EDUCATION 257      Winter-Spring 2003

David Rogosa (rag@stanford.edu, e314)

I. Design and Analysis of Comparative Studies (Experiments)

A. Introduction and review. Factorial Designs
1. Comparing group outcomes on a single classification: One-way analysis of variance
2. Multiple comparisons in one-way anova
3. Two-way fixed effects anova and interactions 
NWK readings for intro factorial designs
one-way anova NWK 16.1-16.9
post hoc pairwise comparisons NWK 17.4-17.5
factorial designs: two-way fixed effects NWK 19.1-19.6, 20.2,20.3
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B. More Factorial Designs
1. Random and mixed anova models (multiple comparisons, variance component estimation)
2. Unbalanced designs
3. k-way classifications
4. Design--Sample size and power
5. Randomized block designs (including Latin Squares) 
NWK readings for more factorial designs
mixed and random 2-way NWK 24.2-24.4
one observation per cell NWK 21.1-21.2
Unbalanced two-way designs NWK 22.1, 22.2, 22.6 24.6
three-way factorial designs NWK 23.1-23.6, 24.5
planned (orthogonal) comparisons NWK 17.3
design and sample size NWK 26.1-26.5
randomized block designs NWK 27.1-27.7, 30.1-30.2
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C. Nested and Repeated Measures Experimental Designs
1. Nested designs
2. Repeated measures designs 
NWK readings for nested and repeated measures designs
nested and crossed-nested NWK 28.1-28.5, 28.9
repeated measures designs NWK 29.1-29.4
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II. Analysis of Association: Correlation and Regression

Review
Correlation and Straight-line regression

A. Basic Regression Models
1. Multiple regression
2. Polynomial regression
3. Model violations and transformations
Note: readings for introductory regression lectures Part A
Review: Straigt-line regression  NWK Ch 1-4
Multiple Linear Regression
  Basic fit: Inference for parama & fit  Ch.6
  R-sq, adj R-sq pp230-1
  Adjusted Variable Intepretation (partial regr) sec 9.1 
  Testing composite Hypoth sec 7.1-7.3
  partial part correl sec 7.4
  standardeized coeff sec 7.5
  polynomial regr sec 7.7
  Inference for correlations sec 15.4 640-643
  Problems 
     heteroskedascity sec 10.1; autocorrelation ch12.1-12.4;
     multicollinearity sec 7.6, VIF sec 9.5, 10.2
     outliers, resduals sec 9.2
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B. Regression Models with Categorical Variables
1. Reformulation of anova models
2. Analysis of covariance & alternatives 
Note: readings for regression lectures, categorical vars, Part B
  NWK Ch 11 Qualitative predictors; NWK Ch 25 Ancova (via anova models)
    Qualitative predictors:
     0,1 dummy vars, reg params sec 11.1 p456-
     non-parallel regressions  sec 11.2
     regr approach to ancova, more than 2 groups  sec 11.3
     anova one-way sec 16.11, 2-way sec 19.7 p.832
    Ancova
      reduction of error var sec 25.1
      single factor sec 25.2, crackers ex sec25.3
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C. Building Regression Models
1. Variable Selection and Model Construction
2. Intro Path Analysis and LISREL

III. Analysis of Categorical Data

A.    Proportion and Count Outcomes:
        Intro and Review:  Bernoulli, Binomial, Multinomial, and Poisson distributions; inferences for proportion and count data;  Univariate Categorical Data;
        Logit and odds transformations;
       Generalized Linear Models: Logistic and Poisson Regression
         NWK Ch.14, Agresti Ch.1,5, 7,10

B.    Statistical Modelling, Estimation, and Inference for Multivariate Categorical Data
         Review: Basic contingency Tables
         Odds-ratios, conditional and marginal independence, Simpsons Paradox,
         Cochran-Mantel-Haenszel for metanalysis,
          Log-linear models for Multi-way Contingency Tables,
         Associations among ordinal variables
         Agresti Ch. 2, 3, 6, 7, 9.



Additional Readings

Rogosa, D. R. (1980).  Comparing nonparallel regression lines.
  Psychological Bulletin, 88, 307-321.

Rogosa, D. R. (1987).  Casual models do not support scientific
   conclusions: A comment in support of Freedman.
   Journal of Educational Statistics, 12, 185-195.

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