Editing Internal validity (section)

== Details ==
Inferences are said to possess internal validity if a causal relationship between two [[Variable and attribute (research)|variables]] is properly demonstrated.<ref>Brewer, M. (2000). Research Design and Issues of Validity. In Reis, H. and Judd, C. (eds.) Handbook of Research Methods in Social and Personality Psychology. Cambridge:Cambridge University Press.</ref><ref name=Shadish>Shadish, W., Cook, T., and Campbell, D. (2002). Experimental and Quasi-Experimental Designs for Generilized Causal Inference Boston:Houghton Mifflin.</ref> 
A valid [[causal inference]] may be made when three criteria are satisfied: 
# the "cause" precedes the "effect" in time (temporal precedence),
# the "cause" and the "effect" tend to occur together (covariation), and
# there are no plausible alternative explanations for the observed covariation (nonspuriousness).<ref name=Shadish/>

In scientific experimental settings, researchers often change the state of one variable (the [[independent variable]]) to see what effect it has on a second variable (the [[dependent variable]]).<ref>Levine, G. and Parkinson, S. (1994). Experimental Methods in Psychology. Hillsdale, NJ:Lawrence Erlbaum.</ref> For example, a researcher might manipulate the dosage of a particular drug between different groups of people 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 (that is, when the researcher observes an association between these variables and can rule out other explanations or ''rival hypotheses''), then the causal inference is said to be internally valid.<ref>Liebert, R. M. & Liebert, L. L. (1995). Science and behavior: An introduction to methods of psychological research. Englewood Cliffs, NJ: Prentice Hall.</ref>

In many cases, however, the [[Effect size|size of effects]] found in the dependent variable may not just depend on 
* variations in the independent variable,
* the [[Statistical power|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 study. As a rule of thumb, conclusions based on direct manipulation of the independent variable allow for greater internal validity than conclusions based on an association observed without manipulation. 

When considering only 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. However, the very methods used to increase internal validity may also limit the generalizability or [[external validity]] of the findings. For example, studying the behavior of animals in a zoo may make it easier to draw valid causal inferences within that context, but these inferences may not generalize to the behavior of animals in the wild. In general, a typical experiment in a laboratory, studying a particular process, may leave out many variables that normally strongly affect that process in nature.