Internal vs. External Validity | Understanding Differences & Threats
Internal and external validity are two ways of testing cause-and-effect relationships.
Trade-off between internal and external validity
A solution to this trade-off is to conduct the research first in a controlled (artificial) environment to establish the existence of a causal relationship, followed by a field experiment to analyze if the results hold in the real world.
Threats to internal validity
There are eight factors that can threaten the internal validity of your research. They are explained below using the following example:
|History||Unanticipated events change the conditions of the study and influence the outcome.||A new (better) manager starts during the study, which improves job satisfaction.|
|Maturation||The passage of time influences the dependent variable (job satisfaction).||During the six-month experiment, employees become more experienced and better at their jobs. Therefore, job satisfaction may improve.|
|Testing||The pre-test (used to establish a baseline) affects the results of the post-test.||Employees feel the need to be consistent in their answers in the pre-test and post-test.|
|Participant selection||Participants in the control and experimental group differ substantially and can thus not be compared.||Instead of a randomly assigning employees to one of two groups, employees can volunteer to participate in an experiment to improve job satisfaction. The experimental group now consists of more engaged (more satisfied) employees to begin with, but can lead to self-selection bias.|
|Attrition||Over the course of a (longer) study, participants may drop out. If the drop out is caused by the experimental treatment (as opposed to coincidence) it can threaten internal validity and cause attrition bias.||Really dissatisfied employees quit their job during the study. The average job satisfaction will now improve, not because the “treatment” worked, but because the dissatisfied employees are not included in the post-test.|
|Regression to the mean||Extreme scores tend to be closer to the average on a second measurement.||Employees who score extremely low in the first job satisfaction survey probably show greater gain in job satisfaction than employees who scored average.|
|Instrumentation||There is a change in how the dependent variable is measured during the study.||The questionnaire in the post test contains extra questions compared to the one used for the pre-test. This leads to information bias.|
|Social interaction||Interaction between participants from different groups influences the outcome.||The group of employees with fixed working hours are resentful of the group with flexible working hours, and their job satisfaction decreases as a result.|
Threats to external validity
There are three main factors that might threaten the external validity of our study example.
|Testing||Participation in the pre-test influences the reaction to the treatment.||The questionnaire about job satisfaction used in the pre-test triggers employees to start thinking more consciously about their job satisfaction, leading to demand characteristics.|
|Sampling bias||Participants of the study differ substantially from the population.||Employees participating in the experiment are significantly younger than employees in other departments, so the results can’t be generalized.|
|Hawthorne effect||Participants change their behavior because they know they are being studied.||The employees make an extra effort in their jobs and feel greater job satisfaction because they know they are participating in an experiment.|
There are various other threats to external validity that can apply to different kinds of experiments.
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Frequently asked questions about internal and external validity
- What is the difference between internal and external validity?
The validity of your experiment depends on your experimental design.
- What are threats to internal validity?
- What are the two types of external validity?
- What are threats to external validity?
- What is experimental design?
Experimental design means planning a set of procedures to investigate a relationship between variables. To design a controlled experiment, you need:
- A testable hypothesis
- At least one independent variable that can be precisely manipulated
- At least one dependent variable that can be precisely measured
When designing the experiment, you decide:
- How you will manipulate the variable(s)
- How you will control for any potential confounding variables
- How many subjects or samples will be included in the study
- How subjects will be assigned to treatment levels
Experimental design is essential to the internal and external validity of your experiment.
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