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.