Research methods in psychology by B. Morling (third edition) – Chapter 13 summary

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:

Threat	Definition
Selection effect	The participants of one level of the independent variable are systematically different from other participants at another level of the independent variable.
Design confounds	In a design confound, some outside variable systematically varies the levels of the targeted independent variable.
Maturation threat	An observed change has emerged more or less spontaneously over time.
History threat	An external, historical event happens for everyone in a study at the same time as the treatment (e.g: a change of seasons).
Regression to the mean	A measure is extreme and will thus (almost) always be less extreme and more closely to the mean on the next measurement.
Attrition threat	Certain kinds of participants drop out systematically (e.g: only the most depressed people drop out).
Testing threat	A testing threat is an order effect in which participants change as a result of having been tested before (e.g: becoming better at something).
Instrumentation threat	A measuring instrument could change over repeated trials. The first measure is not the same as the second.
Observer bias	The experimenter’s expectations influence their interpretation of the results. To solve this problem, the research has to be a double blind design.
Demand characteristics	Participants guess what the study is about and change their behaviour accordingly. To solve this problem, participants mustn’t be able to discover the true aim of the research or the researchers have to respond accordingly.
Placebo effects	The participants’ condition improves, but only because they believe they are receiving an effective treatment. To solve this problem, a research design requires a comparison group that received a placebo treatment.

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:

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.
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.
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.

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