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Validity In Quantitative Research- Assignment
Validity In Quantitative Research- Assignment
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.
· 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.
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.
The terms reliability and validity are used to assess the quality of research.
They describe the accuracy with which a method, approach, or test measures something.
Validity is concerned with a measure’s precision, while reliability is concerned with its consistency.
When designing your research, arranging your techniques, and writing up your findings, it’s critical to think about dependability and validity, especially in quantitative research.
Validity vs. Reliability
What does it have to say to you?
When the research is performed under the identical conditions, the amount to which the results can be replicated.
The degree to which the outcomes accurately reflect what they are designed to reflect.
What criteria are used to evaluate it?
By examining the consistency of outcomes across time, among various observers, and across different sections of the test.
By comparing the results to existing theories and other metrics of the same notion, you can see how well they match up.
What’s the connection between them?
A valid measurement is not always reliable: the results may be repeatable, but they are not always accurate.
A valid measurement is generally dependable: accurate results from a test should be repeatable.
Contents of the book
Understanding the difference between reliability and validity
Although they are closely connected, the terms reliability and validity have different meanings.
A measurement can be accurate but not legitimate.
A valid measurement, on the other hand, is typically also dependable.
What is the definition of dependability?
The consistency with which a method measures something is referred to as reliability.
The measurement is considered reliable if the same result can be regularly achieved using the same procedures under the same conditions.
You take temperature readings from a liquid sample multiple times under the same conditions.
Because the thermometer consistently displays the same temperature, the data can be trusted.
A symptom questionnaire is used by a clinician to diagnose a patient with a long-term medical problem.
With the same patient, several doctors use the same questionnaire but come up with different diagnoses.
As a measure of the condition, this suggests that the questionnaire is unreliable.
What does it mean to be valid?
The accuracy with which a method measures what it is supposed to measure is referred to as validity.
When research has a high level of validity, it delivers results that correspond to real-world traits, characteristics, and variances.
One evidence of a measurement’s validity is its high dependability.
If a method isn’t trustworthy, it isn’t likely to be valid.
If the thermometer displays different temperatures each time, despite carefully controlling conditions to guarantee that the sample’s temperature remains constant, the thermometer is most likely defective, and its results are invalid.
When a symptom questionnaire yields a consistent diagnosis when completed at different periods and by different doctors, it has good validity as a tool for assessing medical conditions.
However, reliability isn’t enough to assure validity on its own.
Even if a test is trustworthy, it may not precisely reflect reality.
The thermometer you used to evaluate the sample produced accurate readings.
However, because the thermometer was not correctly calibrated, the result is 2 degrees below the genuine temperature.
As a result, the measurement is invalid.
A group of people takes a test to assess their working memory.
The results are accurate, although the participants’ ratings are highly correlated with their reading comprehension.
This suggests that the procedure has limited validity: the test may be assessing participants’ reading comprehension rather than working memory.
Validity is more difficult to assess than reliability, but it is more crucial.
To get usable results, the data collection methods you utilize must be valid: the study must measure what it claims to measure.
This guarantees that the data you discuss and the conclusions you draw are both correct.
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