QUASI-EXPERIMENTS
A quasi-experiment differs from a true experiment in that the researchers do not have full experimental control. In quasi-experiments, researchers might not be able to randomly assign participants to one level or the other. They are assigned by other things (e.g: teachers, political regulations or nature).

A non-equivalent control group design is a quasi-experiment in which there is a treatment group and a control group, but the participants have not been randomly assigned. A non-equivalent control group pretest/posttest design is a quasi-experiment in which participants are tested before and after the experiment, but are not randomly assigned to groups. An interrupted time-series design is a quasi-experiment that measures participants repeatedly on a dependent variable. A non-equivalent control group interrupted time-series design is a quasi-experiment in which the independent variable was studied as a repeated-measures variable and an independent groups variable.

There are several possible threats in quasi-experiments to internal validity:

BALANCING PRIORITIES IN QUASI-EXPERIMENTS
Quasi-experiments can be used, because it is interesting to study rare phenomena and important events. Sometimes this cannot use random assignments to group and thus quasi-experiments are a good option. Quasi-experiments can also enhance external validity, because the real-world settings increases the likelihood of observing the same patterns in real life.  A quasi-experiment can also be used, because it would otherwise be unethical to study something. The difference between quasi-experiments and correlational study is that researchers conducting quasi-experiments actively select groups for an independent variable and researchers in a correlational study simply measure variables in a sample.

SMALL-N DESIGNS: STUDYING ONLY A FEW INDIVIDUALS
Large-N designs group the participants together. The data is presented as group averages. In small-N designs, each participant is treated separately and data for each individual are presented. Small N-designs are used because a condition is very rare or because the population is homogeneous. The problems of small N-designs are that the external and internal validity are sometimes poor.

There are three general types of small-N designs:

1. Stable-baseline design
   This is a study in which the researcher observes behaviour for an extended baseline period before beginning a treatment or other intervention.
2. Multiple-baseline design
   This is a study in which the researcher observed behaviour of multiple individuals for an extended period of time in order to get a baseline of several individuals before beginning treatment or another intervention.
3. Reversal design
   This is a study in which the treatment is taken away for a while, to see whether the problem behaviour returns. After this the treatment is returned again to see if this causes any improvement.