, you wrote a very succinct paper, just shy of the required word count. Adding in the conclusion would have helped you meet that goal. Make sure to include appropriate references as noted. Use the available template to help with formatting. Over all, good work.
Comment by Carol Gegenheimer PhD: Use the APA style template to help with the format issues. You should still be using the APA 6th edition until you receive notice to switch to APA 7th edition for classes starting after October 8, 2020.
Reliability and Validity
Grand Canyon University – PSY812
September 16, 2020
Reliability and Validity
Reliability concerns the consistency of a measure, while validity is about the accuracy of a measure. The process of data collection in research involves making sure that the instrument is used to collect the data is both valid and reliable. The researchers reduce the chances of making errors when they ensure that the instrument in use is valid and reliable. Reliability and validity are the two major aspects of measurement integrity. Research would not be considered credible if the instrument used to measure the variable fails to produce correct and consistent results (Markon, Chmielewski, & Miller, 2011). These two concepts of a test shows the usefulness and the quality of the test and are the most vital parts of a test; therefore, one has to watch out the essential features when evaluating the efficiency of the test. Assessment instruments should also be both valid and reliable to make study results credible. Therefore, validity and reliability should be examined.
Importance of using tests that have high levels of reliability and validity Comment by Carol Gegenheimer PhD: For this first level heading, use title case, centered.
A test needs to have high reliability for two reasons. Primary reason is that high reliability enhances measurement to an extent, which a score reflects the random measurement error. Measurement errors are typically caused by any of the three factors, which include the first one being examinee-specific factors, including boredom and fatigue. The second one is test-specific factors such as ambiguity or faulty directions, and thirdly scoring particular factors, which include computational and carelessness errors. It is therefore; better to involve tests, which have good measures of reliability. To ensure that the test scores reflect more than just random error. The second reason for ensuring high reliability in a test is that it is a precursor to test validity. This shows that if the test scores are not assigned consistently, it becomes a challenge to conclude that the scores measure the domain of interest accurately. Hence, the extent which the inferences are accurate and justified is validity. Consequently, validity is the psychometric property about which one is most concerned. Nonetheless, a good assessment of the validity of a specific test can be a time-consuming (Dennis & Vander Wal, 2010). This particular reliability analysis is usually seen as the initial step in the validation process of a test. This implies that when a test is unreliable, one does not have to waste time finding out if it is valid because it would not be there. However, if there was enough reliability for the taste, a study of validation would then be essential.
It is important to ensure that a test is reliable since having an unreliable or invalid give inaccurate results and thus harm the study. It should determine if future observations or measurements confirm the measure or other forms of observations. It would be able to give the same score if the same thing is measured and if the instrument accurately reflects the true score, which is considered reliable, thus reduces the chances of error.
In terms of validity, a test that is considered valid is free from bias. It refers to how well it measures the construct it is meant to measure, and it determines the suitability or meaningfulness of the measurement. It determines if the instrument accurately describes the construct, it is intended to measure, and if the test has an accurate measurement. For instance, a test of mental ability, typically measures an individual’s mental ability. Hence, it gives meaning to a test also (Dennis & Vander Wal, 2010). Besides, validity shows the degree to which an individual can make particular conclusions or even predictions concerning people depending on the scores from the tests. Hence, it shows the importance of the test.
Ramifications for not having high levels of reliability and validity Comment by Carol Gegenheimer PhD: Same formatting note as above. Also move this to the next page.
Having low levels of validity and reliability creates problems in testing since it leads to bad results. For example, low levels of validity will not measure what is expected to measure. Moreover, the people taking a particular questionnaire view it as a valid measure of depression. Besides, the questions, including the range of responses, would not seem as appropriate for measuring a particular factor such as depression. Some testing instruments, such as questionnaires, are not well checked and proved by researchers, with the help of experts, hence do not measure what is intended. When there is a lack of construct validity, the questionnaires do not measure the abstract concept adequately. Also, there are cases when there is no criterion validity. Hence, there is no specified extend in which a measurement tool can be able to provide accurate findings. For instance, the questionnaire results may not relate to the actual clinical diagnoses among the participants who have been surveyed. There is also another psychological testing tool used for face validity. Hence, when the validity level is low, the test would not be said to have face validity, especially since it will not measure the level of happiness. Comment by Carol Gegenheimer PhD: Make sure to include supportive references.
About having low levels of reliability, it leads to unintended outcomes of measurement. For instance, the researchers will not get comparable results, especially if they repeat their questionnaire, even after the conditions have not changed. Therefore, if the questionnaire was administered to the same participants after the first one, the researchers are likely to expect similar levels of depression. In this case, the test-retest reliability is faulty (Shankman, Funkhouser, Klein, Davila, Lerner, & Hee, 2018). Also, similar questions may not provide similar answers; hence, there is no internal consistency. Besides, when there are low levels of reliability, different individuals assessing the same thing will not score the same way, hence lack faulty inter-rater ability. Hence, in this manner, if the researchers are not careful in ensuring the reliability and validity of the questionnaire, it causes a challenge in believing the overall results of the study. Comment by Carol Gegenheimer PhD: Your paper ends without a conclusion.
Comment by Carol Gegenheimer PhD: Omit extra spaces at the top of the page.
References
Dennis, J. P., & Vander Wal, J. S. (2010). The cognitive flexibility inventory: Instrument development and estimates of reliability and validity. Cognitive therapy and research, 34(3), 241-253.
Markon, K. E., Chmielewski, M., & Miller, C. J. (2011). The reliability and validity of discrete and continuous measures of psychopathology: Aa quantitative review. Psychological bulletin, 137(5), 856.
Shankman, S. A., Funkhouser, C. J., Klein, D. N., Davila, J., Lerner, D., & Hee, D. (2018). Reliability and validity of severity dimensions of psychopathology assessed using the Structured Clinical Interview for DSM‐5 (SCID). International journal of methods in psychiatric research, 27(1), e1590.
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