*Part One: Multiple Correlation/Regression Analysis*

**I. Introduction**

**A. Syllabus: Schedule, Content, and Grading**

**B. 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 Data**

**2. Endogenous versus Exogenous Variables**

**C. Measured versus Unmeasured Variables**

**1. Observed versus Latent Variables**

**2. Model Misspecification versus Measurement Error**

**D. Levels of Measurement: Nominal, Ordinal, Interval, and Ratio Scales**

**1. Contrasts Concerning:**

**a. Central Tendency**

**b. Variation**

**c. Distribution**

**d. Transformation**

**2. Complications and Simplifications:**

**a. Rank-Category Measures and Ordinal Data with Tied Ranks**

**b. Categorical (Qualitative, Nominal) versus Numerical (Quantitative, Continuous) Measures**

**E. Simple versus Complex Causal Theories**

**1. Bivariate versus Multivariate Causality**

**2. Linear versus Nonlinear Relations**

**3. Additive versus Multiplicative Functions**

**4. Recursive versus Nonrecursive Models**

**5. Single-Equation versus Multiple-Equation Systems**

**F. Descriptive versus Inferential Statistics**

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

**A. How is r derived? – Three Derivations**

**1. Cross-Products and Covariances**

**2. Differences and Prediction**

**3. Least Squares and Regression**

**B. Are there other coefficients besides r?**

**1. Incognito r’s (f, point-biserial, and r)**

**2. Pseudo-**

*r*’s (tetrachoric, polychoric, and biserial)**C. What does r mean?**

**1. Prediction**

**2. Explanation**

**3. Estimation**

**D. What influences the size of r?**

**1. Bivariate Distributions**

**2. Curvilinear Relations**

**3. Outliers**

**4. Range Restrictions**

**5. Variable Reliabilities**

**E. How big must r be to infer a sizable causal effect?**

**IV. Multiple Regression Analysis for Numerical Measures**

**A. Two Problems**

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

**a. First solution: The Partial Correlation r_{12.3}**

**b. Second solution: The Semipartial (Part) Correlation**

*r*_{1(2.3)}**c. Third solution: The Partial Regression Coefficients**

*B*_{12.3}and*B*_{13.2}**2. How to estimate the total causal effect of two causal variables on a single effect variable: R and R^{2}**

**B. Precautions**

**1. Descriptive Statistics: Suppression**

**2. Inferential Statistics:**

**a. Multicollinearity**

**b. Inflated R^{2}**

**C. Generalization to k > 2**

**D. Significance Tests**

**1. Individual Variables**

**a. Simultaneous Model**

**b. Hierarchical Model**

**c. Stepwise “Model”**

**2. Variable Sets**

**E. Computer Execution**

**1. Input**

**2. Output**

**V. Review and Exam I**