In research, internal validity refers to the quality of a cause-and-effect correlation not being attributed by factors unrelated to the study. Internal validity creates credibility to the study and makes the results reliable. There are certain threats that can occur to internal validity which can be categorized based on whether the study is single-group or multi-group. This essay will focus on examining these threats and ways in which they can be prevented in a study. Single-group studies may face history effect, maturation effect, instrumentation and testing effect threats to internal validity. While multi-group studies face selection bias, regression to mean, attrition and order effect as threats to internal validity.
History effect refers to study unrelated developments in time that systematically effects most of the participants in the group and has significant influence on the outcome of the study. Maturation effect is the natural recovery in time that creates varying outcomes in different periods of the study. Testing effect describes the improvement in outcome due to being subjected to the same pretest and posttest. Threats due to instrumentation occur when there are changes in measures used in the pretest and posttest. History and maturation effect can be prevented by random assignment using comparable control group so both experimental and control groups can be subjected to the same conditions. Testing effects can be countered by using alternate dependent variable while instrumentation can be countered by consistency in measures in pretest and posttest.
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Regression to mean refers to the tendency for experimental group average to statistically perform towards the mean with time. Attrition on the other hand is the dropping out of participants from the study creating a systematically different experimental group. Regression to mean is often countered by use of random assignment which also prevents selection bias, imputation can effectively counter the effects of attrition. Other threats to a multi-group study can be effectively mitigated by altering the design of the experiment.
