Chapter

Experimental Design

Research Methods in Psychology

I. FOUNDATIONS OF RESEARCH ISSUES

  1. Roger E. Kirk PhD

Published Online: 26 SEP 2012

DOI: 10.1002/9781118133880.hop202001

Handbook of Psychology, Second Edition

Handbook of Psychology, Second Edition

How to Cite

Kirk, R. E. 2012. Experimental Design. Handbook of Psychology, Second Edition. 2:I:1.

Author Information

  1. Baylor University, Department of Psychology and Neuroscience, Waco, Texas, USA

Publication History

  1. Published Online: 26 SEP 2012

Abstract

The chapter discusses the three principles of good experimental design-random assignment, replication, and local control-that were championed by Ronald A. Fisher. The chapter also shows how complex experimental designs are constructed from and understood in terms of three simple building block designs: the completely randomized design, randomized block design, and Latin square design. Missing observations and missing cells are common in the behavioral sciences and education. Procedures for dealing with unequal cell n's in randomized block designs and completely randomized factorial designs are illustrated using a regression model and a cell means model. The merits of the two models and the classical model are examined. Researchers sometimes use confounding to reduce the size of blocks or to reduce the number of treatment combinations in an experiment. The advantages and disadvantages of group-treatment confounding in split-plot factorial designs, group-interaction confounding in confounded factorial designs, and treatment-interaction confounding in fractional designs are examined. The chapter discusses a variety of analysis of covariance designs. These designs enable a researcher to (a) remove that portion of the dependent-variable error variance that is predictable from a knowledge of the concomitant variable thereby increasing power and (b) reduce bias by adjusting the dependent variable so that it is free of effects attributable to the concomitant variable.

Keywords:

  • experimental design;
  • analysis of variance;
  • design of experiments;
  • cell means model