Reliability is also viewed differently in qualitative and quantitative research. Relationship between Reliability and Validity

Or reliability and validity are the first line of defense against spurious or incorrect conclusions.

Rather, reliability is a pre-requisite for a measure to be valid. However, reliable measures are not necessarily valid. Thus, a good research design is the one that minimizes errors and maximizes reliability. Random error can affect most stages of the research process and thus is endemic to research, whether in social, physical, or natural sciences. It also means that when multiple observations are taken, it will should even out. This means that such errors will equally likely overestimate the true score as much they are likely to underestimate the true score. Random errors are the chance factors that can distort the true score and are inversely proportional to the degree of reliability. Measurement error can be random or non-random. Reliability is majorly an empirical issue concentrated on the performance of an empirical measure. Reliability = True score/ (True score + Errors) In theory, reliability refers to the true score variance to the observed score variance. Four major ways of assessing reliability are test-retest, parallel test, internal consistency, and inter-rater reliability. Reliability, thus, is a matter of degree.

This consistency is what we refer to as reliability. At the same time, we can and should expect consistent results on repeated measurement from a good experiment, test, or instrument. Even repeated measures of the same characteristics for the same individual might not duplicate themselves. However, the measurement of any phenomenon invariably contains a certain amount of chance error. Reliability, fundamentally, concerns the extent to which a measure, an experiment, or test yields the same results on repeated trials.

Reliability research methods