Reliability in Assessment Practice

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Reliability in Assessment Practice

Despite being a Christian, I believe that screening does not have any notable impact on my beliefs concerning mental illness. People have all sorts of explanations about what causes mental issues but in my viewpoint, none of these reasons are related to religion. I believe that being screened for a mental illness is just like being screened for a normal illness. The only difference is that no medical and physical tests are conducted for mental illness. The professional screens one for an illness by mostly listening to the person and making observations of their behavior. The only time physical tests will be conducted for mental illness is when the condition is dire and is affecting them physically. I believe that screening does not have any effect on my beliefs concerning mental illness. At the end of the day, every individual goes through problems and needs assistance from time to time to overcome them.

Reliability refers to the level of consistency in a specific measuring test. One example of reliability would be a person expecting the same reading if they measure their weight during the day. This might only be possible if the scales used to measure the weight are kept constant throughout. However, if the scales are changed every time the weight is being measured, then the results would conflict making them unreliable. The three sources of measurement error are including random errors, systematic bias errors, and gross errors. Random error refers to the chance difference that exists between true and observed values of given measurement (Maleki, Amiri, & Castagliola, 2017). Systematic error is the proportional difference between true and observed values of a given measurement. Gross errors, also known as outliers are other errors rather than systematic and random errors.

Systematic errors mean that measurements of one single entity care vary according to predictable manners. In essence, every measurement tends to be different from true measurements in the usual direction and even by same measurements in some cases. Sources of systematic errors range from data collection procedures, research material, to individuals analysis techniques (Mayr, Schmid, Pfahlberg, Uter, & Gefeller, 2017). Examples of systematic errors are scale factor and offset errors. Unsystematic errors tend to affect the affect measurements in unpredictable manners. In random errors, once measurements tend to be lower or higher than true values. Sources of unsystematic error include poorly controlled procedures in the relationship, natural variations in experimental contexts, and individual differences.

Practice effects refer to the improvements that take place in cognitive test performance as a result of repeat evaluations from the using the same materials. Traditionally, practice effects are viewed as being sources of error variance. The carryover effect is a concept used to describe the transference of materials that are unwanted from one environment to another. Fatigue refers to a documented phenomenon that takes place when survey participants get tired of the exercise hence affecting the quality of data that comes as a result. The quality deteriorates when the attention and motivation of participants reduces.

References

Maleki, M. R., Amiri, A., & Castagliola, P. (2017). Measurement errors in statistical process monitoring: A literature review. Computers & Industrial Engineering, 103, 316-329.

Mayr, A., Schmid, M., Pfahlberg, A., Uter, W., & Gefeller, O. (2017). A permutation test to analyse systematic bias and random measurement errors of medical devices via boosting location and scale models. Statistical Methods in Medical Research, 26(3), 1443-1460.