It is often easier to impose this sort of control in a laboratory setting. Thus, true experiments have often been erroneously identified as laboratory studies" (Woolf, 2012). True experiments rigidly control for validity by attempting to isolate variables so that only a single independent variable is studies. The independent variable "is the variable that the experimenter manipulates in a study. It can be any aspect of the environment that is empirically investigated for the purpose of examining its influence on the dependent variable" (Woolf, 2012). Furthermore in true experiments, the subjects are randomly assigned to the experimental and control groups. Finally, true experiments are double blind, which means that neither the experimenter nor the subjects know whether the subjects are in the experimental or control groups (Woolf, 2012). True experiments differ from experimental designs in the level of control that exists in each different type of research. An experimental design, like a true experiment, attempts to determine cause and effect relationships. They include randomization, the use of a control group, and manipulation of the independent variable. Therefore, true experiments could properly be classified as a subgroup of experimental designs. However, not all experimental designs are true experiments; the hallmark of the true experiment is the manipulation of the independent characteristic of a true experiment.

4. What are quasi-experimental designs? Why are they important? How are they different from experimental designs?

Quasi-experiments are similar to true experiments, but lack the randomization characteristic that describes the true experiment. In other words, quasi-experiments "use naturally formed or pre-existing groups. For example, if we wanted to compare young and old subjects on lung capacity, it is impossible to randomly assign subjects to either the young or old group (naturally formed groups). Therefore, this cannot be a true experiment. When one has naturally formed groups, the variable under study is a subject variable (in this case - age) as opposed to an independent variable. As such, it also limits the conclusions we can draw from such a research study" (Woolf, 2012)....

When groups are not randomly selected, there are a number of between-group differences that might account for differences.
Quasi-experimental designs are important because it is impossible to randomly assign people to some groups, and those groups may be the important groups that researchers are trying to study. Do people with lupus respond to a particular anti-depressant in the same way as people without lupus? Do Black people respond to a type of therapy in the same why as white people? Do elderly people have the same mental capacity as young people? In each example, the non-randomized grouping is a part of the material being investigated, and is also beyond the researcher's manipulation. That is not only how quasi-experimental designs differ from experimental designs, but also why they are important avenue of research.

Conclusion

While many people may think of the true experiment as the only means of experimentation, there are a number of different ways to correlate different events. This paper discussed various means of collecting and presenting data, allowing either the researcher or the audience to draw conclusions about the information presented. The overview of descriptive and inferential statistics; single case and small N. research designs; true experiments and experimental designs; and qausi-experiments makes it clear that experimental research is about more than the widely accepted scientific-method inspired true experiment.