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Index
Cover
Title Page
Copyright
Preface
Part 1: Getting Started With IBM SPSS®
Chapter 1: Introduction to IBM SPSS®
1.1 What is IBM SPSS?
1.2 Brief History
1.3 Types of IBM SPSS Files and File Name Extensions
Chapter 2: Entering Data in IBM SPSS®
2.1 The Starting Point
2.2 The Two Types of Displays
2.3 A Sample Data Set
2.4 The Variable View Display
2.5 Entering Specifications in the Variable View Display
2.6 Saving the Data File
2.7 Entering Specifications in the Data View Display
Chapter 3: Importing Data from Excel to IBM SPSS®
3.1 The Starting Point
3.2 The Importing Process
Part 2: Obtaining, Editing, and Saving Statistical Output
Chapter 4: Performing Statistical Procedures in IBM SPSS®
4.1 Overview
4.2 Using Dialog Windows to Setup the Analysis
4.3 The Output
Chapter 5: Editing Output
5.1 Overview
5.2 Changing the Wording of a Column Heading
5.3 Changing the Width of a Column
5.4 Viewing More Decimal Values
5.5 Editing Text in IBM SPSS Output Files
Chapter 6: Saving and Copying Output
6.1 Overview
6.2 Saving an Output File as an IBM SPSS Output File
6.3 Saving an Output File in Other Formats
6.4 Using Operating System Utilities to Copy an IBM SPSS Table to a Word Processing Document
6.5 Using the Copy and Paste Functions to Copy an IBM SPSS Output Table to a Word Processing Document
Part 3: Manipulating Data
Chapter 7: Sorting and Selecting Cases
7.1 Overview
7.2 Sorting Cases
7.3 Selecting Cases
Chapter 8: Splitting Data Files
8.1 Overview
8.2 The General Splitting Process
8.3 The Procedure to Split the Data File
8.4 The Data File after the Split
8.5 Statistical Analyses Under Split File
8.6 Resetting the Data File
Chapter 9: Merging Data from Separate Files
9.1 Overview
9.2 Adding Cases
9.3 Adding Variables
Part 4: Descriptive Statistics Procedures
Chapter 10: Frequencies
10.1 Overview
10.2 Numerical Example
10.3 Analysis Setup: Categorical Variables
10.4 Analysis Output: Categorical Variables
10.5 Analysis Setup: Quantitative Variables
10.6 Analysis Output: Quantitative Variables
Chapter 11: Descriptives
11.1 Overview
11.2 Numerical Example
11.3 Analysis Setup
11.4 Analysis Output
Chapter 12: Explore
12.1 Overview
12.2 Numerical Example
12.3 Analysis Setup
12.4 Analysis Output
Part 5: Simple Data Transformations
Chapter 13: Standardizing Variables to z Scores
13.1 Overview
13.2 Numerical Example
13.3 Analysis Setup
13.4 Analysis Output
13.5 Descriptive Statistics on Zneoneuro
13.6 Other Standard Scores
Chapter 14: Recoding Variables
14.1 Overview
14.2 Numerical Example
14.3 Analysis Strategy
14.4 Frequencies Analysis
14.5 Recoding an Original Variable Using Ranges
14.6 The Results of the Recoding
14.7 Recoding an Original Variable Using Individual Values
Chapter 15: Visual Binning
15.1 Overview
15.2 Numerical Example
15.3 Analysis Setup
Chapter 16: Computing New Variables
16.1 Overview
16.2 Computing an Algebraic Expression
16.3 The Outcome of Computing the Linear T Scores
16.4 Computing the Mean of a Set of Variables
16.5 Numerical Example of Computing the Mean of a Set of Variables
16.6 The Computation Process
16.7 The Outcome of Computing the Affiliation Subscale
Chapter 17: Transforming Dates to Age
17.1 Overview
17.2 The IBM SPSS® System Clock
17.3 Date Formats
17.4 The Date and Time Wizard
17.5 Using a Different Time Referent
17.6 Using Age in a Statistical Analysis
Part 6: Evaluating Score Distribution Assumptions
Chapter 18: Detecting Univariate Outliers
