An experiment is a scientific study designed to uncover information about cause and effect through examining the influence of changes in some variable or condition on a measured outcome. In a true experiment, changes in one or more independent variables are tested and the resulting effects on one or more dependent variables are assessed. For example, researchers interested in whether training strategies can help children’s memory performance might design an experiment in which they manipulate the type of strategy that is trained and then measure the resulting effects of that manipulation on an outcome measure, such as accuracy of memory performance after the training.

Experiments require some comparison of the effects of the independent variable on the dependent variable. Often one of those treatments functions as a control condition. The control condition might be one in which no effect is expected. For example, if studying training strategies for memory performance in children, one might compare a group of children who received explicit training on a strategy with a group of children who received no training. The adequacy of the control condition is important to evaluate. In this example, perhaps a better control condition would be a group of children who received some other treatment that had nothing to do with memory strategy, but gave them the same time and attention that the memory strategy group received.

In an experiment, an independent variable may be a variable that the researcher manipulates, such as the comparison of various treatment conditions. An independent variable may also be a group difference that the researcher intends to investigate, such as gender or age. These variables are sometimes called quasi-independent variables because, although they cannot be randomly assigned, they function as independent variables in the design of the experiment.

The selection of the sample to be tested is an important feature of an experiment. If two or more treatment conditions are to be tested in different groups of people, one strategy used in order to avoid bias is to consider the entire group to be tested and then randomly assign participants from that group to one of the treatment conditions. Any resulting differences between the groups in the outcome of the study can then be attributed to the treatment differences, although it should be noted that there are always potential sources of error in an experiment, such as differences in the groups before the treatment, despite random assignment, or measurement error in the dependent variable.

Nonetheless, random assignment to conditions is a good strategy for experimental designs.

The adequacy of the dependent variable is another important factor in experiments. Does the dependent variable measure what the research intended it to measure, that is, does it have validity? And does the dependent variable have reliability, that is, is it a consistent measure of what the researcher intended it to measure?

There are various kinds of experimental designs. For example, an experiment may use comparisons between people in different groups that have been tested with different treatments. This is a between subjects  design. Another  type  of  experiment  might test one group of people in several conditions and compare the effects of conditions. This is a within-subjects design. In research on human development, some studies test the same group of people at different ages in order to assess changes that occur with age. This is a longitudinal design. Other studies test different age groups in order to draw inferences about changes that occur with age. This is a cross-sectional design.

The opportunity to draw inferences about cause and effect is important in scientific reasoning. Some studies are not true experiments because they do not measure such cause-and-effect influences of independent variables on dependent variables. For example, suppose that a group of high school students’ participation in extracurricular activities is measured, and data from those students are split into two groups, based on whether the student’s grade point average (GPA) was high or low. Perhaps the number of extracurricular  activities  would  be  greater  for  the high-GPA than for the low-GPA group. Would it be correct to conclude that GPA caused differences in extracurricular activity? No. In this hypothetical example, we can describe a relation between GPA and extracurricular participation, but cannot demonstrate causality. Such studies may be legitimate and informative, but they are not true experiments.

Experiments with human participants and experiments with animal participants are subject to ethical guidelines.


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