Most behavioral scientists know two important concepts -- how to analyze continuous data from randomly assigned treatment groups of subjects and how to assess practice effects for a single group of subjects given a constant treatment at each of several stages of practice. However, except in the case of the repeated measures Latin square design, researchers are not facile in analyzing data from different subjects receiving different treatments at various times in an experiment. This book helps fill the void.

John W. Cotton

Contents: Preface. An Orientation to Within-Subject Designs. Two-Way Experimental Plans: Split-Plot and Randomized Block Designs. Analyzing Data From a Randomized Block Design Experiment That May Exhibit Time-Related Effects. Interpreting Estimability Information and Reported Estimates of Parameters in SAS(r) GLM Programs. Analyzing Data From Within-Subject Factorial Designs, Taking Into Account Stage-of-Practice Effects. Pretest-Posttest Control Group Designs: Comparing Different Treatment Groups After Pretesting. Switching Treatments in Blocks: AmAm, AmBm, BmAm, or BmBm Patterns With m Stages. ALL M's SHOULD BE SUPERSCRIPT EXCEPT FOR THE LAST ONE. Appendices: A Little About Matrices and Vectors. Using the Gauss Matrix Programming Language.