EXPERIMENTAL METHODS

The experiment generally regarded as sophisticated research method for testing hypotheses. This method begins with a question concerning the relationship two or more variables. At the same time, the researcher advances one or more hypotheses stating the nature of the expected relationship. The experiment is the event planned and carried out by the researcher to gather evidence relevant to the hypotheses.
It is simplest form an experiment has three characteristics: (1) An independent variable is manipulated, (2) All other variables except the independent variable are held constant, (3) The effect of the manipulation of the independent variable on the dependent variable is observed.
A. Characteristics
There are three in involving the conduct of an experiment:
1. Control
Control is the essence of the experimental method because it is impossible to evaluate unambiguously the effect of an Independent variable. In conclusions, the purpose of control in an experiment is to arrange a situation which is the effect of variables can be investigated.
a. Controlling Intersubject Differences
1. Random assignment
Let us consider the experiment task. There is an available supply of subjects who for experimentation must be divided into two groups that will be treated differently and then compared. To obtain randomized groups, the experimenter could number all the available subjects and then from the table of random numbers draw the needed number for the experimental group of the control group.
2. Randomized matching
It is matching individual subjects on as many extraneous variables as one thinks might affect the dependent variable and then use some random procedure to assign the members of the matched pairs to the experimental conditions. If the groups are adequately matched on these variables, then there is reasonable assurance that any post experimental differences can be attributed to the experimental treatment.
3. Homogeneous selection
It is selecting samples that are as homogenous as possible on that variable. However, it has disadvantages of decreasing the extent to which the finding can be generalized to other situations.
4. Analysis of covariance
It a method for analyzing differences between experimental groups on the dependent variable after taking into account any initial differences between the groups on pretest measures or on any other measures of relevant independent variables. It has limitations and cannot be thought of as a substitute for random assignment.
5. Use of subject as their own controls.
Still another procedure involves assigning the same subjects to all experimental conditions and then obtaining measurements of the subjects first under one experimental treatment and then under the other.
b. Controlling Situational Differences
In addition to intersubject differences, it is also necessary to control any extraneous variables tat might operate in the experimental situation itself. If situational variables are not controlled in an experiment, one cannot be sure whether it is the independent variable or these incidental differences operating in the group that is producing the difference in dependent variable.
2. Manipulation
It is deliberating operation performed by the experimenter. In educational research and other behavioral sciences, the manipulation of a variable takes a characteristics form in which the experimenter imposes a predetermined set of varied conditions on the subjects.
3. Observation
In experimentation we are interested in the effect on the manipulation of the independent variable on a response variable. Observations are made with respects to some characteristics of the behavior of the subjects employed I the research. These observations, which are quantitative in nature possible, are the dependent variable. In observations, we give a score.
B. Experimental Design
1. Pre-Experimental Design
Pre-experimental designs are so named because they follow basic experimental steps but fail to include a control group. In other words, a single group is often studied but no comparison between an equivalent non-treatment group is made. Examples include the following:
The One-Shot Case Study. In this arrangement, subjects are presented with some type of treatment, such as a semester of college work experience, and then the outcome measure is applied, such as college grades. Like all experimental designs, the goal is to determine if the treatment had any effect on the outcome. Without a comparison group, it is impossible to determine if the outcome scores are any higher than they would have been without the treatment. And, without any pre-test scores, it is impossible to determine if any change within the group itself has taken place.
One Group Pretest Posttest Study. A benefit of this design over the previously discussed design is the inclusion of a pretest to determine baseline scores. To use this design in our study of college performance, we could compare college grades prior to gaining the work experience to the grades after completing a semester of work experience. We can now at least state whether a change in the outcome or dependent variable has taken place. What we cannot say is if this change would have occurred even without the application of the treatment or independent variable. It is possible that mere maturation caused the change in grades and not the work experience itself.
The Static Group Comparison Study. This design attempts to make up for the lack of a control group but falls short in relation to showing if a change has occurred. In the static group comparison study, two groups are chosen, one of which receives the treatment and the other does not. A posttest score is then determined to measure the difference, after treatment, between the two groups. As you can see, this study does not include any pre-testing and therefore any difference between the two groups prior to the study are unknown.
2. Quasi-Experimental Design
Quasi designs fair better than pre-experimental studies in that they employ a means to compare groups. They fall short, however on one very important aspect of the experiment: randomization.
a. Pretest Posttest Nonequivalent Group.