18.1 Overview
18.2 Numerical Example
18.3 Analysis Setup: Sample as a Whole
18.4 Analysis Output: Sample as a Whole
18.5 Analysis Setup: Considering the Categorical Variable of Sex
18.6 Analysis Output: Considering the Categorical Variable of Sex
Chapter 19: Detecting Multivariate Outliers
19.1 Overview
19.2 The Mahalanobis Distance
19.3 Numerical Example
19.4 Analysis Setup: Linear Regression
19.5 Analysis Output: Linear Regression
19.6 Strategies to Examine the Results
19.7 Examining the Data
Chapter 20: Assessing Distribution Shape: Normality, Skewness, and Kurtosis
20.1 Overview
20.2 Numerical Example
20.3 Analysis Strategy
20.4 Analysis Setup: Frequencies
20.5 Analysis Output: Frequencies
20.6 Analysis Set Up: Explore
20.7 Analysis Output: Explore
Chapter 21: Transforming Data to Remedy Statistical Assumption Violations
21.1 Overview
21.2 Numerical Example
21.3 Analysis Strategy
21.4 Analysis Setup: Frequencies of the Original doc_visits Variable
21.5 Analysis Output: Frequencies of the Original doc_visits Variable
21.6 Analysis Setup: Square Root Transformation
21.7 Analysis Setup: Log Base 10 Transformation
21.8 Analysis Setup: Reflected Inverse Transformation
21.9 Analysis Setup: Frequencies of All of the Transformed Variables
21.10 Analysis Output
Part 7: Bivariate Correlation
Chapter 22: Pearson Correlation
22.1 Overview
22.2 Numerical Example
22.3 Analysis Setup: Checking for Linearity
22.4 Analysis Output: Checking for Linearity
22.5 Analysis Setup: Correlating a Single Pair of Variables
22.6 Analysis Output: Correlating a Single Pair of Variables
22.7 Correlating Several Pairs of Variables
Chapter 23: Spearman Rho and Kendall Tau-b Rank-Order Correlations
23.1 Overview
23.2 The Spearman Rho Correlation
23.3 The Kendall Tau-b Correlation
23.4 Numerical Example Without Ties
23.5 Analysis Setup
23.6 Analysis Output
23.7 Numerical Example With Ties
23.8 Analysis Setup and Output
Part 8: Regressing (Predicting) Quantitative Variables
Chapter 24: Simple Linear Regression
24.1 Overview
24.2 Numerical Example
24.3 Analysis Setup
24.4 Analysis Output
24.5 The Y Intercept Issue
Chapter 25: Centering the Predictor Variable in Simple Linear Regression
25.1 Overview
25.2 Numerical Example
25.3 Analysis Strategy
25.4 Obtaining Descriptive Statistics on the Predictor Variable
25.5 Computing the Centered Predictor Variable
25.6 Analysis Setup: Simple Linear Regression Using BMI as the Predictor
25.7 Analysis Setup: Simple Linear Regression Using BMIcentered as the Predictor
25.8 Analysis Output From Both Regression Analyses
Chapter 26: Multiple Linear Regression
26.1 Overview
26.2 Numerical Example
26.3 Analysis Strategy
26.4 Analysis Setup: Standard Method
26.5 Analysis Output: Standard Method
26.6 Analysis Setup: Stepwise Method
26.7 Analysis Output: Stepwise Method
26.8 Analysis Setup: Automatic Linear Modeling
26.9 Analysis Output: Automatic Linear Modeling
Chapter 27: Hierarchical Linear Regression
27.1 Overview
27.2 Numerical Example and Analysis Strategy
27.3 Analysis Setup
27.4 Analysis Output
Chapter 28: Polynomial Regression
28.1 Overview
28.2 Numerical Example
28.3 Analysis Strategy
28.4 Obtaining the Scatterplot
28.5 Computing the Polynomial Variables
28.6 Analysis Setup: Linear Regression
28.7 Analysis Output: Linear Regression
Chapter 29: Multilevel Modeling
29.1 Overview
29.2 Numerical Example
29.3 Analysis Strategy
29.4 Aggregating the Optimism Variable
29.5 Centering the Level 2 optimismgroupmean Variable
