Understanding validity can be something of a foreign concept for many, especially those who pay little attention to or do not conduct research and the methods used to carry them out. In simple terms, validity is the reliability of the study. And knowing the difference between internal vs external validity is crucial.
A good example would be a test of strength is measuring just that, strength, and nothing else (such as endurance or stamina). For the test itself to be valid, it should be limited only to strength.
Within any level of research, there are two goals the researcher is working to achieve. Measure a specific constant or variable through a subject group and take the results of that testing and use it to conclude a broader group at large.
The initial goal, relating directly to the specific test group is internal validity. The second goal, which moves to translate the results to a larger population or setting, is external validity.
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Before going any further on internal vs external validity, we will need to define a handful of terms to relate better the internal vs external validity and how they work. There are subsets to each and when research is conducted understanding the connection between the variables at work will better explain the overall objectives.
- Internal Validity: Reflects if the conducted research or study is sound.
- Independent Variable: An item within the research or study that is unaffected by outside elements.
- Dependent Variable: An item within research that is impacted or “depends” on an outside factor.
- Confounding Variable: An item within research or a study that is unaccounted for.
- External Validity: Reflects if the information from the conducted research can be applied to larger sample size or setting or can be generalized.
- Ecological Validity: Application of the study results to places or settings.
- Historical Validity: Application of the study results to a time frame.
- Population Validity: Application of the study results to other people.
The ultimate purpose of internal validity is to determine if conducted research is appropriate. You are establishing cause and effect by asking if the changes to the independent variable resulted in the changes to the dependent variable.
You determine this verification by the manner in which the independent variable affects the dependent variable and the number of confounding variables that are present or influence the other variables.
The higher the number of confounding variables, the more your internal validity and the integrity of your information suffers. If the tests or experiments in your study occur without confounding variables, the internal validity will remain at an optimal level.
With the strength example as a guide, let’s assume the study calls for a determination of how many chin-ups a person can do. You can account for confounding variables in the test by utilizing random samples to avoid bias and considering for things that may affect the person’s ability to perform the chin-up (injury, weight, or age).
By allowing for items that could compromise the data, you increase the internal validity. A few other examples of issues that have an impact on the internal validity include:
- Regression to the Mean: Within your study, this could reflect if extreme outputs are nearing the average outputs.
- Pre-Screening: If subjects are made aware of the specific study purpose, they may alter their natural inclinations toward it.
- Altering Equipment or Instrumentation: Even the slightest change makes a meaningful impact on the outcome. For our strength study, this could mean the position of the weight equipment changing or the gauges used for the strength measurement not calibrated correctly.
- Absent Test Subjects: In most cases, this will have a more substantial impact if your study involves more than one group. If you are running a study that pulls in 50 subjects that workout and 50 that don’t, no-shows from one side or the other will skew the overall findings.
- Not Following Protocols: Failure to account for or complete the proper protocols can have a negative impact on the internal validity.
- Changes that are Unaccounted For: If your study calls for 100 people not currently in a workout program, but ten of the test subjects unexpectedly start one during the research.
By no means a complete list, this does detail the sensitivity of conducting experiments and why internal validity is essential.
These are small examples, but they do help to illustrate that even if a minor change appears insignificant, it can have a significant and lasting impact on the internal variable.
In its simplest terms, external validity comes down to whether or not you can generalize the results of a study and apply them to a broader group or place. This application of the information can help to confirm if the study itself was correct.
You can counter external validity when its shown that the parameters used in the original study do not account for real-world variables. Using our original concept of the strength research, here are some examples of what can oppose external validity:
- Are the test subjects selected at random? If not, selection bias will conflict with the external validity.
- Was there some type of pre-screen or testing done before the experiment? This can tip off the test subjects and potentially alter their behavior or expectations of the study in which they are participating.
- Are all subjects participating in a workout regimen? If the sample is homogenized or uniform, you will not be able to apply the study results to a broader population (if the subject group is entirely made up of people who don’t exercise or is nothing but people who do, neither sample can correlate to an average population).
- Are the subjects changing throughout the study? If your strength study is on younger subjects, they could be maturing at a rate that would throw off the results.
- Is the study impacted by the Hawthorne effect? If your sample group knows they are part of a study, their normal behavior could be altered (for a strength study that could translate to someone who doesn’t typically workout starting a regimen before or during the research).
- Is the study impacted by the Rosenthal effect? This is the concept of an anticipated result leading to improved performance.
Much like the internal threats, these are the most common challenges to external validity. Anyone of these could create issues and negatively pull own the external validity. Again, these are merely examples to show threats to external validity or if research can be successfully generalized.
Speaking of generalization, let’s take the strength example one step further, and apply it to population validity and applying preset circumstances in which a generalization could occur.
For population validity, you can easily generalize the results if the strength measurement was to determine the average number of people who lift weights if the sample size was randomly selected.
If you needed to increase external validation, then you could recreate the lab experiment in the real world without the real-world subjects realizing it. It’s important to note that this will present challenges as the real-world environment may change from one location to the next.
The same process can also apply to the ecological validity (where does the average person lift weights, at home or in a gym) and the historical validity (how long does the average person maintain a weightlifting regimen).
Internal vs External Validity Differences
Now that we’ve seen internal and external validity on their own let’s directly detail the main differences between them. The difference over internal vs external validity will be discussed below.
Internal validity is the measure of the accuracy of your research, and any changes within it are due to nothing other than the independent variable. Applying it to a broader scope requires pulling in a dependent variable for measurement.
Further, the external validity will draw a link between the dependent and independent variables and determine if it can indeed apply to that wider scope.
Additional contrasts include internal validity looking at the number of confounding variables and accounting for them while the external variable is more concerned with applying the resulting data to real-world scenarios.
You can also add to this the internal side wanting to limit additional interpretation of the given data, whereas externally, the conclusion is more concerned with application beyond a limited purpose.
From the internal standpoint, remember that it is about accuracy, control, and the overall soundness of the research. The external side wants to apply the analysis outside the laboratory, in a natural setting that can both generalize the data and use it in actual situations.
Finally, you cannot capture both internal and external validity with the same experiment or test. As mentioned, internal validity must come first with the real-world applications for external validity being performed or generalized after.
Regardless of the experiments, research, or studies, you may be conducting; it is crucial to understand both internal vs external validity. Internal validity is the initial key as it drives the primary data set. It must be sound before making any more significant conclusions. Once that is accomplished, external validity may then be achieved in the broader context.
To sum up, internal validity is the cause and effect relationship in the study. On the other hand, external validity usually asks the question, “does this apply in the everyday real situations outside the laboratory?”
Knowing the difference between internal vs external validity is important in order to come up with a successful social research.
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