Working with SPSS and PASW Software

Working with SPSS and PASW Software Order Instructions: Working with SPSS (PASW) Software

For this assignment, you write a paper covering answers to assigned problems in Lessons 22 and 23 of your course text.

• Lesson 22: problems 1–4 (page 150)

• Lesson 23: problems 1–5 (page 155)
Your paper should be written using APA 6th edition guidelines. There is a sample paper on page 41 of the Publication Manual which you may use as a reference. Your paper must meet the following requirements:

• Include explanation and justification of all answers.

• Include an APA Results section for each problem set. (There is an example on page 167 of the course text)

• Include presentation, discussion and explanation of all figures and tables

• Include only the critical elements of your SPSS output

• Include a properly formatted H10 (null) and H1a (alternate) hypothesis which cover all possible situations related to a problem

I will email the questions for this paper

Working with SPSS and PASW Software Sample Answer

Some of the descriptive statistics on the Kudi algebra test are as summarized in Table 1.

Table 1: Statistics
Kudi
N	Valid	30
	Missing	0
Mean		54.63
Median		55.00
Mode		55
Skewness		-.255
Std. Error of Skewness		.427
Sum		1639

The hypothesis that need to be tested in this case is:

H₀: there is no significance difference in the mean algebra scores.

H₁: There in a significance difference between the mean algebra scores.

1. The total sum of the algebra score is 1639, with the average score of 54.63. The one-sample t-test analysis results summary is as follows:

 

One-Sample Test
	Test Value = 0
	t	Df	Sig. (2-tailed)	Mean Difference	95% Confidence Interval of the Difference
					Lower	Upper
kudi	28.975	29	.000	54.633	50.78	58.49

2. The test value is 4.18
3. The results indicate that the t-test value is 28.975, with the mean algebra score of 633. The algebra scores lie at the 95% confidence interval of 50.78 and 58.49. The p-value is < 0.001.
4. This result indicates that the null hypothesis will be rejected. This is because the significance values <0.001 are less than α = 0.05. Thus, the conclusion will be that there exists a significant difference between the mean algebra scores.

 

5. The one sample t-test on the dataset (hap_sad) is summarized in Table 3.

 

Table 3: One-Sample Test
	Test Value = 0
	t	df	Sig. (2-tailed)	Mean Difference	95% Confidence Interval of the Difference
					Lower	Upper
hap_sad	8.267	19	.000	8.750	6.53	10.97

Since the significance value (p-value) < 0.001 is less than α = 0.05, the null hypothesis will be rejected and a conclusion will be made that the classical music had a different impact on peoples (Stevens, 2012).

Figure 1: Histogram of classical music effect ratings.

Figure 2: The boxplot of classical music effect ratings.

Figure 2 illustrates that the data negatively skewed since it has a long tail towards the right (Lem, 2013).

• The total sum of the life stress at different ages is summarized in Table 4.

Table 4: Statistics
		Interpersonal life stress at age 40	Occupational life stress at age 40	Interpersonal life stress at age 60	Occupational life stress at age 60
N	Valid	45	45	45	45
	Missing	0	0	0	0
Sum		3519	3314	3375	2784

• Table 5 is a summary of the paired t-test;

 

 

 

Table 5: Paired Samples Statistics
		Mean	N	Std. Deviation	Std. Error Mean
Pair 1	Interpersonal life stress at age 40	78.20	45	11.655	1.737
	Interpersonal life stress at age 60	75.00	45	7.711	1.149
Pair 2	Occupational life stress at age 40	73.64	45	9.547	1.423
	Occupational life stress at age 60	61.87	45	6.625	.988

• The overall change in stress level at the age of 40, and at the age of 60 is illustrated in Figure 4 and 5.

Figure 4: Occupational life stress at age 40

Figure 5: Occupational life stress at age 60.

Figure 6: Interpersonal life stress at age 40.

Figure 7: Interpersonal life stress at age 60.

These graphs indicate that there is a general decline in both the occupational and interpersonal life stress with age.

Table 6: 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	Interpersonal life stress at age 40 – Interpersonal life stress at age 60	3.200	13.942	2.078	-.989	7.389	1.540	44	.131
Pair 2	Occupational life stress at age 40 – Occupational life stress at age 60	11.778	12.696	1.893	7.964	15.592	6.223	44	.000

• In testing whether overall life stress increases or decreases with age, the results indicate that interpersonal life stress at 40 and 60 has no significant difference since the p-value 0.131 is greater than α = 0.05 (Hampel, 2011). In addition, occupational life stress indicates that there is a significance difference at age 40 and 60 since the p-value < 0.001 is less than α = 0.05. This deduces that the hypothesis that Mike made were valid.

Table 7: Paired Samples Statistics
		Mean	N	Std. Deviation	Std. Error Mean
Pair 1	Husband’s infertility anxiety score	57.46	24	7.337	1.498
	Wife’s infertility anxiety score	62.54	24	12.441	2.540

 

The results are clear that the wife’s infertility, anxiety score has high means scores of 62.54 with a standard deviation of 12.441. The husband’s infertility, anxiety score has the least mean score of 57.46 and standard deviation of 7.337 (Samuels, 2012).

The t-statistics can be summarized in the table below.

 

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	Husband’s infertility anxiety score – Wife’s infertility anxiety score	-5.083	7.649	1.561	-8.313	-1.853	-3.256	23	.003

The p-value, in this case, is 0.03, and the t-test value is -3.256.

• The results indicate that there exists a significant difference between husband’s infertility, anxiety score and wife’s infertility, anxiety scores, since this the p-value = 0.03 is less than α = 0.05.
• To compare the variability and distribution of the data, a boxplot was plotted, and its output is as illustrated in Figure 8.

 

Figure 8: The boxplot of husband’s infertility, anxiety score and wife’s infertility, anxiety scores.

The boxplot shows that wife’s infertility, anxiety scores shows a high variability, this is because the wife’s infertility, anxiety scores has a great spread of data (has higher upper quartile and least lower quartile) (Leech, 2012).

Working with SPSS and PASW Software References

Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., & Stahel, W. A. (2011). Robust statistics: the approach based on influence functions (Vol. 114). John Wiley & Sons.

Leech, N. L., Barrett, K. C., & Morgan, G. A. (2012). IBM SPSS for intermediate statistics: Use and interpretation. Routledge.

Lem, S., Onghena, P., Verschaffel, L., & Van Dooren, W. (2013). The heuristic interpretation of box plots. Learning and Instruction, 26, 22-35.

Samuels, M. L., Witmer, J. A., & Schaffner, A. (2012). Statistics for the life sciences. Pearson Education.

Stevens, J. P. (2012). Applied multivariate statistics for the social sciences. Routledge.