• Charecteristics and Types
• Internal and External Validity
• True experimental designs
• Quasi experimental designs
• Evaluative and other designs
• References

TRUE EXPERIMENTAL DESIGNS

A. Overview of true experimental design

The experimental level of research is designed to test theory in laboratory settings or in controlled clinical trials, thus the purpose is to test theory. The greater the degree of control in the research setting, the greater the confidence, that the research findings are accurate. The experimental design the most controlled of all research designs entails manipulation of the independent variables and requires control of all intervening variables. On the basis of theory developed from previous research, each step in the experiment requires a predictive hypothesis regarding the effect of the independent on the dependent variables.

Cause and effect is always predicted based on theory, thus predictive hypotheses are written at the beginning of the study stating the precise nature of both the manipulation of the independent variable and its effects on the independent variables.  All assumptions are spelled out or are either verified by previous research or tested by the current research. All logical steps between cause and effect are specified. Although causal laws can never be proved researchers in experimental designs should be aware of three criteria for causality: a temporal relationship – cause must precede the effect in time; an empirical relationship - there must be an evidence that the independent variable and the dependent variable are associated; a spurious relationship – the relationship cannot be explained by the influence of a third variable. The key issue of the design is internal validity or the assumptions that changes in the dependent variable are actually due to the independent variable.

Experimental and control groups are created by random assignment. Ethical concerns for the protection of human and animal subjects are the most restrictive. Experimenters more than any kind of researchers, are required to establish that all subjects rights are protected to the greatest degree possible and potential harmful effects are counterbalanced by potential benefits. Data collection is quantitative and prospective. Data analysis is designed to discriminate between and among experimental and control groups. Although experimental studies are the most directly applicable to nursing practice because of their controlled samples, they are the least widely generalizable in and of themselves. This is simply due to the controlled narrowness of the study and sample. Experiments are deigned to be repeated on many samples with small variations in the independent variable over time.

A single experiment on a single small sample adds to the test of a part of the theory but not of the entire theory. Many experiments with different samples may be required to increase generalizability to the point where findings can be widely applied in practice. Thus the true experiment is regarded as the cornerstone of scientific research, the most powerful strategy for testing causal hypothesis and achieving the four criteria namely: establishing causal relationships; manipulating independent variable; measuring the impact of independent variable on the dependent variable; minimizing or accounting for the effects of factors other than the independent variable on the dependent variable.

B. Types of true experimental designs:

A number of different experimental designs meet the criteria for true experiments. These include but are not limited to the classical experimental designs, the factorial design, the multiple treatment groups-repeated measures design and the Solomon four group design.

1. The classical experimental design: Kothari describes them as formal designs either with or without a control group namely before and after without control design, before and after with control design. Similarly post test only design with control grou.p

a. pretest-posttest design/ before-after design: All experimental groups are variations on the basic classical experimental designs which consist of two groups, an experimental and control group and two variables, an independent and dependent variable. Units to be analyzed are randomly assigned to each of the experimental and control groups. Units in the experimental groups receive the independent variable that the investigator has manipulated and participants in the control group do not receive the independent variable treatment. Pretest and post test measures are taken on the independent variables and the control group participants are measured at the same time as the experimental group although no planned change or manipulation has taken place with regard to the independent variable in the control group. Researchers use this design when they are interested in assessing the change from the pretest to the posttest as a result of a treatment or intervention.

R   O1   X   O2   

R  O1         O2

 (R: Random assignment, X: Treatment, O: Observation)

b. Post test only design:  One group receives a treatment whereas the other group receives no treatment and serves as control. The key difference in the post test only design is that, neither group are pretested and only at the end of the study are both groups measured on the dependent variable. Some researchers favor this design because they are concerned that the pretest measures will sensitize participants or that a learning effect might take place that influences individuals’ performance on the posttest.

R    X    O2 

R          O2   

(R: Random assignment, X: Treatment, O: Observation)   

c.  N=1 True experiments or single subject design: The sample is one subject, who is exposed to two or more treatments on various occasion. It may involve before and after designs. The design gives the researcher opportunity to focus on an individual and therefore pay more attention to details.  It is particularly suitable to the principle of patient centered care, since the interaction between the individual and the treatment is unique, although lessons learned can be applied to other cases as well. But findings can’t be generalized.

2. Factorial designs: Classical experimental designs allow the manipulation of a single variable at a time, holding all other conditions constant. Factorial designs permit the manipulation of more than one independent variable at a time. Furthermore, interaction effects between variables are revealed and the simultaneous testing of multiple hypotheses is thus allowed. The researcher can study the effects of each of the variables (known as main effects) and the interactions between them or their joint effects on the independent variable.

Though commonly used design is 2 x 2 factorial design, experiments can be conducted with any number of categories ad independent variables. Each factor must have two or more levels thus when new factors are added the analysis becomes more complex. E.g. 4 X 5 design means that one of the factors has four categories and the other has five. These designs can also be extended to have more than two factors as in 3 x 2 x 4 factorial design, which means that there are three factors consisting of three, two and four categories respectively.

Factorial designs are also referred to as “levels of treatment” designs. When one of the factors cannot be manipulated, such as sex or race it is referred to as blocking variable. The design that incorporates the blocking variable is known as the randomized block design. Stratified randomization is often used in this case to ensure that approximately equal numbers of participants within categories are randomly assigned to the various groups.

3. Multiple Treatment Groups- repeated measures design: This design uses several experimental groups each of which receives a different treatment. Control is achieved through comparison among groups and from measures taken on the dependent variable for all groups at time 1 and time 2. It is always not true that a control group will be used that has no treatment at all. There can also be more than one pretest and posttest measure for the dependent variable for all groups.

4. Solomon Four Group Design: This is a rigorous design by controlling effects on the dependent variable that may be due to factors other than the independent variable. It is essentially a combination of pretest-posttest and posttest only designs. This design requires a large group of homogeneous subjects to make up the four groups, thus is used less often than other designs. It is difficult to introduce the treatment simultaneously for all groups, in order to avoid extraneous temporal effects and statistical analysis complex. The design consists of four groups of which two are control and two are experimental. Pretest and posttest are carried out with one control and one experimental group only and post tests only for the other control and experimental group.

Exp. Group 1	O1	X	O2
Control Group1	O2	-	O4
Exp. Group 2	--	X	O5
Control Group2	--	-	O6

C. Strengths and weaknesses of the design:

1. Strengths: Ability to diminish bias; permit the researcher to maximize systematic variance and to control the extraneous and error variance; control the threats to validity; degree of confidence they allow the researcher in inferring causal relationships, because of high internal validity

2. Limitations: the number of potentially interesting  research variables that are not within researcher’s purview to manipulate; ethical concerns; organizational prohibitions; human characteristics often interfere with the researcher’s ability to control a number of variables experimentally; sample representativeness/ homogeneous sample; many cases population is unknown; requires many replications to achieve generalizations; artificiality in laboratory testing; Hawthorne effect; many of the phenomena to be tested lack theoretical background which could be either exploratory or descriptive studies than experimental; issues related to the outcome measure like relevant outcome variables, validity, reliability and systematic bias.