University Biostatistical Research Using SPSS Solution

This assignment provides a comprehensive analysis of biostatistical research using SPSS. It begins with an examination of subcutaneous oxygen measurements under different protocols (bed rest vs. high activity) using a paired samples t-test, demonstrating the process of hypothesis testing and interpretation of results, including t-statistic and p-value. The second part of the assignment explores the relationship between working status and depression levels in women, outlining the use of an independent samples t-test. The assignment presents the data, null and alternative hypotheses, and the rationale for selecting this test. Finally, the assignment analyzes the relationship between employment status and CES-D scores in women using an independent samples t-test. It discusses the group statistics, Levene's test, and the violation of equal variances, which is addressed by using a t-statistic for separate variances. The analysis includes the interpretation of statistical significance and the conclusion that working women have different CES-D scores than non-working women. The assignment references multiple sources to support the analysis. The results of the statistical tests and the conclusions are clearly presented using tables and interpretations.

Running Head: BIOSTATISTICAL RESEARCH USING SPSS
BIOSTATISTICAL RESEARCH USING SPSS
Name of the Student:
Name of the University:
Author Note:

1BIOSTATISTICAL RESEARCH USING SPSS
Answer 1
Here the data shows the subcutaneous oxygen measurement after 12 hours of the start of
two protocols- bed rest and high activity. It was performed on same 10 patients for both the
protocols. The main interest is to find whether there is any dissimilarity in the measurements of
the two protocols. Therefore the null hypothesis will be-
There is no significant difference in the amount of oxygen due to different activity.
For testing the hypothesis, a paired sample t-test is performed. The necessary outputs are
described below:
Table 1.1:
Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean
Pair 1 Bed_rest 66.90 10 2.601 .823
High_activity 64.10 10 2.685 .849
Table 1.2:
Paired Samples Correlations
N Correlation Sig.
Pair 1 Bed_rest & High_activity 10 .733 .016
Table 1.3:
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
Bed_rest -
High_activity
2.800 1.932 .611 1.418 4.182 4.583 9 .001

2BIOSTATISTICAL RESEARCH USING SPSS
From table 1.1, it can be seen that on average, the amount of oxygen tension is 66.90 mm
Hg when a person is taking bed rest whereas that for full activity is 64.10 mm Hg. The
correlation between bed rest and working mode is 0.733 which indicates a strong positive
correlation between them (table 1.2).
The value of the t- statistic is 4.182 with p-value=0.001<0.05. Hence, the null hypothesis
is rejected at 5% level of significance. In other words, the t-statistic is significant and it can be
concluded that there is significant difference in amount of oxygen tension under the skin under
two different protocols.
Answer 2
Here it is required to test the relation between the working status and level of depression of
women. To study this case, a sample data should be collected for both employed and
unemployed women and their corresponding depression level.
a. The null hypothesis for this study can be taken as-
There is no significant difference in the levels of depression due to difference in working
status of women.
The alternative hypothesis will be-
The levels of depression differ noticeably for working and non- working women.
b. Here the analysis is related to the connection between two groups of women with respect
to their dejection level. Hence, there are two independent groups in the data– one for
working women and other for non-working women. Therefore, two –sample independent
t-test would be an appropriate tool for testing the hypothesis.

3BIOSTATISTICAL RESEARCH USING SPSS
Answer 3
a. There are 524 women who are unemployed and 436 women who are employed in the
sample (table 3.1).
b. The mean CES-D score for unemployed women is 20.90(approximately) and for
employed women is 15.82 (table 3.1).
c. The value of the F-statistic is 23.615 with p-value 0.000. Here the level of significance is
5%, thus, p-value<0.05. Therefore, the assumption is not correct and it can be deduced
that there is significant variation in the CES-D scores due to employment status of a
woman.
d. In independent sample t-test, it is assumed that the both groups have equal variances.
Here the value of Levene’s test statistic is significant at 5% level. Hence, the assumption
of equal variances is violated which may lead to type-I error. To rectify this violation, t-
statistic for separate variance is used. Therefore, the value of t-statistic for unequal
variances would be an appropriate measure in this study.
e. The value of t- test is 6.954 with p-value 0.000.
f. In this case, it was required to study the effect of employment status on the CES-D score
of women. For this study CES-D score were recorded for working and non- working
women. An independent sample t-test was performed assuming equal variances of the
two groups. The analysis showed that the test statistic is significant at 5% level, leading
to the rejection of the claim that there is no difference in CES-D score due to employment
status of a woman. Therefore, it can be deduced that working women have different CES-
D score than that for non-working women.
g. The outputs are shown below.

4BIOSTATISTICAL RESEARCH USING SPSS
Table 3.1
Group Statistics
Currently employed? N Mean Std. Deviation Std. Error Mean
CES-D Score No 524 20.8965 12.46425 .54450
Yes 436 15.8239 10.13655 .48545
Table 3.2
Independent Samples Test
Levene's Test
for Equality of
Variances t-test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence
Interval of the
Difference
Lower Upper
CES-D
Score
Equal
variances
assumed
23.615 .000 6.825 958 .000 5.07264 .74326 3.61404 6.53124
Equal
variances
not
assumed
6.954 957.514 .000 5.07264 .72949 3.64107 6.50421

5BIOSTATISTICAL RESEARCH USING SPSS
References
Altman, N. and Krzywinski, M., 2016. Points of significance: P values and the search for
significance.
Hinton, P.R., McMurray, I. and Brownlow, C., 2014. SPSS explained. Routledge.
Jayalath, K.P., Ng, H.K.T., Manage, A.B. and Riggs, K.E., 2017. Improved tests for
homogeneity of variances. Communications in Statistics-Simulation and Computation, 46(9),
pp.7423-7446.
Kim, Y.J. and Cribbie, R.A., 2018. ANOVA and the variance homogeneity assumption:
Exploring a better gatekeeper. British Journal of Mathematical and Statistical Psychology, 71(1),
pp.1-12.
Lawson, D., 2014. Hypothesis testing.
Lowry, R., 2014. Concepts and applications of inferential statistics.