Some things like blood pressure and the number of arrests are easy to measure without error.
However, this is not usually the case in most measurements. When a measurement has the potential for inaccuracy, one of the most important criteria for its soundness is its reliability. Generally, reliability offers a measure for test scores from a group of students.
But what is reliability? Let’s find our below.
What is Reliability?
We can use an analogy to understand what is reliability. Let’s visualize a kitchen scale. If the scale is accurate, it will reflect the same weight for a bag of flour placed on it today and a bag of flour placed on it tomorrow. However, if the scale is unreliable, it may give you a different weight each time.
The stability or consistency of test scores is measured by reliability. Simply put, reliability is the degree to which an assessment tool gives consistent and stable results. You can also think of it as the ability to replicate a test or research findings.
A reliable math test will precisely measure mathematical knowledge for everyone who takes it. Reliable study findings may be duplicated time and time again.
Types of Reliability
Now that you have an idea of what is reliability, let’s look at the different types of reliability.
1. Internal Reliability
We can also determine what reliability is from an internal perspective. Assessing reliability using a single measuring instrument provided to a group of persons on a single occasion is known as internal reliability estimation.
In effect, it assesses the instrument’s reliability by assessing how well items that reflect the same construct provide similar outcomes. It looks at how consistent the findings are across items within the measure for the same construct. Internal consistency metrics come in a range of shapes and sizes.
Internal consistency, often known as internal dependability, is a metric that indicates how successfully your test is measuring what you want it to measure.
Importance of Internal Reliability
When creating a series of questions that will be merged to get an overall score, internal reliability helps make sure that each set of questions truly reflects the same thing. The test may be unreliable if replies to different items contradict one another.
You may develop a questionnaire with a collection of items that respondents must agree or disagree with to gauge client satisfaction. Internal reliability indicates whether all of the claims are credible indications of consumer satisfaction.
There’s the so-called split half method which is an internal reliability method that determines a test’s internal consistency. In this method, a test is split in half using different methods like half and second half. It is great to use on psychometric tests and questionnaires, but the questionnaires should be large and the tests should measure the same construct.
It involves comparing the results of one half of a test with that of the other half. A test has internal reliability if two halves of the test gives the same results.
2. External Reliability
We will first look at what is reliability from an external perspective. External reliability refers to the ability of a test or measure to be generalized outside of its context. A claim that in-person tutoring enhances test scores, for example, should apply to several subjects.
Depression tests should be able to detect depression in people of different socioeconomic backgrounds, ages as well as introverts and extroverts.
Types of External Reliability
The different types of external reliability include:
- Test-Retest Reliability
When selecting measuring tools for an experiment, you should make sure they are valid. This means that they can accurately measure the construct in question. The tools should reliably replicate the results in the same context and population multiple times. This is one of the main reasons we are looking at what is reliability and why it matters.
In a multi-time point experiment, the measurement tool you use should consistently recreate the same result through all visits if all other variables remain constant. Tools that provide such consistency are considered to have excellent test retest reliability. They are suitable for use in longitudinal studies.
Many things can influence your results over time: respondents’ moods, for example, or external conditions can alter their capacity to react appropriately.
Test-retest reliability can be used to determine how well a method holds up over time to these conditions. The higher the test-retest reliability, the smaller the discrepancy between the two sets of results.
- Interrater Reliability
When humans are included as part of a measuring technique, you must consider if the results are accurate and consistent. People are known for their erratic behavior. We are prone to distraction. We get tired of doing the same things over and over again. We daydream. We make mistakes.
So, how can we tell if two observers are being consistent with their observations? You should probably establish inter-rater reliability outside of the framework of your study’s measurement. After all, if you use your study’s data to determine the dependability and discover that it is low, you’re kind of trapped. It’s probably preferable to conduct this as a pilot study.
If your study is ongoing, you may wish to reestablish inter-rater reliability regularly to ensure that your raters aren’t changing. There are two main methods for calculating inter-rater reliability.
You can calculate the proportion of agreement between the observers if your measurement has categories and the observers are marking off the category each observation belongs to.
When the measure is continuous, there is another method for estimating inter-rater reliability. All you have to do now is determine the correlation between the two observers’ assessments.
Because people are subjective, their impressions of situations and phenomena will differ. Reliable research seeks to reduce subjectivity as much as possible. This ensures that the same results may be replicated by another researcher.
It’s critical to ensure that various people will score the same variable consistently. You should achieve this with little bias when developing the scale and criteria for data collecting. When numerous researchers are involved in data gathering or analysis, this is extremely crucial.
- Parallel Forms Reliability
To ensure the trustworthiness of parallel forms, it is essential to develop two parallel forms. The method of testing for parallel forms reliability is to produce a huge set of questions that address the same construct. They are then divided into two groups at random. Both instruments are given to the same group of persons. The dependability estimate is based on the correlation between the two parallel forms.