29.6 Analysis Setup: Unconditional Model
29.7 Analysis Output: Unconditional Model
29.8 Analysis Setup: Mixed Level 1 Model
29.9 Analysis Output: Mixed Level 1 Model
29.10 Analysis Setup: Mixed Level 2 Model
29.11 Analysis Output: Mixed Level 2 Model
29.12 Analysis Setup: Hierarchical Model
29.13 Analysis Output: Hierarchical Model
29.14 Analysis Setup: Interaction Model
29.15 Analysis Output: Interaction Model
Part 9: Regressing (Predicting) Categorical Variables
Chapter 30: Binary Logistic Regression
30.1 Overview
30.2 Numerical Example
30.3 Analysis Setup
30.4 Analysis Output
Chapter 31: ROC Analysis
31.1 Overview
31.2 Numerical Example
31.3 Analysis Strategy
31.4 Binary Logistic Regression Analysis: Default Classification Cutoff
31.5 ROC Analysis: Setup
31.6 ROC Analysis: Output
31.7 Binary Logistic Regression Analysis: Revised Classification Cutoff
Chapter 32: Multinominal Logistic Regression
32.1 Overview
32.2 Numerical Example
32.3 Analysis Setup
32.4 Analysis Output
Part 10: Survival Analysis
Chapter 33: Survival Analysis: Life Tables
33.1 Overview
33.2 Numerical Example
33.3 Analysis Setup
33.4 Analysis Output
Chapter 34: The Kaplan–Meier Survival Analysis
34.1 Overview
34.2 Numerical Example
34.3 Analysis Strategy
34.4 Analysis Setup: Comparing Males and Females
34.5 Analysis Output: Comparing Males and Females
34.6 Analysis Setup: Comparing Males and Females with Stratification
34.7 Analysis Output: Comparing Males and Females with Stratification
Chapter 35: Cox Regression
35.1 Overview
35.2 Numerical Example
35.3 Analysis Setup
35.4 Analysis Output
Part 11: Reliability as a Gauge of Measurement Quality
Chapter 36: Reliability Analysis: Internal Consistency
36.1 Overview
36.2 Numerical Example
36.3 Analysis Setup
36.4 Analysis Output
Chapter 37: Reliability Analysis: Assessing Rater Consistency
37.1 Overview
37.2 Numerical Example: ICC
37.3 Analysis Setup: ICC
37.4 Analysis Output: ICC
37.5 Numerical Example: Kappa
37.6 Analysis Setup: Kappa
37.7 Analysis Output: Kappa
Part 12: Analysis of Structure
Chapter 38: Principal Components and Factor Analysis
38.1 Overview of Principal Components and Factor Analysis
38.2 Numerical Example
38.3 A Starting Place
38.4 Analysis Setup: Preliminary Analysis
38.5 Analysis Output: Preliminary Analysis
38.6 Our Analysis Strategy for the Main Analyses
38.7 Analysis Setup for the Four-Factor Structure
38.8 Analysis Output for the Four-Component/Factor Structure
38.9 The Three-Component/Factor Structure
38.10 Determining Which Solution to Accept
Chapter 39: Confirmatory Factor Analysis
39.1 Overview
39.2 Numerical Example
39.3 Drawing the Model
39.4 Analysis Setup
39.5 Analysis Output
39.6 Analysis Setup: Modified Model
39.7 Analysis Output: Modified Model
Part 13: Evaluating Causal (Predictive) Models
Chapter 40: Simple Mediation
40.1 Overview
40.2 Numerical Example
40.3 Analysis Strategy
40.4 The Independent Variable Predicting the Mediator Variable
40.5 The Independent Variable and the Mediator Predicting the Outcome Variable
40.6 The Unmediated Model with the Independent Variable Predicting the Dependent Variable
40.7 Consolidating the Results of the Mediation Model
40.8 Testing the Statistical Significance of the Indirect Effect
40.9 Testing the Statistical Significance of the Difference between the Direct Paths in the Unmediated and the Mediated Models
40.10 Determining the Relative Strength of the Mediated Effect
Chapter 41: Path Analysis Using Multiple Regression
41.1 Overview
