**I. Introduction**

A. Syllabus: Schedule, Content, and GradingB. Course Content: Correlations and Causal Models

**II. Review and Overview: Critical Distinctions**

A. Constants versus Variables

B. Dependent versus Independent Variables

1. Experimental versus Correlational Data2. Endogenous versus Exogenous Variables

1. Observed versus Latent Variables2. Model Misspecification versus Measurement Error

1. Contrasts Concerning:

a. Central Tendencyb. Variationc. Distributiond. Transformation

**E. Simple versus Complex Causal Theories**

1. Bivariate versus Multivariate Causality2. Linear versus Nonlinear Relations3. Additive versus Multiplicative Functions4. Recursive versus Nonrecursive Models5. Single-Equation versus Multiple-Equation Systems

**III. Bivariate Correlation: The Pearson Product-Moment Coefficient
( r) between 2 Numerical Measures**

A. How isrderived? - Three Derivations

1. Cross-Products and Covariances2. Differences and Prediction3. Least Squares and Regression

1. Incognitor’s (f, point-biserial, and r)2. Pseudo-r’s (tetrachoric, polychoric, and biserial)

1. Prediction2. Explanation3. Estimation

1. Bivariate Distributions2. Curvilinear Relations3. Outliers4. Range Restrictions5. Variable Reliabilities

A. Two Problems

1. How to estimate a causal effect between two variables controlling for a third

a. First solution: The Partial Correlationr_{12.3}b. Second solution: The Semipartial (Part) Correlationr_{1(2.3)}c. Third solution: The Partial Regression CoefficientsB_{12.3}andB_{13.2}

**B. Precautions**

1. Descriptive Statistics: Suppression2. Inferential Statistics:

a. Multicollinearityb. InflatedR^{2}

**D. Significance Tests**

**2. Variable Sets**

**E. Computer Execution**

**V. Review and Exam I**

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