This volume focuses on computational techniques appropriate to the analysis of data from within-subjects and crossover within-subjects designs using various statistical packages. For statisticians, behavioral scientists, and researchers.
This volume focuses on computational techniques appropriate to the analysis of data from within-subjects and crossover within-subjects designs using various statistical packages. For statisticians, behavioral scientists, and researchers.
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, A mBm, 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.
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, A mBm, 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.
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