41.2 Numerical Example
41.3 Analysis Strategy
41.4 The “Flat” Multiple Regression Analysis: Setup
41.5 The “Flat” Multiple Regression Analysis: Output
41.6 Path Analysis using Multiple Regression: Analysis 1
41.7 Path Analysis using Multiple Regression: Analysis 2
41.8 Path Analysis using Multiple Regression: Synthesis
41.9 Assessing the Statistical Significance of the Indirect Effects
41.10 Assessing the Strength of Each Indirect Effect
41.11 Evaluating the Possibility of Mediation
41.12 Testing the Statistical Significance of the Difference between the Direct Paths in the Unmediated and the Mediated Models
Chapter 42: Path Analysis Using Structural Equation Modeling
22.1 Overview
22.2 Path Analysis Based on SEM: Drawing the Model
22.3 Path Analysis based on SEM: Analysis Setup
22.4 Path Analysis based on SEM: Analysis Output
22.5 Path Analysis Based on SEM: Modified Model Output
Chapter 43: Structural Equation Modeling
43.1 Overview
43.2 Numerical Example
43.3 Analysis Strategy
43.4 Evaluating the Measurement Model: Drawing the Model
43.5 Evaluating the Measurement Model: Analysis Setup
43.6 Evaluating the Measurement Model: Analysis Output
43.7 Evaluating the Structural Model: Drawing the Model
43.8 Evaluating the Structural Model: Analysis Setup
43.9 Evaluating the Structural Model: Analysis Output
43.10 Evaluating the Structural Model: Synthesis
43.11 The Strategy to Configure and Analyze a Trimmed Model
43.12 Examining the Direct Effect of Efficacy on Statistics in Isolation
43.13 Examining the Mediated Effect of Efficacy on Statistics through Science
43.14 Synthesis of the Trimmed (Mediated) Model Results
43.15 Statistical Significance of the Indirect Effect: The Aroian Test
43.16 Comparing the Direct Effects of Efficacy on Statistics in the Simple Model and the Mediated Model: The Freedman-Schatzkin Test
43.17 The Relative Strength of the Mediated Effect
Part 14: Test
Chapter 44: One-Sample t Test
44.1 Overview
44.2 Numerical Example
44.3 Analysis Setup
44.4 Analysis Output
Chapter 45: Independent-Samples t Test
45.1 Overview
45.2 Numerical Example: Meeting the Homogeneity of Variance Assumption
45.3 Analysis Setup: Meeting the Homogeneity of Variance Assumption
45.4 Analysis Output: Meeting the Homogeneity of Variance Assumption
45.5 Magnitude of the Mean Difference
45.6 Numerical Example: Violating the Homogeneity of Variance Assumption
45.7 Analysis Setup: Violating the Homogeneity of Variance Assumption
45.8 Analysis Output: Violating the Homogeneity of Variance Assumption
Chapter 46: Paired-Samples t Test
46.1 Overview
46.2 Numerical Example
46.3 Analysis Setup
46.4 Analysis Output
46.5 Magnitude of the Mean Difference
Part 15: Univariate Group Differences: ANOVA and ANCOVA
Chapter 47: One-Way Between-Subjects ANOVA
47.1 Overview
47.2 Numerical Example
47.3 Analysis Strategy
47.4 Analysis Setup
47.5 Analysis Output
Chapter 48: Polynomial Trend Analysis
48.1 Overview
48.2 Numerical Example
48.3 Analysis Strategy
48.4 Analysis Setup
48.5 Analysis Output
Chapter 49: One-Way Between-Subjects ANCOVA
49.1 Overview
49.2 Numerical Example
49.3 Analysis Strategy
49.4 Analysis Setup: ANOVA
49.5 Analysis Output: ANOVA
49.6 Evaluating the ANCOVA Assumptions
49.7 Analysis Setup: ANCOVA
49.8 Analysis Output: ANCOVA
Chapter 50: Two-Way Between-Subjects ANOVA
50.1 Overview
50.2 Numerical Example
50.3 Analysis Setup
50.4 Analysis Output: Omnibus Analysis
50.5 Analysis Output: Simple Effects Tests
Chapter 51: One-Way Within-Subjects ANOVA
