Each of the four types of validity will be briefly defined and described below. Be aware that this represents a cursory discussion of the concept of validity. Each type of validity has many threats which can pose a problem in a research study. Examples, but not an exhaustive discussion, of threats to each validity will be provided. For a comprehensive discussion of the four types of validity, the threats associated with each type of validity, and additional validity issues see Cook and Campbell (1979).
Statistical Conclusion Validity: Unfortunately, without a background in basic statistics, this type of validity is difficult to understand. According to Cook and Campbell (1979), "statistical conclusion validity refers to inferences about whether it is reasonable to presume covariation given a specified alpha level and the obtained variances (p. 41)." Essentially, the question that is being asked is - "Are the variables under study related?" or "Is variable A correlated (does it covary) with Variable B?". If a study has good statistical conclusion validity, we should be relatively certain that the answer to these questions is "yes". Examples of issues or problems that would threaten statistical conclusion validity would be random heterogeneity of the research subjects (the subjects represent a diverse group - this increases statistical error) and small sample size (more difficult to find meaningful relationships with a small number of subjects). This form of validity will be addressed more throughout the text.
Internal Validity: Once it has been determined that the two variables (A & B) are related, the next issue to be determined is one of causality. Does A cause B? If a study is lacking internal validity, one can not make cause and effect statements based on the research; the study would be descriptive but not causal. There are many potential threats to internal validity. For example, if a study has a pretest, an experimental treatment, and a follow-up posttest, history is a threat to internal validity. If a difference is found between the pretest and posttest, it might be due to the experimental treatment but it might also be due to any other event that subjects experienced between the two times of testing (for example, a historical event, a change in weather, etc.).
Construct Validity: One is examining the issue of construct validity when one is asking the questions "Am I really measuring the construct that I want to study?" or "Is my study confounded (Am I confusing constructs)?". For example, if I want to know a particular drug (Variable A) will be effective for treating depression (Variable B) , I will need at least one measure of depression. If that measure does not truly reflect depression levels but rather anxiety levels (Confounding Variable X), than my study will be lacking construct validity. Thus, good construct validity means the we will be relatively sure that Construct A is related to Construct B and that this is possibly a causal relationship. Examples of other threats to construct validity include subjects apprehension about being evaluated, hypothesis guessing on the part of subjects, and bias introduced in a study by expectencies on the part of the experimenter.
External Validity: External validity addresses the issue of being able to generalize the results of your study to other times, places, and persons. For example, if you conduct a study looking at heart disease in men, can these results be generalized to women? Therefore, one needs to ask the following questions to determine if a threat to the external validity exists: "Would I find these same results with a difference sample?", "Would I get these same results if I conducted my study in a different setting?", and "Would I get these same results if I had conducted this study in the past or if I redo this study in the future?" If I can not answer "yes" to each of these questions, then the external validity of my study is threatened.
Threats to Validity
Statistical Conclusion Validity
Internal Validity
Construct Validity
External Validity