Chapter 9 Glossary

internal validity: the degree to which the study demonstrates that the treatment caused a change in behavior. If a study lacks internal validity, the researcher may falsely believe that a factor causes an effect when it really doesn’t.

       Most studies do not have internal validity because they can’t rule out the possibility that some other factor may have been responsible for the effect. Unfortunately, steps taken to increase internal validity (such as keeping non-treatment factors constant) could harm the study’s external validity. (p. 254)

 

extraneous factors: factors other than the treatment. If we can’t control or account for extraneous variables, we can’t conclude that the treatment had an effect. That is, we will not have internal validity. History, instrumentation, maturation, mortality, regression, testing, selection, and selection by maturation interactions are all potential sources of extraneous variables. (p. 255)

 

maturationinternal, biological changes such as growth, aging, and development. Apparent treatment effects may really be due to maturation. (p. 267)

 

historyexternal, environmental changes—other than the treatment—that might affect participants’ behavior. These outside events can be almost anything—from wars to unusually cold weather. (p. 268)

 

instrumentationthe way participants were measured changed from pretest to posttest. In instrumentation, the actual measuring instrument changes, the way it is administered changes, or the way it is scored changes. (p. 269)

 

testingparticipants score differently on the posttest as a result of what they learned from taking the pretest. Thus, even if the treatment had no effect, scores might be better on the posttest because of the practice participants got on the pretest.

 (p. 269)

 

mortality (attrition): differences between conditions are due to participants dropping out of the study. (p. 265)

 

selectiontreatment and no-treatment groups were different before the treatment was administered.(p. 256)

 

selection by maturation interaction: treatment and no-treatment groups, although similar at one point, would have naturally grown apart (developed differently) even if no treatment had been administered. (p. 260)

 

regression (toward the mean): if participants are chosen because their scores were extreme, these extreme scores may be loaded with extreme amounts of random measurement error. On retesting, participants are bound to get more normal (average) scores as random measurement error’s effects decrease to more normal levels. (p.263)

 

matchingchoosing your groups so that they are similar (they match) on certain characteristics. Matching reduces, but does not eliminate, selection bias. Because of regression and selection by maturation effects, two groups that were matched on the pretest may score differently on the posttest. (p. 257)

 

pretest–posttest design: a before–after design in which each participant is given the pretest, administered the treatment, then given the posttest.

       The pretest–posttest design is not vulnerable to selection and selection by maturation interactions. It is, however, extremely vulnerable to history, maturation, and testing effects. (p. 267)