What is internal validity in research?

A brief introduction to internal validity

For more best practices see our method overview

Content

Definition of internal validity

The degree of internal validity is defined as the answer to the question “did in fact the experimental stimulus make some significant difference in this specific instance?” (Campbell, 1957)Internal validity builds on top of statistical conclusion validity and determines the strength of the inference of causality from the observed correlation (Shadish et al., 2002).

The other dimensions of research rigor that correspond to internal validity are:

On our page on rigor you can get an overview for judging whether these are the dimensions to evaluate for your research or if (for qualitative research) you should use framework of trustworthiness instead.

Threats to internal validity

Threats to internal validity are any effects that might be responsible for the observed correlation other than the cause-effect relationship between the independent and the dependent variable.
The following table presents common threats to internal validity and their consequence if not mitigated.

Threat Ambiguous temporal precedence	Consequence Uncertainty, which variable is cause and which is effect.
Threat History	Consequence Confounding factors due to concurrent external events.
Threat Maturation	Consequence Confounding factors due to natural changes.
Threat Regression artifacts	Consequence Regression to the mean may be confused with a treatment effect for previously extreme measurements.
Threat Attrition	Consequence Participants dropping out may skew results if the dropout rate is correlated with the observed effects.
Threat Testing effects	Consequence Results of a test may influence participants’ test in future exposure to the same test, independently of treatment.
Threat Changing instrumentation	Consequence Confounding factors due to changes in the measurement instruments.

Strategies to improve internal validity.

To maintain a high level of internal validity you should carefully consider the following strategies:

Randomization: Assigning participants to different groups randomly helps minimize selection bias, ensuring that groups are comparable from the start.
Control Groups: Including control groups that do not receive the experimental treatment provides a baseline for comparison.
Counterbalancing: Randomly altering the order of conditions in repeated measures designs helps minimize the impact of order effects.
Matching: Ensuring that groups are matched based on specific characteristics before the experiment reduces the likelihood of confounding variables affecting results.
Standardization: Implementing standardized procedures across all conditions of the experiment reduces the influence of extraneous variables.
Pre-testing and Post-testing: Conducting pre-tests before the experiment allows researchers to assess initial differences between groups, while post-tests help measure changes over time.

Conclusion on internal validity.

Internal validity is not just a technicality in research design; it is the linchpin that ensures the accuracy and reliability of research findings. Without strong internal validity, the conclusions drawn from your study may be inaccurate or misleading, potentially leading to misguided decisions or misguided advancements in a field. You must be vigilant in identifying and addressing threats to internal validity, employing robust methodologies and strategies to strengthen the credibility of your work. As the bedrock of rigorous research, internal validity safeguards the integrity of scientific progress and contributes to the building of a solid knowledge foundation.

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