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Peer reviewedReichardt, Charles; Gollob, Harry – New Directions for Program Evaluation, 1986
Causal models often omit variables that should be included, use variables that are measured fallibly, and ignore time lags. Such practices can lead to severely biased estimates of effects. The discussion explains these biases and shows how to take them into account. (Author)
Descriptors: Effect Size, Error of Measurement, High Schools, Mathematical Models
Download full textKulik, James A.; Kulik, Chen-Lin C. – 1986
Statistical methodologists have sometimes criticized the use of conventional statistics in meta-analysis, and in recent years a number of them have advocated the use of a special new statistical methodology for research synthesis. An examination of recent books describing this methodology shows that it is seriously limited in its applicability to…
Descriptors: Effect Size, Error of Measurement, Estimation (Mathematics), Mathematical Models
Hummel, Thomas J.; Johnston, Charles B. – 1986
This study investigated seven methods for analyzing multivariate group differences. Bonferroni t statistics, multivariate analysis of variance (MANOVA) followed by analysis of variance (ANOVA), and five other methods were studied using Monte Carlo methods. Methods were compared with respect to (1) experimentwise error rate; (2) power; (3) number…
Descriptors: Analysis of Variance, Comparative Analysis, Correlation, Differences
