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
Answer the following 5 questions:
- 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 developed instruments that may not have existed long enough to accumulate evidence?
- Describe situations in which the use of newly developed instruments would be appropriated.
- What precautions do the practitioners need to take if they want to use new instruments?
- Is it possible to have a test that is reliable but not valid? Why or why not?
- What is the difference between construct, content, and face validity?
Submission Details:
- Post your response to the Discussion Area by the due date assigned. Respond to at least two posts by the end of the week.
- Use an APA style reference list with in-text citations in your initial response.
- Use an APA style reference list with in-text citations in at least one of your two responses to classmates.
Reliability.html
Reliability
It is important for a psychological test to have good psychometric properties that help ensure that the test consistently measures what it is purported to measure.
The two most important psychometric properties of psychological tests are reliability and validity. In order for the results of a test to be applied and understood legitimately, the results must be both reliable and valid. Let’s examine reliability.
Reliability means that the same methods get the same results over time. There are different forms of reliability that have to be considered.
For example, test-retest reliability looks at the stability of scores when the test is given more than once to the same group of people. The closer the scores are between both administrations, the more reliable the test is.
Interrater reliability measures whether different people scoring the same test get the same results. This is especially important for subjective measures such as projective tests.
This goal is for a test to be as reliable as possible.
As with all types of experimental and evaluative measurement in psychological testing, error is always a possibility. While certain types of error are impossible to predict before looking at data, there are some kinds of error that can be prevented through paying careful attention to the way in which tests are being administered, and how information is collected and interpreted.
There are two main types of error that should be accounted for in psychological assessment, and those are measurement error and systematic error.
Measurement error is a result of misinterpretation of data, or drawing conclusions that are resulting from misread data. This type of error is distinguished from systematic error, in which the setup and foundations of the data collection were faulty, and this caused responses from participants to be different than if the items had been reliable.
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Validity.html
Validity
Test validity refers to how accurately a test measures the construct of interest. For example, if you want to measure the length of a board, a scale would not be a reliable test. A ruler would.
In addition to determining that a test is measuring what you want to measure, test validity also ensures that a test is appropriate for what you want to use it for.
For example, you want to test the validity of an employment test designed to measure cognitive ability. Once you determine that the test does measure cognitive ability, you then need to determine whether the test is appropriate to be used as a predictor in your particular employment setting.
Earlier we talked about reliability, or whether a test gives consistent results each time. How does validity relate to reliability? A test that is valid will always be reliable. This is due to the fact that if the test accurately measures a construct, it will then give the same measurement of that construct each time the test is administered to the same group. However, a test that is reliable is not always valid. For example, If I give you a test intending to measure your speed on a bicycle, but I do so by only taking measurement of the size of the bicycle, I will get the same results each time, but I still haven’t measured what I intended to measure.
It is important to know about different types of test validity so that you employ the most suitable items in your test.
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Types of Test Validities.html
Types of Test Validities
Several types of validity are taken into account when examining a psychological test. The three types of interest are construct validity, criterion-related validity, and content validity.
Let's look at each of them individually:
- Face validity is a measure of whether or not the test looks like it measures what it is supposed to measure. In other words, someone taking the test would not be confused that it is measuring something different.
- Construct validity means that the scores on the test are an accurate measure of the construct being measured. For example, do the scores on a new IQ test give an accurate measure of IQ
- Criterion-related validity is observed when a test can effectively predict indicators of a construct. Within the umbrella of criterion-related validity, there are two subtypes: concurrent validity and predictive validity.
- Concurrent validity can be measured when you have another test of the same criterion to compare scores to at the time the test is administered. If both tests gave the same measure of the criterion, then there is concurrent validity.
- Predictive validity is used to determine if test scores accurately predict performance on a criterion at a later time. For example, if I give a test measuring how often you check your email during an hour period, and you have the same number of times checking email during any hour in the future, then the test has predictive validity.
- Content validity measures how well your test measures all aspects of the construct you are trying to measure.
- External validity is an indicator of whether or not your measurement of a construct in one sample group is similar to the same measurement in a different sample group.
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W2_Discussion_Limpert_T
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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)).
References
Kaplan, R. M., & Saccuzzo, D. P. (20170106)). Psychological testing principles, applications and issues (9th ed.) [VitalSource Bookshelf version]. https://doi.org/vbk://9781337470469
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Week 2 Discussion
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
Kaplan, R. M., & Saccuzzo, D. P. (2017). Psychological Testing: Principles, Applications, and Issues (9th Edition). Cengage Limited. https://digitalbookshelf.southuniversity.edu/books/9781337470469
Sullivan G. M. (2011). A primer on the validity of assessment instruments. Journal of graduate medical education, 3(2), 119–120. https://doi.org/10.4300/JGME-D-11-00075.1
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