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Alpha is also a function of the number of items, so shorter scales will often have lower reliability estimates yet still be preferable in many situations because they are lower burden. An alternative way of thinking about internal consistency is that it is the extent to which all of the items of a test measure the same latent variable. The ...
Unfortunately, there is no way to directly observe or calculate the true score, so a variety of methods are used to estimate the reliability of a test. Some examples of the methods to estimate reliability include test-retest reliability, internal consistency reliability, and parallel-test reliability. Each method comes at the problem of ...
The term "internal consistency" is commonly used in the reliability literature, but its meaning is not clearly defined. The term is sometimes used to refer to a certain kind of reliability (e.g., internal consistency reliability), but it is unclear exactly which reliability coefficients are included here, in addition to .
A test is administered twice to the same sample with a time interval. Correlation between two sets of scores is used as an estimate of reliability. Testing conditions are assumed to be identical. (C) Internal Consistency Reliability: Internal consistency reliability estimates consistency of items with each other.
The BRIEF has demonstrated good reliability, with high test-retest reliability (rs ≈ .88 for teachers, .82 for parents) internal consistency (Cronbach's alphas ≈ .80 – .98), and moderate correlations between parent and teacher ratings (rs ≈ .32 – .34). Evidence for the convergent and divergent aspects of the BRIEF's validity comes ...
A valid measure is one that measures what it is intended to measure. Reliability is necessary, but not sufficient, for validity. Both reliability and validity can be assessed statistically. Consistency over repeated measures of the same test can be assessed with the Pearson correlation coefficient, and is often called test-retest reliability. [26]
In other words, the relevance of external and internal validity to a research study depends on the goals of the study. Furthermore, conflating research goals with validity concerns can lead to the mutual-internal-validity problem, where theories are able to explain only phenomena in artificial laboratory settings but not the real world. [13] [14]
Furthermore, apart from these definitions, as the number of data sources increases, the question of internal data consistency becomes significant, regardless of fitness for use for any particular external purpose. People's views on data quality can often be in disagreement, even when discussing the same set of data used for the same purpose.