In the study of interventions, experimental designs provide a structure for evaluating the cause-and-effect relationship between a set of independent and dependent variables. In explanatory and pragmatic trials, the researcher manipulates the levels of the independent variable within the design to incorporate elements of control so that the evidence supporting a causal relationship can be interpreted with confidence.
The purpose of this chapter is to present basic configurations for experimental designs and to illustrate the types of research situations for which they are most appropriate. For each design, the discussion includes strengths and weaknesses in terms of internal and external validity, and the most commonly used statistical procedures are identified, all of which are covered in succeeding chapters. This information demonstrates the intrinsic relationship between analysis and design.
Although experimental designs can take on a wide variety of configurations, the important principles can be illustrated using a few basic structures. A basic distinction among them is the degree to which a design offers experimental control.1,2 In a true experiment, participants are randomly assigned to at least two comparison groups. An experimental design is theoretically able to exert control over most threats to internal validity, providing the strongest evidence for causal relationships.
Experimental designs may be differentiated according to how subjects are assigned to groups. In completely randomized designs, also referred to as between-subjects designs, subjects are assigned to independent groups using a randomization procedure. A design in which subjects act as their own control is called a within-subjects design or a repeated measures design because treatment effects are associated with differences observed within a subject across treatment conditions, rather than between subjects across randomized groups.
Designs are not considered true experiments if they do not include random assignment or control groups. When either of these conditions is not met, quasi-experimental designs can be used. These will be described in Chapter 17.
Number of Independent Variables
Experimental designs can also be described according to the number of independent variables, or factors, within the design. A single-factor design has one independent variable with any number of levels. Also called a one-way design, such a study may include one or more dependent variables. Multi-factor designs contain two or more independent variables.
Once a research question is formulated, the researcher must decide on the most effective design for answering it. Although experimental designs represent the highest standard in scientific inquiry for establishing a causal relationship between independent and dependent variables, they are not necessarily the best choice in every situation. When the independent variable cannot be manipulated by the experimenter, or when important extraneous factors cannot be controlled, an observational design may be more feasible (see ...