Analyzing Within-subjects Experiments
1st Edition
Published on 30. May 2014
Book
Paperback/Softback
352 pages
978-1-138-00299-9 (ISBN)
Description
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.
Reviews / Votes
"Analyzing Within-Subjects Experiments is a unique book. It is written for behavioral researchers, it covers a category of experimental designs..."-Contemporary Psychology
More details
Language
English
Place of publication
Hove
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Dimensions
Height: 229 mm
Width: 152 mm
Thickness: 19 mm
Weight
513 gr
ISBN-13
978-1-138-00299-9 (9781138002999)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
John W. Cotton
Analyzing Within-subjects Experiments
E-Book
05/2013
1st Edition
Psychology Press Ltd
€37.99
Available for download
John W. Cotton
Analyzing Within-subjects Experiments
E-Book
05/2013
1st Edition
Psychology Press Ltd
€37.99
Available for download
John W. Cotton
Analyzing Within-subjects Experiments
Book
01/1998
1st Edition
Psychology Press
€63.60
Shipment within 10-20 days
Person
John W. Cotton
Content
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.