reliabilitets

Reliability refers to the consistency of a measurement. A reliable instrument yields similar results under consistent conditions, making the scores dependable across time, items, raters, or different versions. Reliability is a property of the measurement process, not of the person being measured, and it is distinct from validity. A measure can be reliable without being valid, but a valid measure should also be reliable.

There are several types of reliability. Test-retest reliability assesses stability over time. Inter-rater reliability concerns agreement among observers. Internal consistency evaluates whether items on a scale measure the same construct. Parallel-forms reliability compares different versions of a test, and split-half reliability uses correlations between two halves of a test.

Quantifying reliability often uses statistics such as Pearson correlation for test-retest or simple associations, intraclass correlation coefficients for ratings and continuous data, Cohen’s kappa for categorical data, and Cronbach’s alpha for internal consistency.

Several factors influence reliability. Measurement error, instrument quality, item design, and administration conditions can affect results. Rater training, sample characteristics, and time between measurements also play roles. Heterogeneous samples can reduce reliability, while very long intervals may reduce stability.

To improve reliability, researchers standardize procedures, train raters thoroughly, use clear scoring rubrics, increase the number of items, and employ well-validated instruments. Piloting tests and refining items can also enhance consistency.

In summary, reliability is a fundamental aspect of measurement, reflecting the precision and consistency of scores. It is essential for interpreting results and supports, but does not by itself guarantee, validity.