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Treats to Internal validity. Internal validity is a form of experimental validity . An experiment is said to possess internal validity if it properly demonstrates a causal relation between two variables

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Treats to Internal validity

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Treats to Internal validity

Internal validity is a form of experimental validity . An experiment is said to possess internal validity if it properly demonstrates a causal relation between two variables

An experiment can demonstrate a causal relation by satisfying three criteria:

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 experiments, 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 manipulate the dosage of a particular drug to see what effect it has on a person's health. If the experiment allows the researcher to conclude that different doses of the drug caused a change in peoples' health (by satisfying the above criteria), then the study possesses internal validity. In other words, an experiment possesses internal validity if the observed changes in the dependent variable were caused by the manipulation of the independent variable.

Internal validity

Internal validity is, quite simply, that your experimental treatment, and only the experimental treatment, was responsible for the outcome. Treats are:

1.- History - events that might influence or bias the outcome (E.g., you are studying a behavior modification program for diabetics, and JAMA reports a cure for diabetes that is reported on television.)


Events outside of the experiment or between repeated measures of the dependent variable may have effects on the experiment’s validity.

2.- Maturation - changes in the participant (getting older, tired, hungry, etc.).

3.- Testing - the effect of one test on a second test (For example, if you do a glucose tolerance test before starting the behavior modification program, the fact that they have been tested may cause them to become more cooperative.)


Subjects change during the course of the experiment or even between measurements. Permanent ones, such as physical growth and temporary ones like fatigue, may change the way a subject would react to the dependent variable. So upon completion of the study, the researcher may not be able to determine if the cause of the discrepancy is due to time or the independent variable.

Repeated testing

Repeatedly measuring the participants may lead to bias. Participants may remember the correct answers or may be conditioned to know that they are being tested.


The instrument used during in the testing process can change the experiment. If this occurs, an instrumentation error occurs and internal validity is effected.

4.- Instrumentation - Systematic changes in the accuracy of a test may produce a spurious effect.

5.- Statistical regression - can occur where subjects are selected on the basis of extreme scores (Take this one on faith. Extreme scores tend to move towards the average on a second testing occasion without anything being done to the subjects in the meantime.)

Regression to the mean

This type of error occurs when subjects receive repeated testing, have an extreme score (one far away from the mean) during the first test but score closer to the mean with the second test. This is called regression to the mean and occurs due to a regression artifact.

6.- Experimental mortality - as when subjects drop out of the experiment.

7.- Selection-maturation interaction (and other interactions) - the same maturation factor may effect different persons differently (e.g., time since the last meal tends to affect diabetic patients more than non-diabetic persons).

Experimental mortality

This error occurs if participants drop out before the completion of the experiment.

Selection-maturation interaction

This occurs when the subject-related variables, color of hair, skin color, etc., and the time-related variables, age, physical size, etc., interact. In the fruit experiment, the ages of the children in one school is 4-12 and the other 4-9 years old. If a discrepancy between the two groups occurs between the testing, the discrepancy may be due to the age differences in the age categories.

External Validity

External validity refers to the degree to which your findings may be applicable in everyday use, outside of the experimental context

1.- The reactive or interactive effects of testing - where you are trying to generalize to a population that will not have the same test. (E.g., you administer a questionnaire prior to your experiment, and the subjects become sensitized and more cooperative than the general population would be.)

2.- The interaction of selection and the experimental variable - where your groups are not equivalent, either due to inadequate randomization or to subjects dropping out.

3.- Reactive effects of experimental arrangements - where the experimental conditions are significantly different from the "natural" conditions.

4.- Multiple-treatment interference - which can occur, for example, where subjects serve as their own controls, or when multiple treatments are tried on the same patients. (I.e., multiple treatments usually tend not to occur in the "real world", at least not in the same way.)


First and foremost, learn to think of the people who participate in your experiment, control group(s) as well as experimental group(s) as representatives.

They represent persons who may receive the new treatment or procedure in the future, if your experiment yields promising results. Persons in the control group as well as the experimental group must be equally likely candidates, and, to be really correct, the persons who participate in your experiment should be drawn randomly from all persons who could conceivably be candidates for the new treatment or procedure.

(This last condition is rarely feasible, but you need to be aware of it.) Drawing from the total population of all possible candidates is random selection. Once a pool of participants is randomly selected, they are randomly assigned to experimental or control group(s).

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