Reliability in Research: Definitions and Types

When you’ve collected your data and need to measure your research results, it’s time to consider the reliability level of your methods and tools. It often happens that calculation methods produce errors. Particularly, in case you make wrong initial assumptions. In order to avoid getting wrong conclusions it is better to invest some time into checking whether they are reliable.

Today we’ll talk about the reliability of research approaches, what it means and how to check it properly. Main verification methods such as split-half, inter-item and inter-rater will be examined and explained below. Let’s go and find out how to use them with our PhD dissertation writing services!

First, let’s define reliability. It is highly important to ensure your data analysis methods are reliable, meaning that they are likely to produce stable and consistent results whenever you use them against different datasets. So, a special parameter named ‘reliability’ has been introduced in order to evaluate their consistency. High reliability means that a method or a tool you are evaluating will repeatedly produce the same or similar results when the conditions remain stable.

This parameter has the following key components:

Follow our thesis writing services to find out what are the main types of this parameter and how they can be used.

There are four main types of reliability. Each of them shows consistency of a different approach to data collection and analysis. These types are related to different ways of conducting research, however all of them are equally considered as quality measurements for the tools and methods they describe. We’ll examine each of these 4 types below, discussing their differences, purposes and areas of usage. Let’s take a closer look!

The first type is called ‘test-retest’ reliability. You can use it in case you need to analyze methods which are to be applied to the same group of individuals many times. When running the same tests across the same object over and over again, it is important to know whether they produce reliable results. If the latter don’t change significantly over a period of time, we can assume that this parameter shows a high consistency level. Therefore, these methods must be helpful for your research.

Let’s review an example of test-retest reliability which might provide more clarity about this parameter for a student preparing their own research. Suppose, a group of a local mall’s consumers has been monitored by a research team for several years. Shopping habits and preferences of each person of the group were examined, particularly by conducting surveys. If their responses did not change significantly over those years, it means that the current research approach can be considered reliable from the test-retest aspect. Otherwise, some of the methods used to collect this data need to be reviewed and updated to avoid introducing errors in the research.

Another type is parallel forms reliability. It is applied to a research approach when different versions of an assessment tool are used to examine the same group of respondents. In case the results obtained with the help of all these versions correlate with each other, the approach can be considered reliable. However, an analyst needs to ensure that all the versions contain the same elements before assessing their consistency. For example, if two versions examine different qualities of the target group, it wouldn’t make much sense to compare one version to another.

A parallel forms reliability example using a real-life situation would help illustrate the definition provided above. Let’s take the previous example where a focus group of consumers is examined to analyze dependencies and trends of a local mall’s goods consumption.

Let’s suppose the data about their shopping preferences is obtained by conducting a survey among them, one or several times. At the next stage the same data is collected by analyzing the mall’s sales information. In both cases an assessment tool refers to the same characteristics (e.g., preferred shopping hours). If the results are correlated in both cases, it means that the approach is consistent.

The next type is called inter-rater reliability. This measure does not involve different tools but requires a collective effort of several researchers, or raters, to examine the target population independently from each other. Once they are done with that, their assessment results need to be compared across each other. Strong correlation between all these results would mean that the methods used in this case are consistent. In case some of the observers don’t agree with others, the assessment approach to this problem needs to be reviewed and most probably corrected.

Let’s review an inter rater reliability example – another case to help you visualize this parameter and the ways to use it in your own research.

We’ll suppose that the consumer focus group from the previous example is independently tested by three researchers who use the same set of testing types:

In case each of these researchers obtains the same or very similar results at the end leading to similar conclusions, we can assume that the research approach used in this project is consistent.

The final type is called internal consistency reliability. This measure can be used to evaluate the degree to which different tools or parts of a test produce similar results after probing the same area or object. The purpose is to try calculating or analyzing some value using different ways. In case the same results are obtained in each case, we can assume that the measurement method itself is consistent. Depending on how precise the calculations are, small deviations between these results may or may not be allowed.

In the end of this review of reliability types let’s check out an internal consistency reliability example. 

Let’s take the same situation as described in previous examples: a focus consumer group whose shopping preferences are analyzed with the help of several different methods. In order to test the consistency of these methods, a researcher can randomly split the focus group in half and analyze each half independently. If done properly, random splitting must provide two subgroups with nearly identical qualities, so they can be viewed as the same construct. If analytic measures provide strongly correlated results for both these groups, the research approach is consistent.

In order to evaluate how well a test measures a selected object, a special parameter named reliability coefficient has been introduced. Its definition is fully explained by its name: it shows whether a test is repeatable or reliable. The coefficient is a number lying within the range between 0 and 1.00, where 0 indicates no reliability and 1.00 indicates perfect reliability.

The following proportion is used to calculate this coefficient, R:

R = (N/(N-1)) * ((Total Variance - Sum of Variance)/Total Variance),

where N is the number of times the test has been run.

A real test could hardly have a perfect reliability. Typically, having the coefficient of 0.8 or higher means the test can be considered reliable enough.

It is important to understand the difference between quality vs reliability. These concepts are somewhat related however they have different practical meanings. We use quality to indicate that an object or a solution performs its proper functions well and allows its users to achieve the intended purpose. Reliability indicates how well this object or solution is able to maintain its quality level as time passes or conditions change. It can be stated that reliability is one of the subsets of quality which is used to evaluate the consistency of a certain object or solution in a dynamic environment. Because of its nature, reliability is a probabilistic value.

We also have a reliability vs validity blog. It is so crucial to understanding their difference for your research.

In this article we have reviewed the concept of reliability in research. Its main types and their usage in real life research cases have been examined to a certain degree. Ways of measuring this value, particularly its coefficient, have also been explained.