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Students will have different interests in what they are learning at school.

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In what they study at school the students will have different interests. Some will depend heavily on science and some will choose languages. In analysis of student preference for the subjects, the various preferences for male and female students are not shown. This paper thus discusses the relationship between gender and the practical use of science by the students and how the GPA performance is affected. Grades.sav was used for the analysis of the relationship that exists between different genders of students and their uptake of sciences in school. A sample of 72 students were surveyed in the study. Gender is used as an explanatory variable and GPA is used to measure performance among students for the different sex. Gender is the dichotomous ratio variable and the continuous ratio variable to be used is the GPA (Brown, 2005).

Section 2: Testing Assumption

The process is made possible through carrying out of a t-test which is interested in finding out the difference that exists between male and female students.

Figure 1

Histogram: Male Student GPA

Histogram: Female Student GPA

Table 1

Descriptive Statistics

Descriptives

Gender Gender

Statistic

Std. Error

GPA GPA Range

1.00 Male

Mean

3.4286

.27891

95% Confidence Interval for Mean

Lower Bound

2.8618

 

Upper Bound

3.9954

 

5% Trimmed Mean

3.3651

 

Median

3.0000

 

Variance

2.723

 

Std. Deviation

1.65006

 

Minimum

1.00

 

Maximum

7.00

 

Range

6.00

 

Interquartile Range

2.00

 

Skewness

.508

.398

Kurtosis

-.262

.778

2.00 Female

Mean

3.9730

.26420

95% Confidence Interval for Mean

Lower Bound

3.4372

 

Upper Bound

4.5088

 

5% Trimmed Mean

3.9700

 

Median

4.0000

 

Variance

2.583

 

Std. Deviation

1.60704

 

Minimum

1.00

 

Maximum

7.00

 

Range

6.00

 

Interquartile Range

2.00

 

Skewness

.301

.388

Kurtosis

-.545

.759

Table 2

The Shapiro-Wilk Normality

Tests of Normality

Gender Gender

Kolmogorov-Smirnova

Shapiro-Wilk

Statistic

df

Sig.

Statistic

df

Sig.

GPA GPA Range

1.00 Male

.165

35

.017

.931

35

.029

2.00 Female

.187

37

.002

.932

37

.026

Tables and histogram for both male and female shown above are as a result of the set assumptions. From the female histogram, it is evident that bio-modal GPA will have an effect on the normality of the distribution assumed through the analysis. Table does not affect the skewness as depicted by the results.

Section 3: Research Question, Hypothesis, and Alpha Level

Research Question: Does the GPA in sciences vary with the student’s sex?

Statement of Hypothesis

Null Hypothesis: There is no difference between the males’ and females’ performance in sciences as shown by GPA

Alternative Hypothesis: The GPA of sciences among the male and female students is not the same

The Alpha Level Test: The statements above = 0.05

Conclusion

From the results, it is evident that female and male gender hold significant differences in regard to their uptake of sciences and their performance as evidenced by the GPA score. It is evident that the female gender goes against the normality case.

Appendix

N/B: Data Used in the Analysis

GPA Range

Frequency-For Male

Frequency-For Female

0.50-0.99

4

2

1.00-1.49

7

3

1.50-1.99

8

12

2.0-2.49

9

8

2.50-2.99

2

4

3.0-3.49

3

5

3.50-3.99

2

3

Reference

Brown, A. (2005). A new software for carrying out one-way ANOVA post hoc tests. Computer Methods and Programs in Biomedicine, 79(1), pp.89-95.

August 09, 2021
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470

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