Abstract
The ability to draw valid inferences from data is the cornerstone of research and provides the basis for understanding the new knowledge that research results represent.
Internal validity reflects the extent to which a manipulated variable can be shown to account for changes in a dependent variable. It is indispensable for interpreting the experiment.
Ten common threats to internal validity include selection bias, history effects, maturation effects, testing effects, instrumentation effects, statistical regression, experimental mortality, interaction of these factors, experimenter bias, and subject expectancy effects.
Four threats to external validity (generalizability) are reactive effects of testing, interactive effects of selection and treatment, reactive effects of experimental arrangements, and multiple treatment interference. A variety of research designs can be used to evaluate interventions. Each differs in its adequacy for ensuring that valid inferences are made about effects and generalizability.
The poorest for controlling threats to internal validity are termed “pre-experimental designs.” These lack adequate control groups. The strongest are termed “true-experimental designs.” They incorporate control groups to which subjects have been randomly allocated but may suffer from lack of generalizability. Quasi-experimental designs represent a good compromise when randomization is not possible.
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Supino, P.G. (2012). Fundamental Issues in Evaluating the Impact of Interventions: Sources and Control of Bias. In: Supino, P., Borer, J. (eds) Principles of Research Methodology. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3360-6_5
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