One of the key drawbacks of this method is that you must generate a large number of items all reflecting the same concept. This isn’t always an easy task. Furthermore, the randomly divided halves are assumed to be parallel or equivalent in this method. Even by chance, this isn’t always the case.
You will want to know what is reliability, or parallel forms of reliability when it comes to administering tests. If you want to use numerous versions of a test you must first ensure that each set of questions or measurements produces reliable results. An example of when you want to do this is when you want to prevent respondents from repeating answers from memory.
Different versions of examinations are frequently needed in educational evaluation to ensure that pupils do not have access to the questions ahead of time. Parallel forms reliability states that if the same students take two distinct versions of a reading comprehension test, the answers should be identical.
Reliability Coefficient
A reliability coefficient is a measurement of a test’s ability to accurately assess achievement. The fraction of variance in observed scores attributable to true scores is known as the correlation coefficient. Let us look at what is reliability coefficient in more detail.
The phrase “reliability coefficient” encompasses several different factors. It is important to note that test-retest, parallel forms, and alternate-form are some of the few ways of estimating the coefficient. The reliability coefficient is quite intertwined with our topic on what is reliability.
In an ideal world, we’d verify a test’s reliability by repeating it on a broad group of people. We would then analyze the findings with statistical methods to make s determination.
However, due to practical challenges, changes in what is being tested over time vary in various individuals. There are also confounding factors such as an inability of respondents to accurately recall their answers from previous tests.
To address these issues, statisticians devised the split-half research design. With this method, the test elements are randomly divided into halves and an individual’s test score is calculated twice, once with one-half of the test elements and once with the other half
Cronbach’s alpha coefficient for scale reliability is the outcome of an extension of this principle. It aimed to evaluate every feasible way of separating the test into its component pieces. In questionnaire design, an alpha of 0.8 or above is regarded as acceptable. You will find that the most often used internal consistency coefficient is Cronbach’s alpha.
Test Reliability and Test Validity
The terms test reliability and test validity might seem like they are used interchangeably but they do not mean the same thing. To understand the difference, we need to look at what is reliability and what is validity separately.
Validity does not imply reliability. That is a dependable measure that constantly measures something isn’t always measuring what you want to be measured. While there are numerous reliable assessments of certain abilities, not all of them are valid for predicting job success.
We have already looked at what is reliability; the degree to which a test measures without error. The amount to which the test measures the postulated underlying construct is referred to as test validity. Reliability is not a continuous quality of a test; rather, multiple types of reliability exist for different populations at varying levels of the construct being tested.
However, while reliability is not synonymous with validity, it does set a limit on a test’s overall validity. A test that isn’t completely dependable can’t be completely legitimate, regardless of whether it is a way of evaluating a person’s characteristics or a way of forecasting criterion scores. Ideally, a test that is not dependable cannot possibly be valid even if it provides useful valid information.
An assessment’s validity, or whether it measures what it is supposed to, is just as crucial as its reliability. Using the kitchen scale as an example, a scale may routinely display the incorrect weight. In this situation, the scale is trustworthy but not valid.
Why Does Reliability Matter?
Now that you know what is reliability, let us now look at the importance of reliability.
We must consider why pre-employment examinations are given in the first place. Pre-employment exams are most valuable since they can assess natural abilities that are difficult to detect through the usual CV and interview procedure. Soft talents like critical thinking, conscientiousness, problem-solving, openness, learning capacity and motivation are generally difficult to develop.
You’d expect a pre-employment test to be able to measure these types of stable attributes in a consistent way across time. If the test fails to give consistent findings, you may wonder how effective it is at measuring what it claims to be testing, or whether it is valid in general.
Exams that evaluate natural traits or soft skills can be contrasted with tests that measure acquired skills or talents that are taught over time. Consider a high school math exam as an example. You might be given a test about the equation of a line at the outset of class and given a score of 30%.
Then your teacher goes through the content with you for an hour, after which you sit that same test and get 80%. These results are unreliable and inconsistent. However, that’s a good thing. In this case, the test is evaluating your learnt knowledge. You should, therefore, improve in such a test. It’s essential to understand when a test must produce accurate results.
If an evaluation measures the same thing consistently it is considered reliable. If you gave the same person an assessment on two occasions, you’d get the same conclusions about their knowledge or skills if the test has high reliability.
Researchers would have a difficult time testing hypotheses and comparing data across groups or studies if they got different replies every time they assessed the same variable on the same person. Both the social and physical sciences rely heavily on reliability.
Replication is a fundamental premise of science. You cannot be sure a study wasn’t duplicated only because of measurement error if the data isn’t reliable.
Assuming a researcher is evaluating a novel antidepressant medicine on depression symptoms, the outcome might be determined by a series of depression-related questions. The scale, then, should be a trustworthy indicator of depression symptoms.
An unreliable assessment, like a broken kitchen scale, does not measure anything consistently and cannot be used as a trustworthy measure of competency.
Conclusion
A test’s reliability is important in measuring tests and helps to ensure that test scores for students reflect more than random error. It is also used in the test validation process to measure validity of tests.
