chapter Learning Objectives 77
The One Sample Z-Test 78
The t-Test 80
One-Sample T-Test 80
Two Independent Samples T-Test 84
Two Correlated/Paired Samples T-Test 88
Summary 93
Key Terms 93
Discussion Questions 94
10 Inferential Analyses (ANOVAs) 97
Chapter Learning Objectives 97
One-Way ANOVA (One-Way Command) 98
Repeated-Measures ANOVA (GLM Command) 101
Factorial ANOVA (Unianova Command) 109
Follow-Up Contrasts 113
Summary 113
Key Terms 114
Discussion Questions 115
Reference 116
11 Inferential Analyses (Correlation or Regression) 117
Chapter Learning Objectives 117
Correlation 118
Simple Regression 122
Multiple Regression 125
Straight Regression 126
Hierarchical Regression 130
Visualizing Your Relationship 135
Summary 137
Key Terms 137
Discussion Questions 138
12 Nonparametric Analyses 141
Chapter Learning Objectives 141
Parametric Versus Nonparametric Analyses 141
“The” (Pearson’s) Chi-Square: χ2 143
Two Variable Example 146
Summary 150
Key Terms 150
Discussion Questions 151
Part III Advanced Data Management 153
13 Manipulating Your Data 155
Chapter Learning Objectives 155
Creating Scale Scores 156
How SPSS Thinks About Data 156
Recoding Your Data 157
Creating Your Scales 157
The Importance of Selecting All 161
Summary 167
Key Terms 167
Discussion Questions 167
14 Collapsing and Merging Data Files 169
Chapter Learning Objectives 169
Same People, Different Information 170
Different People, Same Information 175
Summary 177
Key Terms 177
Discussion Questions 178
15 Differential Treatment of Your Data 179
Chapter Learning Objectives 179
Isolating Interesting Cases 180
Creating a New Data File 180
Splitting Files 184
Summary 188
Key Terms 188
Discussion Questions 188
16 Using Your Output 189
Chapter Learning Objectives 189
Problem Solving 190
Spaces in All the Wrong Places 190
Column Information 195
There Is One Little Thing… 198
Maximizing Output Information 199
Summary 200
Key Terms 201
Discussion Questions 201
17 Other Tricks of the Trade 203
Chapter Learning Objectives 203
Salvaging Old Syntax 204
The Importance of Notepad 204
Tricking SPSS To “Think” Across Rows 210
Transposing Your Matrix 210
Aggregating Your Files 211
“Do If” and “End If” 215
Summary 218
Key Terms 219
Discussion Questions 219
Appendix A: Completed Questionnaire Form Example 221
Appendix B: Example Code Sheet for Questionnaire 227
Appendix C: Summary of Creating and Defining a Data File 233
Appendix D: Example Syntax File Integrating Multiple Commands (Fulfilling Multiple Purposes) 239
Appendix E: Commands To Know, Organized By Importance 249
Answers to Chapter Discussion Questions 251
Index 263
| Erscheinungsdatum | 08.06.2021 |
|---|---|
| Verlagsort | New York |
| Sprache | englisch |
| Maße | 150 x 226 mm |
| Gewicht | 454 g |
| Themenwelt | Mathematik / Informatik ► Informatik |
| Mathematik / Informatik ► Mathematik | |
| ISBN-10 | 1-119-41742-2 / 1119417422 |
| ISBN-13 | 978-1-119-41742-2 / 9781119417422 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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