How consistently a method assesses something is described by its reliability. The same approach should yield the same results when applied to the same sample under the same circumstances. If not, your measuring technique might not be accurate, or bias might have entered into your analysis.
There are four main types of reliability. Each can be estimated by comparing different sets of results produced by the same method.
| Type of reliability | Measures the consistency of… |
|---|---|
| Test-retest | The same test over time. |
| Interrater | The same test conducted by different people. |
| Parallel forms | Different versions of a test which are designed to be equivalent. |
| Internal consistency | The individual items of a test. |
1. Test-retest reliability
The consistency of results when the same test is repeated on the same sample at a different time is measured by test-retest reliability. When you are measuring something that you anticipate being consistent in your sample, you utilize it.
Why it's crucial?
At various times, a variety of factors may affect your findings. For instance, respondents' moods or the environment may have an impact on how correctly they react.
To determine how well a procedure holds up over time, test-retest reliability might be utilized. The test-retest reliability increases as the discrepancy between the two sets of findings decreases.
How to calculate it?
You administer the same test to the same set of individuals at two separate times in order to gauge test-retest reliability. The correlation between the two sets of findings is then calculated.
Example of test-retest reliability
You create a survey to gauge a group of participants' IQ levels, a characteristic that is unlikely to fluctuate dramatically over time.The test-retest reliability of the IQ questionnaire is low since the results are noticeably different when the test is given to the same group of individuals two months apart.
2. 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, and it can help mitigate observer bias.
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.
A team of researchers observe the progress of wound healing in patients. To record the stages of healing, rating scales are used, with a set of criteria to assess various aspects of wounds. The results of different researchers assessing the same set of patients are compared, and there is a strong correlation between all sets of results, so the test has high interrater reliability.
3. 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.
Parallel forms reliability example4. 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.
A group of respondents are presented with a set of statements designed to measure optimistic and pessimistic mindsets. They must rate their agreement with each statement on a scale from 1 to 5. If the test is internally consistent, an optimistic respondent should generally give high ratings to optimism indicators and low ratings to pessimism indicators. The correlation is calculated between all the responses to the “optimistic” statements, but the correlation is very weak. This suggests that the test has low internal consistency.
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