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:
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� 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
Some of the descriptive statistics on the Kudi algebra test are as summarized in Table 1.
Table 1:
Statistics
Kudi
N Valid 30
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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:
H 0 : there is no significance difference in the mean algebra scores.
H 1 : There in a significance difference between the mean algebra scores.
- 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
- The test value is 4.18
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- The results indicate that the t-test value is 28.975, with the mean algebra score of 54.633.
The algebra scores lie at the 95% confidence interval of 50.78 and 58.49. The p-value is
< 0.001. - 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. - 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).
6.
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Figure 1: Histogram of classical music effect ratings.
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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).
1) 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
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2) 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
3) The overall change in stress level at the age of 40, and at the age of 60 is illustrated in
Figure 4 and 5.
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Figure 4: Occupational life stress at age 40
Figure 5: Occupational life stress at age 60.
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Figure 6: Interpersonal life stress at age 40.
Figure 7: Interpersonal life stress at age 60.
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These graphs indicate that there is a general decline in both the occupational and interpersonal
life stress with age.
4)
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
5) 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.
6)
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
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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.
7) 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.
8) To compare the variability and distribution of the data, a boxplot was plotted, and its
output is as illustrated in Figure 8.
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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).
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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.
