The 4 Types of Reliability in Research | Definitions & Examples

Reliability tells you how consistently a method measures something. When you apply the same method to the same sample under the same conditions, you should get the same results. If not, the method of measurement may be unreliable.

There are four main types of reliability. Each can be estimated by comparing different sets of results produced by the same method.

Test-retest reliability

Test-retest reliability measures the consistency of results when you repeat the same test on the same sample at a different point in time. You use it when you are measuring something that you expect to stay constant in your sample.

Why it’s important

Many factors can influence your results at different points in time: for example, respondents might experience different moods, or external conditions might affect their ability to respond accurately.

Test-retest reliability can be used to assess how well a method resists these factors over time. The smaller the difference between the two sets of results, the higher the test-retest reliability.

How to measure it

To measure test-retest reliability, you conduct the same test on the same group of people at two different points in time. Then you calculate the correlation between the two sets of results.

Improving test-retest reliability

• When designing tests or questionnaires, try to formulate questions, statements and tasks in a way that won’t be influenced by the mood or concentration of participants.
• When planning your methods of data collection, try to minimize the influence of external factors, and make sure all samples are tested under the same conditions.
• Remember that changes can be expected to occur in the participants over time, and take these into account.

Interrater reliability

Interrater reliability (also called interobserver reliability) measures the degree of agreement between different people observing or assessing the same thing. You use it when data is collected by researchers assigning ratings, scores or categories to one or more variables.

Why it’s important

People are subjective, so different observers’ perceptions of situations and phenomena naturally differ. Reliable research aims to minimize subjectivity as much as possible so that a different researcher could replicate the same results.

When designing the scale and criteria for data collection, it’s important to make sure that different people will rate the same variable consistently with minimal bias. This is especially important when there are multiple researchers involved in data collection or analysis.

How to measure it

To measure interrater reliability, different researchers conduct the same measurement or observation on the same sample. Then you calculate the correlation between their different sets of results. If all the researchers give similar ratings, the test has high interrater reliability.

Improving interrater reliability

• Clearly define your variables and the methods that will be used to measure them.
• Develop detailed, objective criteria for how the variables will be rated, counted or categorized.
• If multiple researchers are involved, ensure that they all have exactly the same information and training.

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Parallel forms reliability

Parallel forms reliability measures the correlation between two equivalent versions of a test. You use it when you have two different assessment tools or sets of questions designed to measure the same thing.

Why it’s important

If you want to use multiple different versions of a test (for example, to avoid respondents repeating the same answers from memory), you first need to make sure that all the sets of questions or measurements give reliable results.

How to measure it

The most common way to measure parallel forms reliability is to produce a large set of questions to evaluate the same thing, then divide these randomly into two question sets.

The same group of respondents answers both sets, and you calculate the correlation between the results. High correlation between the two indicates high parallel forms reliability.

Improving parallel forms reliability

• Ensure that all questions or test items are based on the same theory and formulated to measure the same thing.

Internal consistency

Internal consistency assesses the correlation between multiple items in a test that are intended to measure the same construct.

You can calculate internal consistency without repeating the test or involving other researchers, so it’s a good way of assessing reliability when you only have one data set.

Why it’s important

When you devise a set of questions or ratings that will be combined into an overall score, you have to make sure that all of the items really do reflect the same thing. If responses to different items contradict one another, the test might be unreliable.

How to measure it

Two common methods are used to measure internal consistency.

• Average inter-item correlation: For a set of measures designed to assess the same construct, you calculate the correlation between the results of all possible pairs of items and then calculate the average.
• Split-half reliability: You randomly split a set of measures into two sets. After testing the entire set on the respondents, you calculate the correlation between the two sets of responses.

Improving internal consistency

• Take care when devising questions or measures: those intended to reflect the same concept should be based on the same theory and carefully formulated.

Which type of reliability applies to my research?

It’s important to consider reliability when planning your research design, collecting and analyzing your data, and writing up your research. The type of reliability you should calculate depends on the type of research and your methodology.

If possible and relevant, you should statistically calculate reliability and state this alongside your results.