Reliability vs Validity in Research: Types & Examples
By busayo.longe
| Last updated:
Jul 27
|
11 min read
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In everyday life, we probably use reliability to describe how something is valid. However, in research and testing, reliability and validity are not the same things.
When it comes to data analysis, reliability refers to how easily replicable an outcome is. For example, if you measure a cup of rice three times, and you get the same result each time, that result is reliable.
The validity, on the other hand, refers to the measurement’s accuracy. This means that if the standard weight for a cup of rice is 5 grams, and you measure a cup of rice, it should be 5 grams.
So, while reliability and validity are intertwined, they are not synonymous. If one of the measurement parameters, such as your scale, is distorted, the results will be consistent but invalid.
Data must be consistent and accurate to be used to draw useful conclusions. In this article, we’ll look at how to assess data reliability and validity, as well as how to apply it.
Read: Internal Validity in Research: Definition, Threats, Examples
When a measurement is consistent it’s reliable. But of course, reliability doesn’t mean your outcome will be the same, it just means it will be in the same range.
For example, if you scored 95% on a test the first time and the next you score, 96%, your results are reliable. So, even if there is a minor difference in the outcomes, as long as it is within the error margin, your results are reliable.
Reliability allows you to assess the degree of consistency in your results. So, if you’re getting similar results, reliability provides an answer to the question of how similar your results are.
A measurement or test is valid when it correlates with the expected result. It examines the accuracy of your result.
Here’s where things get tricky: to establish the validity of a test, the results must be consistent. Looking at most experiments (especially physical measurements), the standard value that establishes the accuracy of a measurement is the outcome of repeating the test to obtain a consistent result.
For example, before I can conclude that all 12-inch rulers are one foot, I must repeat the experiment several times and obtain very similar results, indicating that 12-inch rulers are indeed one foot.
Most scientific experiments are inextricably linked in terms of validity and reliability. For example, if you’re measuring distance or depth, valid answers are likely to be reliable.
But for social experiences, one isn’t the indication of the other. For example, most people believe that people that wear glasses are smart.
Of course, I’ll find examples of people who wear glasses and have high IQs (reliability), but the truth is that most people who wear glasses simply need their vision to be better (validity).
So reliable answers aren’t always correct but valid answers are always reliable.
When assessing reliability, we want to know if the measurement can be replicated. Of course, we’d have to change some variables to ensure that this test holds, the most important of which are time, items, and observers.
If the main factor you change when performing a reliability test is time, you’re performing a test-retest reliability assessment.
However, if you are changing items, you are performing an internal consistency assessment. It means you’re measuring multiple items with a single instrument.
Finally, if you’re measuring the same item with the same instrument but using different observers or judges, you’re performing an inter-rater reliability test.
Evaluating validity can be more tedious than reliability. With reliability, you’re attempting to demonstrate that your results are consistent, whereas, with validity, you want to prove the correctness of your outcome.
Although validity is mainly categorized under two sections (internal and external), there are more than fifteen ways to check the validity of a test. In this article, we’ll be covering four.
First, content validity, measures whether the test covers all the content it needs to provide the outcome you’re expecting.
Suppose I wanted to test the hypothesis that 90% of Generation Z uses social media polls for surveys while 90% of millennials use forms. I’d need a sample size that accounts for how Gen Z and millennials gather information.
Next, criterion validity is when you compare your results to what you’re supposed to get based on a chosen criteria. There are two ways these could be measured, predictive or concurrent validity.
Read: Survey Errors To Avoid: Types, Sources, Examples, Mitigation
Following that, we have face validity. It’s how we anticipate a test to be. For instance, when answering a customer service survey, I’d expect to be asked about how I feel about the service provided.
Lastly, construct-related validity. This is a little more complicated, but it helps to show how the validity of research is based on different findings.
As a result, it provides information that either proves or disproves that certain things are related.
We have three main types of reliability assessment and here’s how they work:
This assessment refers to the consistency of outcomes over time. Testing reliability over time does not imply changing the amount of time it takes to conduct an experiment; rather, it means repeating the experiment multiple times in a short time.
For example, if I measure the length of my hair today, and tomorrow, I’ll most likely get the same result each time.
A short period is relative in terms of reliability; two days for measuring hair length is considered short. But that’s far too long to test how quickly water dries on the sand.
A test-retest correlation is used to compare the consistency of your results. This is typically a scatter plot that shows how similar your values are between the two experiments.
If your answers are reliable, your scatter plots will most likely have a lot of overlapping points, but if they aren’t, the points (values) will be spread across the graph.
It’s also known as internal reliability. It refers to the consistency of results for various items when measured on the same scale.
This is particularly important in social science research, such as surveys, because it helps determine the consistency of people’s responses when asked the same questions.
Most introverts, for example, would say they enjoy spending time alone and having few friends. However, if some introverts claim that they either do not want time alone or prefer to be surrounded by many friends, it doesn’t add up.
