Validity In Quantitative Research Designs

Validity In Quantitative Research Designs
Validity In Quantitative Research Designs
Validity in research refers to the extent researchers can be confident that the cause and effect they identify in their research are in fact causal relationships. If there is low validity in a study, it usually means that the research design is flawed and the results will be of little or no value. Four different aspects of validity should be considered when reviewing a research design: statistical conclusion validity, internal validity, construct validity, and external validity. In this Discussion, you consider the importance of each of these aspects in judging the validity of quantitative research.
To prepare:
· Read the method section of one of the following quasi-experimental studies. Identify at least one potential concern that could be raised about the study’s internal validity. (May use Google Search for studies below).
· Metheny, N. A., Davis-Jackson, J., & Stewart, B. J. (2010). Effectiveness of an aspiration risk-reduction protocol. Nursing Research, 59(1), 18–25.
· Padula, C. A., Hughes, C., & Baumhover, L. (2009). Impact of a nurse-driven mobility protocol on functional decline in hospitalized older adults. Journal of Nursing Care Quality, 24(4), 325–331.
· Yuan, S., Chou, M., Hwu, L., Chang, Y., Hsu, W., & Kuo, H. (2009). An intervention program to promote health-related physical fitness in nurses. Journal of Clinical Nursing, 18(10), 1,404–1,411.
· Consider strategies that could be used to strengthen the study’s internal validity and how this would impact the three other types of validity.
· Think about the consequences of an advanced practice nurse neglecting to consider the validity of a research study when reviewing the research for potential use in developing an evidence-based practice.
Question:
Post the title of the study that you selected and your analysis of the potential concerns that could be raised about the study’s internal validity. Propose recommendations to strengthen the internal validity and assess the effect your changes could have with regard to the other three types of validity. Discuss the dangers of failing to consider the validity of a research study.
Understanding reliability vs validity
Reliability and validity are closely related, but they mean different things. A measurement can be reliable without being valid. However, if a measurement is valid, it is usually also reliable.
What is reliability?
Reliability refers to how consistently a method measures something. If the same result can be consistently achieved by using the same methods under the same circumstances, the measurement is considered reliable.
You measure the temperature of a liquid sample several times under identical conditions. The thermometer displays the same temperature every time, so the results are reliable.
A doctor uses a symptom questionnaire to diagnose a patient with a long-term medical condition. Several different doctors use the same questionnaire with the same patient but give different diagnoses. This indicates that the questionnaire has low reliability as a measure of the condition.
What is validity?
Validity refers to how accurately a method measures what it is intended to measure. If research has high validity, that means it produces results that correspond to real properties, characteristics, and variations in the physical or social world.
High reliability is one indicator that a measurement is valid. If a method is not reliable, it probably isn’t valid.
If the thermometer shows different temperatures each time, even though you have carefully controlled conditions to ensure the sample’s temperature stays the same, the thermometer is probably malfunctioning, and therefore its measurements are not valid.
If a symptom questionnaire results in a reliable diagnosis when answered at different times and with different doctors, this indicates that it has high validity as a measurement of the medical condition.
However, reliability on its own is not enough to ensure validity. Even if a test is reliable, it may not accurately reflect the real situation.
The thermometer that you used to test the sample gives reliable results. However, the thermometer has not been calibrated properly, so the result is 2 degrees lower than the true value. Therefore, the measurement is not valid.
A group of participants take a test designed to measure working memory. The results are reliable, but participants’ scores correlate strongly with their level of reading comprehension. This indicates that the method might have low validity: the test may be measuring participants’ reading comprehension instead of their working memory.
Validity is harder to assess than reliability, but it is even more important. To obtain useful results, the methods you use to collect your data must be valid: the research must be measuring what it claims to measure. This ensures that your of the data and the you draw are also valid.
How are reliability and validity assessed?
Reliability can be estimated by comparing different versions of the same measurement. Validity is harder to assess, but it can be estimated by comparing the results to other relevant data or theory. Methods of estimating reliability and validity are usually split up into different types.

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