![]() ConclusionsĪn emphasis upon assessing the quality of assessments primarily in terms of reliability alone can produce a paradoxical and distorted picture, particularly in the situation where a narrower range of candidate ability is an inevitable consequence of being able to take a second part examination only after passing the first part examination. The Specialty Certificate Examinations had small Ns, and as a result, wide variability in their reliabilities, but SEMs were comparable with MRCP(UK) Part 2. The analysis of the MRCP(UK) Part 1 and Part 2 written examinations showed that the MRCP(UK) Part 2 written examination had a lower reliability than the Part 1 examination, but, despite that lower reliability, the Part 2 examination also had a smaller SEM (indicating a more accurate assessment). The Monte Carlo simulation showed, as expected, that restricting the range of an assessment only to those who had already passed it, dramatically reduced the reliability but did not affect the SEM of a simulated assessment. c) Reliability and SEM were studied in eight Specialty Certificate Examinations introduced in 2008-9. b) Reliability and SEM were studied in the MRCP(UK) Part 1 and Part 2 Written Examinations from 2002 to 2008. MethodsĪ) The interrelationships of standard deviation (SD), SEM and reliability were investigated in a Monte Carlo simulation of 10,000 candidates taking a postgraduate examination. This study investigated the extent to which the necessarily narrower ability range in candidates taking the second of the three part MRCP(UK) diploma examinations, biases assessment of reliability and SEM. However the alpha coefficient depends both on SEM and on the ability range (standard deviation, SD) of candidates taking an exam. Of the other statistical parameters, Standard Error of Measurement (SEM) is mainly seen as useful only in determining the accuracy of a pass mark. A value of 0.8-0.9 is seen by providers and regulators alike as an adequate demonstration of acceptable reliability for any assessment. You may remember how much embarrassment the systematic error caused to the OPERA guys who " detected" neutrinos moving faster than light! They didn't account for a bunch of sources of systematic errors, and had to rescind the conclusion.Cronbach's alpha is widely used as the preferred index of reliability for medical postgraduate examinations. Interestingly, in economic forecasting field the biggest problem is the shifts of the mean, which basically an equivalent of systematic error or bias in physical sciences. In physics, for instance, you're supposed to determine the bias (systematic error) outside your experiment, then correct for it in your measurements. You measure the significance by comparing the deviation to the standard error, but that deviation may itself be a bias (systematic error)! That's why the systematic error is the worst kind of error in physical science. Consider this: the whole point of your experiment is often in detecting the effect, such as deviation from zero. ![]() Systematic error leads to bias of the mean and cannot be determined or fixed within the same experiment. Accuracy cannot be fixed by collecting more data of the same measurement because systematic error won't go away. ![]() The precision often can be increased by repeated trials increasing the sample size. The presicion is driven by the random errors, and accuracy is defined by systematic errors.
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