EBP for QI – Data Analysis and Statistics Assignment Example

IBM SPSS

Descriptive Statistics

Descriptive Statistics
	N	Range	Minimum	Maximum	Mean		Std. Deviation	Variance
	Statistic	Statistic	Statistic	Statistic	Statistic	Std. Error	Statistic	Statistic
Age	5	8.00	23.00	31.00	26.0000	1.51658	3.39116	11.500
Weight	5	40.00	125.00	165.00	148.0000	6.44205	14.40486	207.500
Pretest_Score	5	18.00	65.00	83.00	73.6000	3.73631	8.35464	69.800
Posttest_Score	5	15.00	75.00	90.00	82.0000	2.54951	5.70088	32.500
Valid N (listwise)	5

Frequency Distribution

Statistics

Statistics
		Age	Weight	Gender	Pretest_Score	Posttest_Score
N	Valid	5	5	5	5	5
	Missing	0	0	0	0	0
Mean		26.0000	148.0000	1.4000	73.6000	82.0000
Std. Error of Mean		1.51658	6.44205	.24495	3.73631	2.54951
Median		24.0000	150.0000	1.0000	75.0000	80.0000
Mode		24.00	150.00	1.00	65.00	80.00
Std. Deviation		3.39116	14.40486	.54772	8.35464	5.70088
Variance		11.500	207.500	.300	69.800	32.500
Range		8.00	40.00	1.00	18.00	15.00
Minimum		23.00	125.00	1.00	65.00	75.00
Maximum		31.00	165.00	2.00	83.00	90.00

Frequency Table

Age
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	23.00	1	20.0	20.0	20.0
	24.00	2	40.0	40.0	60.0
	28.00	1	20.0	20.0	80.0
	31.00	1	20.0	20.0	100.0
	Total	5	100.0	100.0

Weight
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	125.00	1	20.0	20.0	20.0
	150.00	3	60.0	60.0	80.0
	165.00	1	20.0	20.0	100.0
	Total	5	100.0	100.0

Gender
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Male	3	60.0	60.0	60.0
	Female	2	40.0	40.0	100.0
	Total	5	100.0	100.0

Pretest_Score
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	65.00	2	40.0	40.0	40.0
	75.00	1	20.0	20.0	60.0
	80.00	1	20.0	20.0	80.0
	83.00	1	20.0	20.0	100.0
	Total	5	100.0	100.0

Posttest_Score
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	75.00	1	20.0	20.0	20.0
	80.00	2	40.0	40.0	60.0
	85.00	1	20.0	20.0	80.0
	90.00	1	20.0	20.0	100.0
	Total	5	100.0	100.0

Post-test Score Graph

SPSS Assignment Table

If we think about each situation on its own, we can use a paired t-test (also called a dependent t-test) to see if there’s a big difference between the scores before and after. This test works well when the same people are tested twice in different situations (like before and after a test). With a significance level of 0.05, the t-test will show us if the average difference is really different from zero.

Paired-Samples T-test

Paired Samples Statistics
		Mean	N	Std. Deviation	Std. Error Mean
Pair 1	Posttest_Score	82.0000	5	5.70088	2.54951
	Pretest_Score	73.6000	5	8.35464	3.73631

Paired Samples Correlations
		N	Correlation	Sig.
Pair 1	Posttest_Score & Pretest_Score	5	.887	.045

Paired Samples Test
		Paired Differences					t	df	Sig. (2-tailed)
		Mean	Std. Deviation	Std. Error Mean	95% Confidence Interval of the Difference
					Lower	Upper
Pair 1	Posttest_Score – Pretest_Score	8.40000	4.21900	1.88680	3.16141	13.63859	4.452	4	.011

Item	Response	Comment
1. What’s the mean?	The mean difference between the pretest scores and post-test scores is 8.4.	On average, the scores increased by 8.4 points between the pretest and post-test for all the cases. This means that overall, there was an improvement in scores from before to after the test.
2. What’s the t-test results	The t-test resulted in a t-value of 4.452 with 4 degrees of freedom.	The t-value of 4.452 shows there’s a big difference between the scores before and after the test. At a significance level of 0.05, this t-value tells us it’s unlikely the score difference happened just by luck.
3. What’s the standard deviation	The standard deviation of the differences between the pretest scores and post-test scores is approximately 4.219.	The standard deviation of 4.219 shows how much the scores before and after the test vary. A small standard deviation means the differences are pretty consistent or close to the average difference of 8.4.