Table of contents for Making sense of multivariate data analysis / John Spicer.

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 Part I. The Core Ideas
1. What Makes a Difference?
1. 1	Analyzing Data In the Form of Scores
1.1.1	Univariate Analysis: Capturing Differences In One Set of Scores
1.1.2	Bivariate Analysis: Accounting for Score Differences With Categories
1.1.3	Bivariate Analysis: Accounting for Score Differences With Scores
1.2	Analyzing Data In the Form of Categories
1.3	Further Reading
2. Deciding Whether Differences Are Trustworthy
2.1	Sampling Issues
2.2	Measurement Issues
2.2.1 	Measurement Scales
2.2.2 	Measurement Quality
2.3 	The Role of Chance
2.3.1 	Evaluating Chance With Null Hypothesis Testing
2.3.2	Evaluating the Role of Chance With Estimation
2.4	Statistical Assumptions
2.4.1	Independence of Cases
2.4.2	Normality of Distributions
2.4.3 	Equality of Variances
2.5	Further Reading
3. Accounting for Differences In a Complex World
3.1	Limitations of Bivariate Analysis
3.2	The Multivariate Strategy
3.2.1	A Review of Regression Building Blocks
3.2.2	The Composite Variable in Regression
3.2.3	Generalizing the Composite Variable
3.3	Common Misinterpretations of Multivariate Analyses
3.3.1	What Does ?Accounting for? Differences Mean?
3.3.2 	To Whom or What Do Statistical Results Apply?
3.3.3 	What Do the Results of Hypothesis Tests Mean?
3.3.4	What Does Statistical Control Actually Achieve?
3.4	Further Reading
 Part II. The Techniques
4. Multiple Regression
4.1	The Composite Variable In Multiple Regression
4.2	Standard Multiple Regression In Action
4.3	Trustworthiness In Regression Analysis
4.3.1	Sampling and Measurement Issues
4.3.2	Checking Assumptions in Multiple Regression
4.3.3	Other Issues of Trustworthiness
4.4 	Accommodating Other Types of Independent Variable
4.5 	Sequential Regression Analysis
4.6	Further Reading
5. Logistic Regression and Discriminant Analysis
5.1	Logistic Regression
5.1.1	The Composite Variable In Logistic Regression
5.1.2 	Binary Logistic Regression In Action
5.1.3 	Trustworthiness In Logistic Regression
5.1.4 	Extending the Scope of Logistic Regression				
5.2	Discriminant Analysis
5.2.1	The Composite Variable In Discriminant Analysis
5.2.2	Two Group Discriminant Analysis In Action
5.2.3	Trustworthiness In Discriminant Analysis
5.2.4	Extending the Scope of Discriminant Analysis
5.3	Further Reading
6. Multivariate Analysis of Variance
6.1	Oneway Analysis of Variance
6.2	Factorial Analysis of Variance
6.3	Multivariate Analysis of Variance
6.3.1	The Composite Variable In MANOVA
6.3.2	MANOVA In Action
6.4	Within-Subjects ANOVA and MANOVA
6.5	Issues of Trustworthiness In MANOVA
6.6	Analysis of Covariance
6.7	Further Reading
7. Factor Analysis
7.1	The Composite Variable In Factor Analysis
7.2	Factor Analysis In Action
7.2.1	Extracting and Rotating Factors
7.2.2	Interpreting Factors
7.2.3	Assessing Goodness of Fit
7.2.4	Choosing the Number of Factors
7.2.5	An Example From the Well-Being Literature
7.3	Issues of Trustworthiness In Factor Analysis
7.4	Confirmatory Factor Analysis
7.5	Further Reading
8. Log-Linear Analysis
8.1	Hierarchical Log-Linear Analysis
8.1.1	The Composite Variable In Log-Linear Analysis
8.1.2	Log-Linear Analysis In Action
8.2	Trustworthiness In Log-Linear Analysis
8.3	Log-Linear Analysis With a Dependent Variable: Logit Analysis
8.4	Further Reading
About the Author

Library of Congress Subject Headings for this publication: Social sciences Statistical methods, Multivariate analysis