IBM SPSS Statistics 25 Step by Step: A Simple Guide and Reference

Cover
Half Title
Title Page
Copyright Page
Dedication Page
Table of Contents
Preface
1 An Overview of IBM® SPSS® Statistics
	Introduction: An Overview of IBM SPSS Statistics 25
	1.1 Necessary Skills
	1.2 Scope of Coverage
	1.3 Overview
	1.4 This Book’s Organization, by Chapter
	1.5 An Introduction to the Example
	1.6 Typographical and Formatting Conventions
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
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
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
4 Managing Data
	4.1 Step By Step: Manipulation of Data
	4.2 The Case Summaries Procedure
	4.3 The Replace 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
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
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
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 for Size of the Distribution
	7.7 Measures of Stability: Standard Error
	7.8 Step by Step
	7.9 Printing Results
	7.10 Output
	Exercises
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
9 The Means Procedure
	9.1 Step by Step
	9.2 Printing Results
	9.3 Output
	Exercises
10 Bivariate Correlation
	10.1 What is a Correlation?
	10.2 Additional Considerations
	10.3 Step by Step
	10.4 Printing Results
	10.5 Output
	Exercises
11 The t Test Procedure
	11.1 Independent-Samples t Tests
	11.2 Paired-Samples t Tests
	11.3 One-Sample t Tests
	11.4 Significance and Effect Size
	11.5 Step by Step
	11.6 Printing Results
	11.7 Output
	Exercises
12 The One-Way ANOVA Procedure
	12.1 Introduction to One-Way Analysis of Variance
	12.2 Step by Step
	12.3 Printing Results
	12.4 Output
	Exercises
13 General Linear Model: Two-Way ANOVA
	13.1 Statistical Power
	13.2 Two-Way Analysis of Variance
	13.3 Step by Step
	13.4 Printing Results
	13.5 Output
	Exercises
14 General Linear Model: Three-Way ANOVA
	14.1 Three-Way Analysis of Variance
	14.2 The Influence of Covariates
	14.3 Step by Step
	14.4 Printing Results
	14.5 Output
	14.6 A Three-Way Anova that Includes a Covariate
	Exercises
15 Simple Linear Regression
	15.1 Predicted Values and the Regression Equation
	15.2 Simple Regression and the Amount of Variance Explained
	15.3 Testing for a Curvilinear Relationship
	15.4 Step by Step
	15.5 Printing Results
	15.6 Output
	15.7 A Regression Analysis that Tests for a Curvilinear Trend
	Exercises
16 Multiple Regression Analysis
	16.1 The Regression Equation
	16.2 Regression And R2: The Amount of Variance Explained
	16.3 Curvilinear Trends, Model Building, and References
	16.4 Step by Step
	16.5 Printing Results
	16.6 Output
	16.7 Change of Values as Each new Variable is Added
	Exercises
17 Nonparametric Procedures
	17.1 Step by Step
	17.2 Are Observed Values Distributed Differently than a Hypothesized Distribution?
	17.3 Is the Order of Observed Values Non-Random?
	17.4 Is a Continuous Variable Different in Different Groups?
	17.5 Are the Medians of a Variable Different for Different Groups?
	17.6 Are My Within-Subjects (Dependent Samples or Repeated Measures) Measurements Different?
	17.7 Printing Results
18 Reliability Analysis
	18.1 Coefficient Alpha (α)
	18.2 Split-Half Reliability
	18.3 The Example
	18.4 Step by Step
	18.5 Printing Results
	18.6 Output
	Exercises
19 Multidimensional Scaling
	19.1 Square Asymmetrical Matrixes (The Sociogram Example)
	19.2 Step by Step
	19.3 Printing Results
	19.4 Output
20 Factor Analysis
	20.1 Create a Correlation Matrix
	20.2 Factor Extraction
	20.3 Factor Selection and Rotation
	20.4 Interpretation
	20.5 Step by Step
	20.6 Output
21 Cluster Analysis
	21.1 Cluster Analysis and Factor Analysis Contrasted
	21.2 Procedures for Conducting Cluster Analysis
	21.3 Step by Step
	21.4 Printing Results
	21.5 Output
22 Discriminant Analysis
	22.1 The Example: Admission into a Graduate Program
	22.2 The Steps Used in Discriminant Analysis
	22.3 Step by Step
	22.4 Output
23 General Linear Models: MANOVA and MANCOVA
	23.1 Step by Step
	23.2 Printing Results
	23.3 Output
	Exercises
24 G.L.M.: Repeated-Measures Measures MANOVA
	24.1 Step by Step
	24.2 Printing Results
	24.3 Output
	Exercises
25 Logistic Regression
	25.1 Step by Step
	25.2 Printing Results
	25.3 Output
26 Hierarchical Log-Linear Models
	26.1 Log-Linear Models
	26.2 The Model Selection Log-Linear Procedure
	26.3 Step by Step
	26.4 Printing Results
	26.5 Output
27 Nonhierarchical Log-Linear Models
	27.1 Models
	27.2 A Few Words about Model Selection
	27.3 Types of Models Beyond the Scope of This Chapter
	27.4 Step by Step
	27.5 Printing Results
	27.6 Output
28 Residuals: Analyzing Left-Over Variance
	28.1 Residuals
	28.2 Linear Regression: A Case Study
	28.3 General Log-Linear Models: A Case Study
	28.4 Accessing Residuals in SPSS
Data Files
Glossary
References
Credit
Index