WK 5 dis Data

This student completed a Rapid Response Team (RRT) analysis with a sample of 75 completed RRTs. The student provided a survey on completed Rapid Response Team (RRT) calls asking activating nurses to respond to seven questions about the RRT, indicating their level of satisfaction with the RRT call. Each of the seven questions was measured on a 5-point scale where 1 indicated strong dissatisfaction with the RRT process and 5 equaled a very strong satisfaction with the way the RRT was conducted. Thus, the highest possible score on the RRT evaluation survey was 35 (5 x 7), and the lowest possible score was 7 (i.e., 7 x 1). Higher scores indicate higher satisfaction with the RRT response, and lower scores indicate lower satisfaction with the RRT response.

This sample had 75 observations. This time, there were no entries in the “other” category, only cardiac, respiratory, and neurology.

Description of the Call 1= cardiac 2= respiratory 3= neurology 4= other

Case Processing Summary
	Description of the Call 1= cardiac 2= respiratory 3= neurology 4= other	Cases
		Valid	Missing	Total
		N	Percent	N	Percent	N	Percent
RRT Evaluation	cardiac	42	100.0%	0	0.0%	42	100.0%
	respiratory	12	100.0%	0	0.0%	12	100.0%
	neurology	21	100.0%	0	0.0%	21	100.0%

Descriptives
	Description of the Call 1= cardiac 2= respiratory 3= neurology 4= other	Statistic	Std. Error
RRT Evaluation	cardiac	Mean	30.0238	.99095
		95% Confidence Interval for Mean	Lower Bound	28.0225
			Upper Bound	32.0251
		5% Trimmed Mean	30.8466
		Median	31.0000
		Variance	41.243
		Std. Deviation	6.42210
		Minimum	5.00
		Maximum	35.00
		Range	30.00
		Interquartile Range	7.00
		Skewness	-2.152	.365
		Kurtosis	5.323	.717
	respiratory	Mean	31.0000	.95346
		95% Confidence Interval for Mean	Lower Bound	28.9014
			Upper Bound	33.0986
		5% Trimmed Mean	31.0000
		Median	31.0000
		Variance	10.909
		Std. Deviation	3.30289
		Minimum	27.00
		Maximum	35.00
		Range	8.00
		Interquartile Range	7.00
		Skewness	.000	.637
		Kurtosis	-1.872	1.232
	neurology	Mean	13.9524	2.81955
		95% Confidence Interval for Mean	Lower Bound	8.0709
			Upper Bound	19.8339
		5% Trimmed Mean	13.5582
		Median	7.0000
		Variance	166.948
		Std. Deviation	12.92082
		Minimum	.00
		Maximum	35.00
		Range	35.00
		Interquartile Range	21.50
		Skewness	.718	.501
		Kurtosis	-.999	.972

Notice the difference in the means by type of RRT. It seems as if there is a difference. But remember, this is just descriptive statistics. To see if there is statistical significance, we need an inferential test. This time, I can assure you that the data meets the assumptions of the one-way ANOVA test, which is what we are going to do next.

One-Way

ANOVA
RRT Evaluation
	Sum of Squares	df	Mean Square	F	Sig.
Between Groups	4020.391	2	2010.196	28.104	<.001
Within Groups	5149.929	72	71.527
Total	9170.320	74

Now, there is a lot of information in these results, most of which you do not see mentioned in a published study that used ANOVA. Sometimes, you see the F statistic (28.104). In Salkind and Frey (2019), you can learn how to actually do an ANOVA in Excel, which is for your information. In this course, we are most concerned about understanding the basic elements of the test and how to interpret it.

So, the F statistic is accompanied by the all-important p value in the table above, and as you can see, it indicates a statistically significant result. With a p value of <.001, we can be confident that the differences are not due to chance and more likely to some factor. But the ANOVA level of significance does NOT tell us WHERE those differences are. Is it between cardiac and respiratory, cardiac and neurology, or respiratory and neurology? Three groups, three possible comparisons.

A one-way ANOVA test is used when there is more than one comparison (that would be two groups and an independent sample t test). As we have three groups, we also have three comparisons. So, a post hoc test helps to fathom just where those comparisons are located.

Post Hoc Tests

There are a number of different types of post hoc comparisons. I chose the Scheffe test.

Multiple Comparisons
Dependent Variable: RRT Evaluation
Scheffe
(I) Description of the Call 1= cardiac 2= respiratory 3= neurology 4= other	(J) Description of the Call 1= cardiac 2= respiratory 3= neurology 4= other	Mean Difference (I-J)	Std. Error	Sig.	95% Confidence Interval
					Lower Bound	Upper Bound
cardiac	respiratory	-.97619	2.76832	.940	-7.8958	5.9434
	neurology	16.07143*	2.26032	<.001	10.4216	21.7212
respiratory	cardiac	.97619	2.76832	.940	-5.9434	7.8958
	neurology	17.04762*	3.06049	<.001	9.3977	24.6975
neurology	cardiac	-16.07143*	2.26032	<.001	-21.7212	-10.4216
	respiratory	-17.04762*	3.06049	<.001	-24.6975	-9.3977
*. The mean difference is significant at the 0.05 level.

As you can see, the Scheffe post hoc test computes the difference in the means and then calculates the significance level. (Behind the scenes in the statistical software, there are actually several independent sample t tests that make these two-group comparisons.)

Note the lack of statistical significance between cardiac and respiratory. That is likely because the difference was so small—less than a point. You would need a huge sample, far greater than 75 cases, to be able to see this statistically. But notice that the difference between cardiac and neurology is much bigger, and the p value is also statistically significant ( p <.001). Finally, notice that the difference between respiratory and neurology is also statistically significant (also with a p value of < .001).