These people who claim to be introverts or one this factor isn’t a reliable way of measuring introversion.
Internal reliability helps you prove the consistency of a test by varying factors. It’s a little tough to measure quantitatively but you could use the split-half correlation.
The split-half correlation simply means dividing the factors used to measure the underlying construct into two and plotting them against each other in the form of a scatter plot.
Introverts, for example, are assessed on their need for alone time as well as their desire to have as few friends as possible. If this plot is dispersed, likely, one of the traits does not indicate introversion.
This method of measuring reliability helps prevent personal bias. Inter-rater reliability assessment helps judge outcomes from the different perspectives of multiple observers.
A good example is if you ordered a meal and found it delicious. You could be biased in your judgment for several reasons, perception of the meal, your mood, and so on.
But it’s highly unlikely that six more people would agree that the meal is delicious if it isn’t. Another factor that could lead to bias is expertise. Professional dancers, for example, would perceive dance moves differently than non-professionals.
Read: What is Experimenter Bias? Definition, Types & Mitigation
So, if a person dances and records it, and both groups (professional and unprofessional dancers) rate the video, there is a high likelihood of a significant difference in their ratings.
But if they both agree that the person is a great dancer, despite their opposing viewpoints, the person is likely a great dancer.
Researchers use validity to determine whether a measurement is accurate or not. The accuracy of measurement is usually determined by comparing it to the standard value.
When a measurement is consistent over time and has high internal consistency, it increases the likelihood that it is valid.
This refers to determining validity by evaluating what is being measured. So content validity tests if your research is measuring everything it should to produce an accurate result.
For example, if I were to measure what causes hair loss in women. I’d have to consider things like postpartum hair loss, alopecia, hair manipulation, dryness, and so on.
By omitting any of these critical factors, you risk significantly reducing the validity of your research because you won’t be covering everything necessary to make an accurate deduction.
For example, a certain woman is losing her hair due to postpartum hair loss, excessive manipulation, and dryness, but in my research, I only look at postpartum hair loss. My research will show that she has postpartum hair loss, which isn’t accurate.
Yes, my conclusion is correct, but it does not fully account for the reasons why this woman is losing her hair.
This measures how well your measurement correlates with the variables you want to compare it with to get your result. The two main classes of criterion validity are predictive and concurrent.
It helps predict future outcomes based on the data you have. For example, if a large number of students performed exceptionally well in a test, you can use this to predict that they understood the concept on which the test was based and will perform well in their exams.
On the other hand, involves testing with different variables at the same time. For example, setting up a literature test for your students on two different books and assessing them at the same time.
You’re measuring your students’ literature proficiency with these two books. If your students truly understood the subject, they should be able to correctly answer questions about both books.
Quantifying face validity might be a bit difficult because you are measuring the perception validity, not the validity itself. So, face validity is concerned with whether the method used for measurement will produce accurate results rather than the measurement itself.
If the method used for measurement doesn’t appear to test the accuracy of a measurement, its face validity is low.
Here’s an example: less than 40% of men over the age of 20 in Texas, USA, are at least 6 feet tall. The most logical approach would be to collect height data from men over the age of twenty in Texas, USA.
However, asking men over the age of 20 what their favorite meal is to determine their height is pretty bizarre. The method I am using to assess the validity of my research is quite questionable because it lacks correlation to what I want to measure.
Construct-related validity assesses the accuracy of your research by collecting multiple pieces of evidence. It helps determine the validity of your results by comparing them to evidence that supports or refutes your measurement.
If you’re assessing evidence that strongly correlates with the concept, that’s convergent validity.
Examines the validity of your research by determining what not to base it on. You are removing elements that are not a strong factor to help validate your research. Being a vegan, for example, does not imply that you are allergic to meat.
You need a bulletproof research design to ensure that your research is both valid and reliable. This means that your methods, sample, and even you, the researcher, shouldn’t be biased.
To enhance the reliability of your research, you need to apply your measurement method consistently. The chances of reproducing the same results for a test are higher when you maintain the method you’re using to experiment.
For example, you want to determine the reliability of the weight of a bag of chips using a scale. You have to consistently use this scale to measure the bag of chips each time you experiment.
You must also keep the conditions of your research consistent. For instance, if you’re experimenting to see how quickly water dries on sand, you need to consider all of the weather elements that day.
So, if you experimented on a sunny day, the next experiment should also be conducted on a sunny day to obtain a reliable result.
There are several ways to determine the validity of your research, and the majority of them require the use of highly specific and high-quality measurement methods.
Before you begin your test, choose the best method for producing the desired results. This method should be pre-existing and proven.
Also, your sample should be very specific. If you’re collecting data on how dogs respond to fear, your results are more likely to be valid if you base them on a specific breed of dog rather than dogs in general.
Validity and reliability are critical for achieving accurate and consistent results in research. While reliability does not always imply validity, validity establishes that a result is reliable. Validity is heavily dependent on previous results (standards), whereas reliability is dependent on the similarity of your results.
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