With this design, both a control group and an experimental group is compared, however, the groups are chosen and assigned out of convenience rather than through randomization. This might be the method of choice for our study on work experience as it would be difficult to choose students in a college setting at random and place them in specific groups and classes. We might ask students to participate in a one-semester work experience program. We would then measure all of the students’ grades prior to the start of the program and then again after the program. Those students who participated would be our treatment group; those who did not would be our control group.
b. Time Series Designs.
Tim series designs refer to the pretesting and posttesting of one group of subjects at different intervals. The purpose might be to determine long term effect of treatment and therefore the number of pre- and posttests can vary from one each to many. Sometimes there is an interruption between tests in order to assess the strength of treatment over an extended time period. When such a design is employed, the posttest is referred to as follow-up.
c. Nonequivalent Before-After Design.
This design is used when we want to compare two groups that are likely to be different even before the study begins. In other words, if we want to see how a new treatment affects people with different psychological disorders, the disorders themselves would create two or more nonequivalent groups. Once again, the number of pretests and posttests can vary from one each to many.
The obvious concern with all of the quasi-experimental designs results from the method of choosing subjects to participate in the experiment. While we could compare grades and determine if there was a difference between the two groups before and after the study, we could not state that this difference is related to the work experience itself or some other confounding variable. It is certainly possible that those who volunteered for the study were inherently different in terms of motivation from those who did not participate. Whenever subjects are chosen for groups based on convenience rather than randomization, the reason for inclusion in the study itself confounds our results.
3. True Experimental Design
True experimental design makes up for the shortcomings of the two designs previously discussed. They employ both a control group and a means to measure the change that occurs in both groups. In this sense, we attempt to control for all confounding variables, or at least consider their impact, while attempting to determine if the treatment is what truly caused the change. The true experiment is often thought of as the only research method that can adequately measure the cause and effect relationship. Below are some examples:
a. Posttest Equivalent Groups Study.
Randomization and the comparison of both a control and an experimental group are utilized in this type of study. Each group, chosen and assigned at random is presented with either the treatment or some type of control. Posttests are then given to each subject to determine if a difference between the two groups exists. While this is approaching the best method, it falls short in its lack of a pretest measure. It is difficult to determine if the difference apparent at the end of the study is an actual change from the possible difference at the beginning of the study. In other words, randomization does well to mix subjects but it does not completely assure us that this mix is truly creating an equivalency between the two groups.

b. Pretest Posttest Equivalent Groups Study.
Of those discussed, this method is the most effective in terms of demonstrating cause and effect but it is also the most difficult to perform. The pretest posttest equivalent groups design provides for both a control group and a measure of change but also adds a pretest to assess any differences between the groups prior to the study taking place. To apply this design to our work experience study, we would select students from the college at random and then place the chosen students into one of two groups using random assignment. We would then measure the previous semester’s grades for each group to get a mean grade point average. The treatment, or work experience would be applied to one group and a control would be applied to the other.
It is important that the two groups be treated in a similar manner to control for variables such as socialization, so we may allow our control group to participate in some activity such as a softball league while the other group is participating in the work experience program. At the end of the semester, the experiment would end and the next semester’s grades would be gathered and compared. If we found that the change in grades for the experimental group was significantly different than the change in the grades of our control group, we could reasonably argue that one semester of work experience compared to one semester of non-work related activity results in a significant difference in grades.
The experiment, especially the true experimental design is often the measure of choice when attempting to determine a cause and effect relationship. Utilizing randomization and the pre-testing and post-testing of both an experimental group and a control group allows us to control for more confounding variables than any other research method. These confounding variables, when not addressed, can often result in inaccurate findings.
Controlling for confounding variables is important in research and especially important in the experimental designs. This process helps us assure valid results both internally and externally. The threats to internal validity, those that apply to the experimental situation itself, and external validity, those relating to the generalizability of our results to the real world are also issues of great concern to researchers. As the saying goes: garbage in, garbage out. If we start with a flawed design we will end up with flawed results.
As the degree of control for each of the designs discussed increases, the difficulty in performing the research also increases. Feasibility is always an issue and even when the most stringent control is used, the mere fact that the subjects have agreed to participate in the experiment may have a negative effect on the study’s generalizability. Are volunteer subjects truly representative of the population at large? As you can see, there are varying degrees of experimental research, but there is no perfect experiment that controls for all possible variables and assures us of 100% generalizability.