IBM SPSS Statistics 27 Step by Step: A Simple Guide and Reference, seventeenth edition, takes a straightforward, step-by-step approach that makes SPSS software clear to beginners and experienced researchers alike. Extensive use of four-color screen shots, clear writing, and step-by-step boxes guide readers through the program. Output for each procedure is explained and illustrated, and every output term is defined. Exercises at the end of each chapter support students by providing additional opportunities to practice using SPSS. This book covers the basics of statistical analysis and addresses more advanced topics such as multidimensional scaling, factor analysis, discriminant analysis, measures of internal consistency, MANOVA (between- and within-subjects), cluster analysis, Log-linear models, logistic regression, and a chapter describing residuals. The end sections include a description of data files used in exercises, an exhaustive glossary, suggestions for further reading, and a comprehensive index. IBM SPSS Statistics 27 Step by Step is distributed in 85 countries, has been an academic best seller through most of the earlier editions, and has proved an invaluable aid to thousands of researchers and students.
Author(s): Darren George, Paul Mallery
Edition: 17
Publisher: Routledge | Taylor & Francis Group
Year: 2022
Language: English
Commentary: TruePDF
Pages: 419
Tags: SPSS (Computer File); Social Sciences: Statistical Methods: Computer Programs
Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Preface
Chapter 1: An Overview of IBM® SPSS® Statistics
Introduction: An Overview of IBM SPSS Statistics 27 and Subscription Classic
1.1: Necessary Skills
1.2: Scope of Coverage
1.3: Overview
1.4: This Book’s Organization, Chapter by Chapter
1.5: An Introduction to the Example
1.6: Typographical and Formatting Conventions
Chapter 2A: IBM SPSS Statistics Processes for PC
2.1: The Mouse
2.2: The Taskbar and Start Menu
2.3: Common Buttons
2.4: The Data and Other Commonly Used Windows
2.5: The Open Data File Dialog Window
2.6: The Output Window
2.7: Modifying or Rearranging Tables
2.8: Printing or Exporting Output
2.9: The “Options . . . ” Option: Changing the Formats
Chapter 2B: IBM SPSS Statistics Processes for Mac
2.1: Selecting
2.2: The Desktop, Dock, and Application Folder
2.3: Common Buttons
2.4: The Data and Other Commonly Used Windows
2.5: The Open Data File Dialog Window
2.6: The Output Window
2.7: Modifying or Rearranging Tables
2.8: Printing or Exporting Output
2.9: The “Options . . .” Option: Changing the Formats
Chapter 3: Creating and Editing a Data File
3.1: Research Concerns and Structure of the Data File
3.2: Step by Step
3.3: Entering Data
3.4: Editing Data
3.5: Grades.sav: The Sample Data File
Exercises
Chapter 4: Managing Data
4.1: Step By Step: Manipulation of Data
4.2: The Case Summaries Procedure
4.3: Replacing Missing Values Procedure
4.4: The Compute Procedure: Creating New Variables
4.5: Recoding Variables
4.6: The Select Cases Option
4.7: The Sort Cases Procedure
4.8: Merging Files Adding Blocks of Variables or Cases
4.9: Printing Results
Exercises
Chapter 5: Graphs and Charts: Creating and Editing
5.1: Comparison of the Two Graphs Options
5.2: Types of Graphs Described
5.3: The Sample Graph
5.4: Producing Graphs and Charts
5.5: Bugs
5.6: Specific Graphs Summarized
5.7: Printing Results
Exercises
Chapter 6: Frequencies
6.1: Frequencies
6.2: Bar Charts
6.3: Histograms
6.4: Percentiles
6.5: Step by Step
6.6: Printing Results
6.7: Output
Exercises
Chapter 7: Descriptive Statistics
7.1: Statistical Significance
7.2: The Normal Distribution
7.3: Measures of Central Tendency
7.4: Measures of Variability Around the Mean
7.5: Measures of Deviation from Normality
7.6: Measures of Size of the Distribution
7.7: Measures of Stability: Standard Error
7.8: Step by Step
7.9: Printing Results
7.10: Output
Exercises
Chapter 8: Crosstabulation and χ2 Analyses
8.1: Crosstabulation
8.2: Chi-Square (χ2) Tests of Independence
8.3: Step by Step
8.4: Weight Cases Procedure: Simplified Data Setup
8.5: Printing Results
8.6: Output
Exercises
Chapter 9: The Means Procedure
9.1: Step by Step
9.2: Printing Results
9.3: Output
Exercises
Chapter 10: A Priori Power Analysis: What Sample Size Do I Need?
