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In healthcare research, scales and questionnaires are super common tools used to collect data about behavior. But did you know that poorly designed scales can lead to unreliable data and a waste of resources?
To make sure your scale is top-notch, you need to focus on two key aspects of quality: reliability and validity. These are kind of complicated concepts, so let’s break them down a bit. Let’s take a look at the different types of reliability and validity that you need to calculate to ensure the scale or measure you’re using is psychometrically sound.
Types of Reliability
Reliability is all about consistency – you want your scale to produce the same results every time you use it. We’re basically asking the question: can we trust this scale to give us consistent results? We want to make sure that no matter who’s using the scale or when it’s being used, the output is pretty much the same every time. Let’s look at the different types of reliability that are most commonly used for a scale:
Internal consistency reliability
Internal consistency refers to ensuring that different items in a scale are measuring the same thing. Think of it like this: if we’re using a scale to measure patient satisfaction, we want to make sure that all the items are actually measuring satisfaction and not some other unrelated thing.
To check for internal consistency, we look at how similar the responses are to different items in the scale. For example, if we have two items on our patient satisfaction scale that ask about recommending their healthcare provider to others, we would expect respondents to provide similar responses to both items.
One popular way to measure internal consistency is by using Cronbach’s α which is a statistic that basically tells us how well all the items in our scale are measuring the same thing. The higher the Cronbach’s α, the more internally consistent our scale is, which is a good thing!
So, by assessing internal consistency, we can make sure that our scale is measuring what it’s supposed to be measuring and that we can trust the data we’re collecting.
Inter-rater reliability
This basically refers to how much agreement there is among different people who are using the same scale to measure something. So if we have two or more independent appraisers using the same scale to assess a phenomenon or behaviour, we want to make sure that they’re all getting similar results. Ensuring high inter-rater reliability is super important in healthcare settings because it helps us make more informed decisions based on the data we’re collecting.
Test-retest reliability
The test-retest involves giving the same test to the same person at two different points in time and seeing how similar their scores are. By comparing their scores from both time points, we can see if the scale is stable over time.
Split-half reliability
Split-half reliability is a method we use to test the consistency of a scale. Here’s how it works:
First, we take all the questions on the scale and split them into two groups. Then, we compare the scores for each half of the scale to see if they’re similar. If they are, that indicates that the scale is reliable. It’s worth noting that split-half reliability works best for scales that have a large number of questions. This is because it’s harder to get an accurate picture of reliability with a smaller number of questions.
Types of Validity
Validity refers to how well the scale actually measures what it’s supposed to measure. For example, let’s say we’re using a scale to measure health anxiety – that is, how worried someone is about their health. In order for the scale to be valid, it needs to actually measure health anxiety and not some other kind of anxiety, like social anxiety. Below are the different types of validity that are typically assessed for scales:
Face validity
Face validity is basically whether or not a scale seems like it’s actually measuring what it’s supposed to measure – like if you just look at the questions on the scale, do they seem relevant and appropriate for the thing we’re trying to measure? For example, when assessing the face validity of a scale measuring the quality of life, one would expect to see items on physical pain and energy level but not on medication adherence. However, it is important to note that face validity is considered a pretty weak measure of validity, and it’s usually not assessed quantitatively. Content validity
Content validity is all about whether a scale covers all the important parts of what we’re trying to measure. So if we’re using a scale to measure how much someone knows about diabetes, we want to make sure the questions cover all the important things someone should know about diabetes. Content validity too is not usually assessed quantitatively – instead, experts review the scale and compare it to what we know about the construct to see if it covers all the important information.
Criterion validity
Criterion validity is about how well a scale is related to other things it should be related to. So if we have a scale that measures how well someone can understand health information, we should expect that people who score high on that scale also need less help reading instructions for things like over-the-counter medications. This helps us know if our scale is really measuring what we think it is..
Convergent and divergent validity
Convergent validity is when scores on one scale match up with scores on other scales that measure the same thing. So, for example, if we have a scale that measures depression, we want the scores on that scale to match up with scores on other depression scales like say the Hamilton Depression Rating Scale and the Beck Depression Inventor.
Divergent validity is kind of the opposite. It’s when scores on one scale should not match up with scores on other scales that measure something different. For example, a scale that measures fatigue shouldn’t match up with a scale that measures racism..
Concurrent validity
Concurrent validity means how well scores on a new measure are linked to scores on another measure that’s taken at the same time. This other measure could be another scale or something else entirely. Typically, researchers compare a new scale to a measure that’s already been shown to be valid. For instance, if we’re testing a scale that measures balance and mobility, we might compare it to people’s results on the Timed-Up-and-Go test and the 3-Meter Tandem Walk task, which are already well-proven measures.
Would you like guidance from an expert statistician in developing and validating the scale or questionnaire used in your study? Editage’s Statistical Analysis & Review Service can help!
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