Internal and external views of Testing
Inferences are said to possess internal validity if a causal relation between two variables is properly demonstrated. A causal inference may be based on a relation when three criteria are satisfied:
1. the "cause" precedes the "effect" in time (temporal precedence),
2. the "cause" and the "effect" are related (covariation), and
3. there are no plausible alternative explanations for the observed covariation (nonspuriousness)
In scientific experimental settings, researchers often manipulate a variable (the independent variable) to see what effect it has on a second variable (the dependent variable)] For example, a researcher might, for different experimental groups, manipulate the dosage of a particular drug between groups to see what effect it has on health. In this example, the researcher wants to make a causal inference, namely, that different doses of the drug may be held responsible for observed changes or differences. When the researcher may confidently attribute the observed changes or differences in the dependent variable to the independent variable, and when he can rule out other explanations (or rival hypotheses), then his causal inference is said to be internally valid
In many cases, however, the magnitude of effects found in the dependent variable may not just depend on
· variations in the independent variable,
· the power of the instruments and statistical procedures used to measure and detect the effects, and
· the choice of statistical methods (see: Statistical conclusion validity).
Rather, a number of variables or circumstances uncontrolled for (or uncontrollable) may lead to additional or alternative explanations (a) for the effects found and/or (b) for the magnitude of the effects found. Internal validity, therefore, is more a matter of degree than of either-or, and that is exactly why research designs other than true experiments may also yield results with a high degree of internal validity.
In order to allow for inferences with a high degree of internal validity, precautions may be taken during the design of the scientific study. As a rule of thumb, conclusions based on correlations or associations may only allow for lesser degrees of internal validity than conclusions drawn on the basis of direct manipulation of the independent variable. And, when viewed only from the perspective of Internal Validity, highly controlled true experimental designs (i.e. with random selection, random assignment to either the control or experimental groups, reliable instruments, reliable manipulation processes, and safeguards against confounding factors) may be the "gold standard" of scientific research. By contrast, however, the very strategies employed to control these factors may also limit the generalizability or External Validity of the findings.
External validity is the validity of generalized (causal) inferences in scientific research, usually based on experiments as experimental validity. In other words, it is the extent to which the results of a study can be generalized to other situations and to other people For example, inferences based on comparative psychotherapy studies often employ specific samples (e.g. volunteers, highly depressed, no comorbidity).
If psychotherapy is found effective for these sample patients, will it also be effective for non-volunteers or the mildly depressed or patients with concurrent other disorders?
· Situation: All situational specifics (e.g. treatment conditions, time, location, lighting, noise, treatment administration, investigator, timing, scope and extent of measurement, etc. etc.) of a study potentially limit generalizability.
· Pre-test effects: If cause-effect relationships can only be found when pre-tests are carried out, then this also limits the generality of the findings.
· Post-test effects: If cause-effect relationships can only be found when post-tests are carried out, then this also limits the generality of the findings.
· Reactivity (placebo, novelty, and Hawthorne effects): If cause-effect relationships are found they might not be generalizable to other settings or situations if the effects found only occurred as an effect of studying the situation.
· Rosenthal effects: Inferences about cause-consequence relationships may not be generalizable to other investigators or researchers.