10.1: One-Sample t Test
10.2: Independent-Samples t Test
10.3: Paired-Samples t Test
10.4: One-Way ANOVA
10.5: Correlation
10.6: Regression
10.7: Printing Results
Exercises
Chapter 11: Bivariate Correlation
11.1: What is a Correlation?
11.2: Additional Considerations
11.3: Step by Step
11.4: Printing Results
11.5: Output
Exercises
Chapter 12: The t Test Procedure
12.1: Independent-Samples t Tests
12.2: Paired-Samples t Tests
12.3: One-Sample t Tests
12.4: Significance and Effect Size
12.5: Step by Step
12.6: Printing Results
12.7: Output
Exercises
What is Bootstrapping?
Chapter 13: The One-Way ANOVA Procedure
13.1: Introduction to One-Way Analysis of Variance
13.2: Step by Step
13.3: Printing Results
13.4: Output
Exercises
Chapter 14: General Linear Model: Two-Way ANOVA
14.1: Statistical Power
14.2: Two-Way Analysis of Variance
14.3: Step by Step
14.4: Printing Results
14.5: Output
Exercises
Chapter 15: General Linear Model: Three-Way ANOVA
15.1: Three-Way Analysis of Variance
15.2: The Influence of Covariates
15.3: Step by Step
15.4: Printing Results
15.5: Output
15.6: A Three-Way ANOVA that Includes a Covariate
Exercises
Chapter 16: Simple Linear Regression
16.1: Predicted Values and the Regression Equation
16.2: Simple Regression and the Amount of Variance Explained
16.3: Testing for a Curvilinear Relationship
16.4: Step by Step
16.5: Printing Results
16.6: Output
16.7: A Regression Analysis that Tests for a Curvilinear Trend
Exercises
Chapter 17: Multiple Regression Analysis
17.1: The Regression Equation
17.2: Regression and R2: The Amount of Variance Explained
17.3: Curvilinear Trends, Model Building, and References
17.4: Step by Step
17.5: Printing Results
17.6: Output
17.7: Change of Values as Each new Variable is Added
Exercises
Chapter 18: Nonparametric Procedures
18.1: Step by Step
18.2: Are Observed Values Distributed Differently than a Hypothesized Distribution?
18.3: Is the Order of Observed Values Non-Random?
18.4: Is a Continuous Variable Different in Different Groups?
18.5: Are the Medians of a Variable Different for Different Groups?
18.6: Are My Within-Subjects (Dependent Samples or Repeated Measures) Measurements Different?
18.7: Printing Results
Chapter 19: Reliability Analysis
19.1: Coefficient Alpha (ɑ)
19.2: Split-Half Reliability
19.3: The Example
19.4: Step by Step
19.5: Printing Results
19.6: Output
Exercises
Chapter 20: Multidimensional Scaling
20.1: Square Asymmetrical Matrixes (The Sociogram Example)
20.2: Step by Step
20.3: Printing Results
20.4: Output
Chapter 21: Factor Analysis
21.1: Create a Correlation Matrix
21.2: Factor Extraction
21.3: Factor Selection and Rotation
21.4: Interpretation
21.5: Step by Step
21.6: Output
Chapter 22: Cluster Analysis
22.1: Cluster Analysis and Factor Analysis Contrasted
22.2: Procedures for Conducting Cluster Analysis
22.3: Step by Step
22.4: Printing Results
22.5: Output
Chapter 23: Discriminant Analysis
23.1: The Example: Admission into a Graduate Program
23.2: The Steps Used in Discriminant Analysis
23.3: Step by Step
23.4: Output
Chapter 24: General Linear Models: MANOVA and MANCOVA
24.1: Step by Step
24.2: Printing Results
24.3: Output
Exercises
Chapter 25: G.L.M.: Repeated-Measures MANOVA
25.1: Step by Step
25.2: Printing Results
25.3: Output
Exercises
Chapter 26: Logistic Regression
26.1: The Math of Logistic Regression
26.2: Step by Step
26.3: Printing Results
26.4: Output
Chapter 27: Hierarchical Log-Linear Models
27.1: Log-Linear Models
27.2: The Model Selection Log-Linear Procedure
27.3: Step by Step
27.4: Printing Results
27.5: Output
Chapter 28: Nonhierarchical Log-Linear Models
28.1: Models
28.2: A Few Words about Model Selection
28.3: Types of Models Beyond the Scope of This Chapter
28.4: Step by Step
28.5: Printing Results
28.6: Output
Chapter 29: Residuals: Analyzing Left-Over Variance
29.1: Residuals
29.2: Linear Regression: A Case Study
29.3: General Log-Linear Models: A Case Study
29.4: Accessing Residuals in SPSS
Data Files
Glossary
References
Credits
Index
