Is it possible to develop a test that will be totally free of error variance? Why or why not? What rationale would you use to justify the use of newly dev

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Tiffany Limpert posted Jun 9, 2022 5:56 PM

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Psychometric Properties of Psychological Testing

While it may technically be possible for a data set to be free of error variance (meaning that all of the numbers are the same), it is highly unlikely. The idea that each test taker produces a true score and an observed score is implied in the classical test theory (Kaplan & Saccuzzo, 20170106)). The difference between the two is said to be the measurement error. Instruments used for quantifying are often flawed, typically leaving errors of variance (Kaplan & Saccuzzo, 20170106)).

When circumstances call for unconventional approaches, especially when other understood methods had failed, it will be difficult to justify implementing a newly developed instrument for measurement, particularly one that has not had the opportunity to produce valid and/or reliable supporting evidence. Psychologist may use their education and experience to obtain insight if/when desperate times call for desperate measures, as the rookie instrument may just be their last hope.

If a practitioner found themselves in a relevant situation, they should proceed with caution by understanding the possibility of the bias and limitations that are associated with the chosen instrument. It is also imperative to verify the reliability and validity for the intended use.

While tests that are valid are also able to be considered reliable, the opposing scenario is not necessarily true. In other words, reliability does not infer validity since it cannot confirm the proper measurement is in fact what is being recorded (Kaplan & Saccuzzo, 20170106)). Validity offers several constituents, first, construct validity delineates that the contents being measured are capable of producing relative scores, face validity refers to whether the test taker can adequately understand the intentions of the tests, and finally, content validity determines whether the contents of the test correspond with its intended measurements (Kaplan & Saccuzzo, 20170106)).

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Viviana Gonzalez Marquez posted Jun 9, 2022 2:03 PM

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Variance is a useful statistic commonly used in data analysis and is the average squared deviation around the mean, the error variance is the statistical variability of scores that are produced by extraneous factors other than the independent variable. It is usually so difficult to try to control all the extraneous variables. (American Psychological Association,n.d.)

To create a reliable test is important to make sure that the test score does not represent an item or a subset of elements from the entire domain, this usually indicates the random fluctuation that is expected in the scores. I believe that all tests are subject to error variance, this error can be reduced but not eliminated because it always will exist in differences within the group (Kaplan & Saccuzzo,2017).

For new instruments to be used they need to have reliability which is when the assessment gives the same results each time that is used and the result is consistent and dependable. The instrument also needs to have validity that is how accurately the study answers the question and the strength of the conclusions of the study. Assessment instruments need to be reliable and valid to have credible results. Reliability and validity must be examined reported and reference cited for each assessment uses to measure the study outcomes (Sullivan 2011). Examples of assessment can be resident feedback, and survey course evaluation.

The validity of the assessment instrument requires several sources of evidence to build the case. Evidence can be found in the content including the description of the steps to develop the instruments and other steps that support that the instruments have the appropriate content (Sullivan 2011).

Construct validity is established through a series of activities in which the researcher defines the construct and develops the instrumentation to measure. Construct validation involves assembling evidence of what a test means (Kaplan & Saccuzzo,2017).

Face validity is the appearance that the measure has validity. Test have face validity of the items seem reasonably related to the perceived purpose of the test. Face validity is not really validity because it does not offer evidence to support the conclusion driven by the test score (Kaplan & Saccuzzo,2017).

The researchers that create novel assessments need to state the development process, reliability measure, pilot result, and any other information that may lead to credibility in the use of the home-grown instrument transparency enhance credibility, they also enhance the validity of the instruments they need to literature research in previously developing similar studies (Sullivan 2011).

Validity will tell us how good the test is for a particular situation and reliability will tell you how trustworthy a score of the test will be. To have a valid conclusion the tests need to be reliable. The test can not be valid unless they are reliable, but it can be reliable and not valid. Because is reliable if the researcher gets the same result twice but if the test instead of giving the result for the purpose it was created for example if a test that measures career choice instead gives personality traits results then the assessment is not valid.

References

American Psychological Association. (n.d.). APA Dictionary of Psychology. American Psychological Association. Retrieved June 7, 2022, from https://dictionary.apa.org/error-variance