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Before conducting quantitative research, considering the reliability of your research methods as well as the instruments used for measurement is very important.
With a focus on reliability, you can determine how consistently something has been measured by a method. On applying a similar method to a similar sample under similar conditions, the results gathered should be similar too. If that doesn’t happen, the measurement method can be considered as unreliable.
The reliability is categorized into four main types which involve:
Let’s understand each of them in detail.
Test-retest reliability is used for measuring the uniformity of results especially when a similar test is repeated on a similar sample at a different time. This type of reliability comes into play when you measure something that should stay consistent in your sample. For instance, conducting a color blindness test on trainee pilot applicants is likely to have high test-retest reliability as color blindness remains constant and does not change with time.
The results that you collect can be influenced by multiple factors across different stages of time. For instance, the respondents might be going through some hard times in their personal life due to which you might experience their mood swings. There can be some external factors also that might impact the respondents’ ability to respond correctly.
The test-retest reliability offers a great way of assessing the effectiveness of a method and how capable is it to resist such factors over a point of time. The test-retest reliability is inversely proportional to the difference between the set of results. If there’s a small gap between the two of them, there will be a high test-retest reliability.
In order to measure test-retest reliability effectively, you need to conduct a similar test on a similar set of audiences at two different times. By doing so, it becomes easy to quantify the correlation that exists between the two different sets of results.
Let’s consider an example: you set up a questionnaire for measuring the IQ of a specific group of people (remember IQ is an attribute that is unlikely to change with time). On conducting the test over the same set of people after two months, you notice that the results gathered are significantly different. This represents that the test-retest reliability of the questionnaire that you’ve created is low.
Popularly known as interobserver reliability, interrater reliability helps in measuring the level of agreement among different people that observe or assess a similar thing. This type of reliability is used when data is gathered by researchers assigning scores or even categories to single or multiple variables.
Especially in observational studies where researchers are supposed to gather data on classroom behavior, interrater reliability plays a pivotal role. In such a case, the entire team of researchers should agree on the ways of categorizing or rating various types of behavior.
As individuals are subjective, so their perceptions related to different situations usually differ. Reliable research can play a pivotal role in minimizing subjectivity to a great extent so that similar results can be replicated by a different researcher too.
When you plan to finalize the scale as well as criteria for collecting data, it’s imperative to ensure that different individuals rate the similar variable constantly with minimal bias. This becomes more crucial when there are numerous researchers working in a particular data collection or analysis.
For measuring interrater reliability, researchers are asked to conduct a similar measurement or observation over a similar sample. Then you should focus on calculating the correlation that exists between the different sets of results generated. If the ratings given by all the researchers are similar, the test is known to have higher interrater reliability.
A group of researchers is asked to observe the wound healing among patients in a hospital. To accurately record the healing stages, you can use rating scales and set specific criteria for assessing the different aspects of wounds. Once the researchers evaluate a similar set of patients, the results shared by them are compared. If there exists a correlation between the sets of results shared, it can be considered that the test holds high interrater reliability.
In parallel forms reliability, the correlation that exists between two equivalent test versions gets measured. It comes into the picture when there are two diverse sets of questions or assessment tools are used for measuring the same thing.
If you prefer using various different versions of a particular test (for instance, to avoid gathering repetitive answers from respondents), it’s essential to ensure that all different sets of questions or measurements scales should yield reliable results.
For sharing assessments in educational institutions, it becomes necessary to develop different test versions for ensuring that students don’t gain access to the test questions in advance. According to parallel forms reliability, if a student participates in two different versions of a specific test, the results generated from both tests should be the same.
The ideal and most popular way of measuring parallel forms reliability is by creating a huge set of multiple questions to analyze the same thing. Once you do that, divide those questions randomly into two different sets.
Now share both of the question sets with a similar group of respondents. After they answer both of them, you can easily determine the correlation between the gathered results. If there’s a high correlation between them, the parallel forms reliability is high.
In order to improve parallel forms reliability, you need to make sure that the different questions or test items that you use are based on a similar theory and focused on measuring the similar thing.
Internal consistency is used for evaluating the correlation between various items in a test that aim at measuring a similar construct.
You don’t have to repeat the test for calculating internal consistency. Also, it lets you involve other researchers which makes it a great way of assessing reliability especially when there’s only one data set involved.
On formulating a series of questions or ratings that are likely to be integrated into a single score, it’s really important to ensure that a similar thing is being reflected by all of those items. The test can be considered unreliable if there are contradictory responses generated by the different items.
There are two methods used for measuring internal consistency:
Average inter-item correlation: In this method, the measures designed for assessing a similar construct are taken into account and you need to calculate the correlation existing between results that are generated through the possible item pairs. After this, you need to calculate the average.
Split-half reliability: You can create two sets by randomly splitting a set of measures. Once you successfully test the entire set over your intended respondents, you can easily calculate the existing correlation between those two sets of collected responses.
While drawing up questions or measures, you need to be extra cautious. You should only use the ones that reflect a similar concept and are based on a similar theory!