实例雪弗兰与福特的Meta分析.ppt

Chevy versus Ford NASCAR Race Effect Size – A Meta-Analysis Data Description All 256 NASCAR Races for 1993-2000 Season Race Finishes Among all Ford and Chevy Drivers (Ranks) Ford: 5208 Drivers (20.3 per race) Chevrolet: 3642 Drivers (14.2 per race) For each race, Compute Wilcoxon Rank-Sum Statistic (Large-sample Normal Approximation) Effect Size = Z/SQRT(NFord + NChevy) Wilcoxon Rank-Sum Test (Large-Sample) Evidence that Chevrolet tends to do better than Ford Effect Sizes Appear to be approximately Normal Combining Effect Sizes Across Races Weighted Average of Race-Specific Effect Sizes Weight Factor ? 1/V(di) = 1/Ni = 1/(NFord,i+NChevy,i) Test for Homogeneity of Effect Sizes Testing for Year Effects Testing for Year Effects Testing for Year and Race/Track Effects Regression Model Relating Effect Size to: Season (8 Dummy Variables (No Intercept)) Track Length Number of Laps Race Length (Track Length x # of Laps) Weighted Least Squares with weighti = Ni Regression Coefficients/t-tests Controlling for all other predictors, none appear significant C2 – Tests for Sub-Models and Overall Sources Hedges, L.V. and I. Olkin (1985). Statistical Methods for Meta-Analysis, Academic Press, Orlando, FL. Winner, L. (2006). “NASCAR Winston Cup Race Results for 1975-2003,” Journal of Statistical Education, Volume 14, #3 /publications/jse/v14n3/datasets.winner.html