NR 439 Week 6 Data Results and Analysis

Week 6: Data Results and Analysis
Professor and Class,
It is important to understand how to interpret research results, I think this can be a
little tricky sometimes and it does take some time to learn. There are four rules to
understand the results of research studies: understanding the purpose of the research
study, identifying the variables, identifying how the variables are measured, measures of
central tendency and measures of variability for the study of variables (CCN, 2018). I
chose to discuss how the variables are measured. Levels of measurement help us
decide how to interpret the data from our variables. There are four levels of
measurement nominal, ordinal, interval, and ratio. The first level of measurement is
nominal level of measurement. In this level of measurement, the numbers in the
variable are used only to classify the data. The second level of measurement is the
ordinal level of measurement. This level of measurement depicts some ordered
relationship among the variables observed. Suppose a student scores the highest grade
of 100 in the class. In this case, he would be assigned the first rank. Then, another




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classmate scores the second highest grade of a 96; so she would be assigned the




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second rank and so on. The third level of measurement is the interval level of




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measurement. The interval level of measurement not only classifies and orders the
measurements, but it also specifies that the distances between each interval on the




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scale are equivalent along the scale from lowest to highest. For example, 1 dollar to 2
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dollars is the same interval as 88 dollars to 89 dollars. The fourth level of measurement
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is the ratio level of measurement. In this level of measurement you add a value of zero
as well. The researcher should note that among these levels of measurement, the
nominal level is simply used to classify data, whereas the levels of measurement
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described by the interval level and the ratio level are much more exact. Knowing the
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level of measurement helps you decide what statistical analysis is appropriate on the
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values that were assigned. If a measure is nominal, then you know that you would never
average the data values or do a t-test on the data (SRM, 2006). Levels of measurement
can also help determine the cause and effect relationship among variables and it is
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always important to remember that correlation does not equal causation, I think this
often gets confused.
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“Statistical significance tells us the findings are real; clinical significance tells us if
the results are important for practice (Houser, 2018).” Statistical significance has to do
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with the likelihood that a research result is true and not merely a coincidence. Clinical
significance is a subjective interpretation of a research result as practical or meaningful
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for the patient, therefore, likely to affect provider behavior. Let’s say you are studying a
new blood pressure drug to lower the risk of stroke and you design a randomized
controlled trial against the standard of care drug. You set your p-value at p < 0.001 to be
very sure that if the research findings are statistically significant that the probability of a
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false result due to chance is less than 1 in 1000. You declare that the new drug
significantly lowers diastolic blood pressure in hypertensive adults, but is this clinically
significant? Let’s say the difference between the groups was 6mmHg. So here is where
we would have to know the research around these variables to have a clear answer.
What if the difference was only 2mmHg? Again, with a large enough sample, that small
difference in DBP may be statistically significant, but would you switch a patient to this


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