51.1 Overview
51.2 Numerical Example
51.3 Analysis Setup
51.4 Analysis Output
Chapter 52: Repeated Measures Using Linear Mixed Models
52.1 Overview
52.2 Numerical Example
52.3 Analysis Strategy
52.4 Restructuring the Data File
52.5 Analysis Setup: Autoregressive Covariance Structure
52.6 Analysis Output: Autoregressive Covariance Structure
52.7 Analysis: Compound Symmetry
52.8 Analysis: Unstructured Covariance
Chapter 53: Two-Way Mixed ANOVA
53.1 Overview
53.2 Numerical Example
53.3 Analysis Setup
53.4 Analysis Output
Part 16: Multivariate Group Differences: MANOVA and Discriminant Function Analysis
Chapter 54: One-Way Between-Subjects MANOVA
54.1 Overview
54.2 Numerical Example
54.3 Correlation Analysis
54.4 Analysis Setup: Manova
54.5 Analysis Output: MANOVA
Chapter 55: Discriminant Function Analysis
55.1 Overview
55.2 Numerical Example
55.3 Analysis Setup
55.4 Analysis Output
Chapter 56: Two-Way Between-Subjects MANOVA
56.1 Overview
56.2 Numerical Example
56.3 Analysis Setup
56.4 Analysis Output
Part 17: Multidimensional Scaling
Chapter 57: Multidimensional Scaling: Classical Metric
57.1 Overview
57.2 Numerical Example
57.3 Analysis Setup
57.4 Analysis Output
Chapter 58: Multidimensional Scaling: Metric Weighted
58.1 Overview
58.2 Numerical Example
58.3 Analysis Setup
58.4 Analysis Output
Part 18: Cluster Analysis
Chapter 59: Hierarchical Cluster Analysis
59.1 Overview
59.2 Numerical Example
59.3 Analysis Setup
59.4 Analysis Output
Chapter 60: k-Means Cluster Analysis
60.1 Overview
60.2 Numerical Example: -Means Clustering
60.3 Analysis Strategy
60.4 Transforming Cluster Variables to z Scores
60.5 Analysis Setup: -Means Clustering
60.6 Analysis Output: -Means Clustering
60.7 Follow-Up One-Way Between-Subjects Analysis
60.8 Numerical Example: One-Way ANOVA
60.9 Analysis Setup: One-Way ANOVA
60.10 Analysis Output: One-Way ANOVA
Part 19: Nonparametric Procedures for Analyzing Frequency Data
Chapter 61: Single-Sample Binomial and Chi-Square Tests: Binary Categories
61.1 Overview
61.2 Numerical Example
61.3 Analysis Strategy
61.4 Frequencies Analysis
61.5 Analysis Setup
61.6 Analysis Output
Chapter 62: Single-Sample (One-Way) Multinominal Chi-Square Tests
62.1 Overview
62.2 Numerical Example
62.3 Analysis Strategy
62.4 Frequencies Analysis
62.5 Analysis Setup: Omnibus Analysis
62.6 Analysis Output: Omnibus Analysis
62.7 Analysis Setup: Comparison of Categories 1 and 2
62.8 Analysis Output: Comparison of Categories 1 and 2
62.9 Analysis Setup: Comparison of Categories 1 and 3
62.10 Analysis Output: Comparison of Categories 1 and 3
62.11 Analysis Setup: Comparison of Categories 2 and 3
62.12 Analysis Output: Comparison of Categories 2 and 3
Chapter 63: Two-Way Chi-Square Test of Independence
63.1 Overview
63.2 Analysis Strategy
63.3 Numerical Example: 2 × 2 Chi-Square
63.4 Analysis Setup: 2 × 2 Chi-Square
63.5 Analysis Output: 2 × 2 Chi-Square
63.6 Numerical Example: 4 × 2 Chi-Square
63.7 Analysis Setup: 4 × 2 Chi-Square
63.8 Analysis Output: 4 × 2 Chi-Square
Chapter 64: Risk Analysis
64.1 Overview
64.2 Numerical Example
64.3 Analysis Setup
64.4 Analysis Output
Chapter 65: Chi-Square Layers
65.1 Overview
65.2 Numerical Example
65.3 Analysis Setup
65.4 Analysis Output
Chapter 66: Hierarchical Loglinear Analysis
66.1 Overview
66.2 Numerical Example
66.3 Analysis Setup
66.4 Analysis Output
66.5 The Next Steps
Appendix: Statistics Tables
References
Author Index
Subject Index
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