In addition to construction questions about attitudes, it is important to have the questions drafted in the correct format (Nachmias, 2008).
The Quantitative methodologies will be the statistical tests designed for the overall model to incorporate the information provided through one, two or all of the Qualitative data analysis methodologies. The tests used to determine the relationship between these "qualitative" factors and increases in Infection rates, will be the Chi-Square, Student's T-Test, ANOVA (to test for variations among the data), the construction of a Linear Regression Model and the calculation of the Pearson Correlation Coefficient, otherwise known as "R-Squared" (Nachmias, 2008).
These tests will be utilized in conjunction with a predetermined level of significance, or alpha. Since these tests will all be measuring the means and relationships of one data set, the level of alpha to be used in these tests is set at .05. Therefore, the resultant calculations will be compared to this level of alpha to determine if there is any statistical significance between these factors and increases in infection rates. To ensure the validity of the linear models, the R-squared value will be calculated and included within the analysis. There are numerous classes of the R-squared value, however for our purposes; it will be the "coefficient of determination" that accompanies linear regression models. Consequently, the main role of R-squared in our analysis will be to provide an explanation for the variances in the data. For example, if the R-squared value of the PDI for a certain subset of the population equates to .88, this would translate into the PDI being responsible for 88% of the data results, with 12% being attributed to chance or randomness. The formula that will be incorporated into this research for R-squared is as follows:
and the traditional standard of "goodness of fit" will be applied to the R-squared values; wherein, a R-squared result of 1.0 indicates the data fits perfectly and conversely, a R-squared value of 0.0 indicates no correlation between the two variables and all the data points are the result of randomness and chance. Furthermore, the various Chi-Square analysis will be broken down across age categories in order to determine the impact of generational status on the various elements. The standard formula for Chi-Squared will be utilized; the formula is as follows:
Error margin
The allowable error margin as plus or minus 15 percentage points.
Pattern of responses
The expected pattern of responses is as follows (see plot). 'Response a' (40%), 'Response B' (60%). In particular, the percentage for 'Response a' is 40%.
Margin of error
One factor that determines the required sample size is the acceptable margin of error. If we are willing to accept a relatively wide margin of error we'll need a relatively small sample. By contrast, if we desire a relatively narrow margin of error we'll need a relatively large sample.
For an error margin of plus/minus 15.00 points we need 41 subjects. If we were to double the error margin (to 30. The sample size would be reduced to 11 subjects. By contrast, if we were to cut the error margin in half (to 7. The required sample size would increase to 164 subjects.
We assumed that the percentage in this category is 40% which led to a sample size of 41. If the true percentage is actually 30% the required sample size would be 36. If the true percentage is actually 50% the required sample size would be 43.
Alpha
In computing the sample size we assume that we want to be 95% certain that the observed value falls within the margin of error (rather than 90% certain, for example) and also that we are concerned with errors in either direction. Changing either of these assumptions would also affect the sample size required.
Results:
According to the data presented in the Figure, the R-squared value is .7987. As this graph demonstrates there is a distinct correlation between these two variables. The dotted lines represent the Confidence Interval for this analysis. This Confidence Interval represents the relationship between these two variables. The solid line in the figure represents the regression of the means of the data involved in the analysis. As the Confidence Interval is closer to the solid line this indicates that the relationship is based on a statistical relationship and not random chance. As this figure demonstrates, the relationship between washing hands by nursing staffs and the increase in Nosocomial Infection rates is based on statistical correlation, therefore it can be asserted that this one specific, cultural value can have an impact on attitudes and behaviors and negatively impact these behaviors to the end result of increasing infection rates.
Using the...
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