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Experimental Design

Experimental Design
Procedures for the Behavioral Sciences

Fourth Edition

June 2012 | 1 072 pages | SAGE Publications, Inc
This classic text, with a reputuation for accessibility and readability, has been revised and updated to make learning design concepts even easier. Roger E. Kirk shows how three simple experimental designs can be combined to form a variety of complex designs. He provides diagrams illustrating how subjects are assigned to treatments and treatment combinations. New terms are emphasized in boldface type, there are summaries of the advantages and disadvantages of each design, and real-life examples show how the designs are used.

Chapter 1. Research Strategies and the Control of Nuisance Variables
Chapter 2. Experimental Designs: an Overview
Chapter 3. Fundamental Assumptions in Analysis of Variance
Chapter 4. Completely Randomized Design
Chapter 5. Multiple Comparison Tests
Chapter 6. Trend Analysis
Chapter 7. General Linear Model Approach to ANOVA
Chapter 8. Randomized Block Designs
Chapter 9. Completely Randomized Factorial Design with Two Treatments
Chapter 10. Completely Randomized Factorial Design with Three or More Treatments and Randomized Block Factorial Design
Chapter 11. Hierarchical Designs
Chapter 12. Split-Plot Factorial Design: Design with Group-Treatment Confounding
Chapter 13. Analysis of Covariance
Chapter 14. Latin Square and Related Designs
Chapter 15. Confounded Factorial Designs: Designs with Group-Interaction Confounding
Chapter 16. Fractional Factorial Designs: Designs with Treatment-Interaction Confounding

Generally, a very well written and understandable book. However, with it's depth and thoroughness it is more suited for academics and possibly doctoral students with an interest in methods than M.Sc. student. Unfortunately, for them it will likely not fit their level and their expectancies (e.g., they prefer to not have formulas or calculations).

Dr Sebastian Jentschke
Department of Psychosocial Science , Bergen University
April 30, 2019

Kirk is squarely a postgraduate level book. Undergraduates and even master students in most psychology and behavioral sciences tracks, with the possible exception of strongly research-oriented cognitive science tracks, will likely find it way beyond their level. It definitely requires a pretty solid math background (at least bachelor level calculus, linear algebra, and mathematical statistics), and prior experience with statistical computing in some environment such as SPSS, SAS, or R.

The Kirk book should probably be more aptly called and marketed as a guide to designing Analysis of Variance (ANOVA) statistical apparatus. This is the precise focus of the book, and readers expecting to find a general treatment of research design and planning experiments, or a treatment of other kinds of statistical methods, should be directed elsewhere. This book is General Linear Model as seen in ANOVA, and that's the one and only area it treats - in exhaustive depth. (That being said, the first chapter of the book is the most succinct overview I've ever seen of the main points to keep in mind when designing a study; but the rest of the book deals with much more specialized themes).

Second, the book's style and structure feels inherited from the days when statistics and research design was to be learned with paper, pencil, and perhaps a sizable desk calculator. These days I'd expect most books of this kind to directly provide the reader with invitations and means to implement the models discussed on a computer - for instance, in R or SPSS environment. The way the book currently stands, the reader has to work all of this on their own, with other resources, which is a sure way to turn away most modern readers who do not come to the courses with strong pre-existing background in data analysis.

Dr Nikita A. Kharlamov
Communication & Psychology Department, Aalborg University
April 4, 2016

I really enjoyed that book for it helps students considering how research can be valuable in behavioural sciences. Well done!

Dr George Varvatsoulias
Counselling, Newham College of Further Education
September 30, 2015

I draw on this text as a key teaching resource, and reference for my students, because of the very thorough review of the variety of quantitative designs, and the best overview (in Chapter 1) that I've been able to find on the elements of a quantitative research study.

Dr Tami Moore
School Of Educational Studies, Oklahoma State University
March 16, 2015

One of the best in experimental design, but may be it's complicated for undergards

Mr Mahmoud Ismael
Faculty of Pharmacy, Assiut University
August 18, 2014

Still not decided.

Dr Bal Barot
Science, Lake Michigan Clg-Napier Ave
May 27, 2014

This book is really one of the best books in the market for experimental design, especially for graduate students.

Mr SeyedAlireza Mirbagheri
Management , Sharif University of Technology
April 6, 2014

A complete guide, perfect for graduate students.

Dr Geronimo Maldonado-Martinez
Retrovirus Research Center, Universidad Central del Caribe
January 23, 2014

A lot about variance analysis, less on design of experimentation

Dr Jacques-Bernard Gauthier
Administration, Université du Québec en Outaouais
December 9, 2013

This classic book now improved is essential for a doctorate or master student who wants to carry out thorough investigations.

Dr Moises Betancort
Methodology, La Laguna University
October 10, 2013
Key features

New to the Fourth Edition

  • Includes recommendations on how to report statistical results from the Publication Manual of the American Psychological Association
  • Reorganizes the sequence of chapters so that topics that are likely to be covered in a one-semester course appear in the first half of the book
  • Provides expanded coverage of key areas including exploratory data analysis, measures of practical significance, determination of sample size and power, and one degree-of-freedom measures
  • Describes the latest advances in multiple comparisons along with recommendations for the use of each procedure
  • Improves the procedures for testing the tenability of assumptions in analysis of variance, helping students get to grips with these procedures
  • Demonstrates the flexibility of the cell means model for analyzing data with missing observations and missing cells

Key Features

  • Provides thorough coverage of all the experimental designs that are used in the behavioral and social sciences, making this a must-have, one-stop reference book
  • Includes up-to-date coverage of 16 multiple comparison procedures together with recommendations for the use of each procedure
  • Provides easy to follow computational examples for each experimental design, helping students understand how to analyze the different designs
  • Offers extensive discussion of effect size, sample size determination, and computation of power, making these difficult topics accessible to students
  • Describes and compares the merits of the different models—classical experimental design model, regression model, and cell means model—helping students select the best approach for analyzing data

Sample Materials & Chapters


ch 1 & 2

ch 4

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