Please Run SPSS on Independent sample T test analysis on the data provided in order to show if there is any statistically significant in value from both the data obtained in pre and post survey testing, 1. Staff member’s self-reported occurrence of daily oral care, and 2. Staff members self-reported comfort level. With the pre and post testing quiz of 3. Staff member’s knowledge of oral care. You have to include the tables in which the numbers were put in and provide a brief conclusion comparing both the pre and post data as seen by the example below between mean mile time between non-athletes and athletes. There should be three different run SPSS on Independent sample T test analysis and three interpretation of what the result signify
I have uploaded the PowerPoint presentation showing the three data that you must perform the SPSS on Independent sample T test analysis from, which is self-reported occurrence of daily oral care, Increase knowledge of daily oral care and self-reported comfort level of performing oral care based on a pre-and post-survey that staff members were asked.
Tables
Two sections (boxes) appear in the output: Group Statistics and Independent Samples Test. The first section, Group Statistics, provides basic information about the group comparisons, including the sample size (n), mean, standard deviation, and standard error for mile times by group. In this example, there are 166 athletes and 226 non-athletes. The mean mile time for athletes is 6 minutes 51 seconds, and the mean mile time for non-athletes is 9 minutes 6 seconds.
The second section, Independent Samples Test, displays the results most relevant to the Independent Samples t Test. There are two parts that provide different pieces of information: (A) Levene’s Test for Equality of Variances and (B) t-test for Equality of Means
Levene's Test for Equality of of Variances: This section has the test results for Levene's Test. From left to right:
• F is the test statistic of Levene's test
• Sig. is the p-value corresponding to this test statistic.
The p-value of Levene's test is printed as ".000" (but should be read as p < 0.001 -- i.e., p very small), so we we reject the null of Levene's test and conclude that the variance in mile time of athletes is significantly different than that of non-athletes. This tells us that we should look at the "Equal variances not assumed" row for the t test (and corresponding confidence interval) results. (If this test result had not been significant -- that is, if we had observed p > α -- then we would have used the "Equal variances assumed" output.)
B t-test for Equality of Means provides the results for the actual Independent Samples t Test. From left to right:
• t is the computed test statistic
• df is the degrees of freedom
• Sig (2-tailed) is the p-value corresponding to the given test statistic and degrees of freedom
• Mean Difference is the difference between the sample means; it also corresponds to the numerator of the test statistic
• Std. Error Difference is the standard error; it also corresponds to the denominator of the test statistic
Note that the mean difference is calculated by subtracting the mean of the second group from the mean of the first group. In this example, the mean mile time for athletes was subtracted from the mean mile time for non-athletes (9:06 minus 6:51 = 02:14). The sign of the mean difference corresponds to the sign of the t value. The positive t value in this example indicates that the mean mile time for the first group, non-athletes, is significantly greater than the mean for the second group, athletes.
The associated p value is printed as ".000"; double-clicking on the p-value will reveal the un-rounded number. SPSS rounds p-values to three decimal places, so any p-value too small to round up to .001 will print as .000. (In this particular example, the p-values are on the order of 10-40.)
C Confidence Interval of the Difference: This part of the t-test output complements the significance test results. Typically, if the CI for the mean difference contains 0, the results are not significant at the chosen significance level. In this example, the 95% CI is [01:57, 02:32], which does not contain zero; this agrees with the small p-value of the significance test.
Since p < .001 is less than our chosen significance level α = 0.05, we can reject the null hypothesis, and conclude that the that the mean mile time for athletes and non-athletes is significantly different.
Based on the results, we can state the following:
• There was a significant difference in mean mile time between non-athletes and athletes (t315.846 = 15.047, p < .001).
• The average mile time for athletes was 2 minutes and 14 seconds faster than the average mile time for non-athletes
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