The Reliability and Validity Concepts in Research
Reliability and validity are concepts used to assess the quality of research. They show how well a method, technique, or test measures something. Reliability is strongly linked to the measurement sequence, and validity is connected to measurement accuracy (Salkind, 2017). It is essential to consider reliability and validity when developing a research design, planning methods, and formatting results, especially in quantitative studies. Reliability and validity are closely related but mean different things. The measurement may be reliable but not valid. However, if the size is reliable, it is usually also reliable.
Reliability shows how consistently the method measures something. The measurement is considered reliable if the same result can be consistently achieved using the same techniques under the same circumstances (Salkind, 2017). Validity shows how accurately a method measures what it is intended for. If the studies have high reliability, they give results corresponding to fundamental properties, characteristics, and changes in the physical or social world (Salkind, 2017). High reliability is one of the indicators of measurement reliability. If the method is unreliable, it is probably invalid. Validity is more challenging to assess than reliability, but it is still much more important. The methods used to collect data must be reliable to get valuable results: the study must measure what is claimed to be measured. This ensures that the discussion of the data and the conclusions drawn are also correct.
There are four main types of reliability assessments, each of which evaluates reliability. They are (Rose & Johnson, 2020):
- Reliability between evaluators or observers: used to assess the extent to which different evaluators/observers give consistent estimates of the same phenomenon. Reliability between evaluators measures the degree of agreement between people observing or evaluating the same thing. This method is used when data is collected by researchers assigning scores, ratings, or categories to one or more variables.
- Reliability during repeated testing is used to evaluate the constancy of a measure from one time to another. The reliability of a repeated test measures the consistency of the results. For this, the same test is repeated on the same sample at different times. It is used to measure something expected to remain constant in the sample.
- Reliability of parallel forms: it is used to assess the consistency of the results of two tests constructed similarly from the same subject area. The reliability of parallel forms measures the correlation between two equivalent test versions. This type is used when two different assessment tools or questions are designed to measure the same thing.
- Reliability of internal consistency: it is used to evaluate the consistency of the results for the test elements. Internal consistency evaluates the correlation between multiple test elements designed to measure the same construct. Calculating internal consistency without repeating the test or involving other researchers is possible. This is an excellent way to assess reliability when only one data set exists.
The validity of the study is divided into four main types, which include (Rose & Johnson, 2020):
- Construct validity. Constructive validity helps to evaluate whether a particular measurement tool represents what we want to measure. It plays an essential role in determining the overall validity of a particular method.
- Content validity. The validity of the content is used to assess whether the test can represent various aspects of a particular design. In order to obtain reliable results, it is crucial that the content of the test, survey, or any measurement method used covers the relevant and necessary areas of the subject that it intends to measure.
- Face validity is used to consider how appropriate the content of a specific test looks on the surface. This seems similar to meaningful validity but is considered a more subjective and informal assessment.
- Criterion validity. The validity criterion plays a crucial role in assessing the relativity of test results and how closely the results of one test match the results of another.
The main differences between the types of validity and reliability are the following factors. When evaluating a multi-element scale, validity is considered more valuable than reliability. It is easy to assess the reliability of the measuring device, but it is not easy to determine its validity. Validity focuses on accuracy. It checks whether the scale gives the expected results or not. Conversely, reliability concentrates on accuracy, which measures the extent to which scale produces consistent results.
It is necessary to use a sufficient number of questions to evaluate variables to increase reliability. It is also required to provide a permanent environment for the participants so that the test results are consistent, and the test environment must be consistent. It is also necessary to measure reliability, there are several ways to do this, but the most common is the calculation of the so-called “Cronbach’s alpha,” which measures the reliability of internal consistency (the higher it is, the better) (Rose & Johnson, 2020). This is especially useful if all the evaluation questions measure the same construction.
Thus, reliability refers to the stability of the measurement scale, that is, to how much it will give the same results in individual cases. It can be evaluated in different ways; stability, internal consistency, and equivalence. Validity is the degree to which a scale measures what it is designed to measure. It is essential to consider reliability and validity when developing a research plan, planning methods, and formatting results, especially in quantitative studies.
References
Rose, J., & Johnson, C. W. (2020). Contextualizing reliability and validity in qualitative research: toward more rigorous and trustworthy qualitative social science in leisure research. Journal of Leisure Research, 51(4), 432-451. Web.
Salkind, N. J. (2017). Tests & measurement for people who (think they) hate tests & measurement. Sage Publications.
Appendix A
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Appendix B
Demographic Information for Cummings et al. (2002)’s Review
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